diff --git a/.gitignore b/.gitignore
index cdd06fd..910c9b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -177,4 +177,7 @@ cython_debug/
 #.idea/
 
 #Ignore old thesis
-Aerostructural_model_LEI
\ No newline at end of file
+Aerostructural_model_LEI
+
+# Generated cache from the fancy-plot SurfplanAdapter conversion
+vsm_processed/
\ No newline at end of file
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..fe3990e
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,46 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Repository purpose
+
+Python implementation of the Vortex Step Method (VSM): an enhanced lifting-line aerodynamic solver coupled with 2D viscous airfoil polars, aimed at low-aspect-ratio wings and leading-edge inflatable (LEI) kites. Scientific software — physical correctness, numerical stability, and reproducibility outrank abstraction and stylistic churn. **Read `AGENTS.md` before changing solver/aerodynamic logic** (sign conventions, reference frames, and circulation definitions must never change silently).
+
+## Commands
+
+```bash
+source venv/bin/activate          # project venv at ./venv — required; system python lacks deps
+pip install -e .[dev]             # editable install (dev = pytest, pytest-cov, black)
+
+pytest                            # full suite (~35 s), config in pytest.ini (testpaths = tests)
+pytest tests/Solver/test_solver.py                 # one file
+pytest tests/Solver/test_solver.py::test_name      # one test
+black src tests                   # formatting (used by the project)
+```
+
+Examples run as plain scripts: `python examples/TUDELFT_V3_KITE/tutorial.py`. CI (`.github/workflows/testing.yml`) runs pytest with coverage on Python 3.10.
+
+## Architecture (big picture)
+
+Data flow: **geometry YAML → `BodyAerodynamics.instantiate()` → `Wing`/`Panel` mesh → `Solver.solve()` → results dict → plotting**.
+
+- `src/VSM/core/BodyAerodynamics.py` — central orchestrator. `BodyAerodynamics.instantiate(n_panels, file_path=<aero_geometry.yaml>, spanwise_panel_distribution=..., bridle_path=...)` reads the YAML's `wing_sections` (headers `[airfoil_id, LE_x..z, TE_x..z]`) and `wing_airfoils` tables, builds polars per airfoil, adds sections to a `Wing`, and refines to `n_panels`. YAML readers index columns **by header name**, so extra columns (e.g. `VUP_x..z` from newer SurfplanAdapter exports) are ignored harmlessly.
+- `src/VSM/core/AirfoilAerodynamics.py` — polar generation per airfoil type: `breukels_regression`, `masure_regression` (needs `ml_models_dir`; ~2.3 GB models downloaded from Zenodo into `data/ml_models/`, not in git), `neuralfoil`, `polars` (CSV), `inviscid`. Batch entry point: `from_yaml_entry_batch()`.
+- `src/VSM/core/WingGeometry.py` (`Wing`), `Panel.py`, `Filament.py`, `Wake.py` — mesh & vortex elements. **Panels retain only LE/TE points + interpolated polar data**; airfoil `info_dict` parameters (`t`, `chord`, …) are discarded during instantiation — anything needing them later must re-read the YAML.
+- `src/VSM/core/Solver.py` — iterative circulation (gamma) loop; optional Anderson acceleration and Li/Gaunaa spanwise artificial viscosity for post-stall stabilization. Returns a results dict (forces, moments, distributions).
+- Top-level `src/VSM/` modules build on core: `stability_derivatives.py`, `trim_angle.py`, plus plotting: `plotting.py` (polars/distributions), `plot_geometry_matplotlib.py` (`plot_geometry`), `plot_geometry_plotly.py` (`interactive_plot` — the primary interactive 3D wing view, incl. bridle rendering), `plot_styling.py`.
+- Performance-critical vector math is `numba`-jitted (`core/utils.py`).
+
+## Data layout
+
+`data/<KITE_NAME>/` holds per-kite inputs, e.g. `data/TUDELFT_V3_KITE/`:
+- `CAD_derived_geometry/` and `Surfplan_derived_geometry/` — `aero_geometry_*.yaml` variants (one per airfoil-model type) + `struc_geometry_*.yaml` (bridle/particle data, used via `bridle_path`).
+- `Surfplan_export/` — raw Surfplan export (`<name>.txt` + `profiles/*.dat`). Converted to VSM YAMLs by the sibling [SurfplanAdapter](https://github.com/awegroup/SurfplanAdapter) project, which also handles the Surfplan→VSM coordinate transform.
+- Different YAML sources use different global frames (CAD-derived vs Surfplan-derived origins differ by ~10 m in z) — never mix coordinates across YAML families.
+
+## Conventions that matter
+
+- Body-fixed reference frame: **x rearward (LE→TE), y right wing, z up**; α positive nose-up, β positive wind from port. Aircraft-frame conversion via `stability_derivatives.map_derivatives_to_aircraft_frame()`.
+- Wing sections are ordered/symmetric tip-to-tip; `airfoil_id` in `wing_sections` must match `wing_airfoils`.
+- Style (`docs/style_guide.md`): typed Google docstrings, `snake_case`, path variables end `_path`, directories end `_dir`.
+- Tests include verification cases against analytical solutions (`tests/verification_cases/`: elliptical, swept, curved wing, horseshoe vortices) — solver changes must keep these passing; add regression tests when numerical behaviour intentionally changes and explain what changed and why in the commit.
diff --git a/README.md b/README.md
index 1d42c90..2376db6 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,18 @@ The Vortex Step Method (VSM) is an enhanced lifting line method that improves up
 A Julia version of this project is available at [VortexStepMethod.jl](https://github.com/Albatross-Kite-Transport/VortexStepMethod.jl)
 
 
+## Key Features
+- **Accurate low-aspect-ratio wing modeling** with enhanced lifting line theory
+- **Viscous-inviscid coupling** using 2D airfoil polars (inviscid, CFD, ML-based)
+- **Complex geometry support**: sweep, dihedral, anhedral, leading-edge inflatable (LEI) kites
+- **Rigid-body stability derivatives**: automatic computation of dCx/dα, dCMy/dq, etc.
+- **Trim angle solver**: automatic determination of trimmed angle of attack
+- **Non-dimensional rate derivatives**: controls-friendly output (hat_p, hat_q, hat_r)
+- **Reference point flexibility**: correctly handles moment reference point for rotational velocities
+- **Interactive visualization**: Plotly and Matplotlib geometry and results plotting
+<!-- ![](docs/TUDELFT_V3_KITE_plotly.gif) -->
+![](docs/plotly_render.png)
+
 ## Reference Frame
 
 The VSM uses a body-fixed reference frame with the following conventions:
@@ -30,17 +42,6 @@ The reference frame is illustrated below for the open-source example kite, the [
 **Aircraft Frame Transformation:** For stability derivatives in standard aircraft coordinates (x-forward, y-right, z-down), use `map_derivatives_to_aircraft_frame()` from the `VSM.stability_derivatives` module. See `examples/TUDELFT_V3_KITE/evaluate_stability_derivatives.py` for usage.
 
 
-## Key Features
-
-- **Accurate low-aspect-ratio wing modeling** with enhanced lifting line theory
-- **Viscous-inviscid coupling** using 2D airfoil polars (inviscid, CFD, ML-based)
-- **Complex geometry support**: sweep, dihedral, anhedral, leading-edge inflatable (LEI) kites
-- **Rigid-body stability derivatives**: automatic computation of dCx/dα, dCMy/dq, etc.
-- **Trim angle solver**: automatic determination of trimmed angle of attack
-- **Non-dimensional rate derivatives**: controls-friendly output (hat_p, hat_q, hat_r)
-- **Reference point flexibility**: correctly handles moment reference point for rotational velocities
-- **Interactive visualization**: Plotly and Matplotlib geometry and results plotting
-
 ## Documentation
 For detailed documentation, please refer to the following resources.
 
diff --git a/changelog.md b/changelog.md
index 410043c..707ff01 100644
--- a/changelog.md
+++ b/changelog.md
@@ -4,6 +4,162 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
+## [2.3.0] - 03/11/2026
+
+**Default results change.** The lifting-line coupling now follows the consistent
+formulation of Gaunaa, Li & Pirrung (TORQUE 2026): circulation from the inner
+velocity, span-perpendicular sections, quarter-chord force directions,
+attached-trailed-vortex forces and a chord-regularised curved bound vortex.
+Forces, moments and coefficients computed with the defaults differ from 2.2.0,
+most on swept and curved wings (kites). Each new default can be switched back
+individually where an option exists (listed below). See
+`docs/aerodynamic_model` for the formulation and its validation.
+
+This release also contains everything shipped in 2.2.0. `v2.2.0` was tagged on
+the `improve_plotting` branch, which had not been merged into `develop`/`main`.
+
+### Changed (results)
+- Circulation target is `Gamma = 0.5 |V_inner| c Cl` with the inner
+  (span-perpendicular, own-2D-induction-removed) velocity, instead of
+  `0.5 Umag^2 / Umagw c Cl`. On the V3 kite the old map put the circulation
+  2-6 % low.
+- Panel frame and chord follow the Crossflow Principle: the chord direction is
+  perpendicular to the bound vortex, the normal is chord x span, and the panel
+  chord is the perpendicular one. Unswept panels are unchanged; on swept panels
+  the chord shrinks (V3 tip panels by 56 % and 19 %).
+- `is_aoa_corrected=True` by default: lift and drag directions come from the
+  flow at the quarter chord (TAT3), with magnitudes from the 3/4-chord angle.
+  `is_aoa_corrected=False` restores the 3/4-chord directions.
+- The quarter-chord flow includes the influence of a curved or swept bound
+  vortex through a chord-regularised correction (the actual minus a locally
+  unrolled vortex system). On-blade induced drag matches the Trefftz plane
+  within about 1 % on arc, swept and kite geometries, independently of the
+  panel count. It is computed once per shape and rotated under rigid motion.
+- Drag acts along the local inner flow, not the global span axis.
+- `is_with_attached_trailed_vortex_force=True` by default: Kutta-Joukowski force
+  on the chordwise attached trailed vortex segments (`F_attached_trailed_distribution`
+  in the results).
+- `relaxation_factor=None` by default: 0.8 x the fixed-point stability limit of
+  Li et al. (2026), evaluated per solve (`Solver.compute_relaxation_factor_limit`).
+  2.2.0 used a fixed 0.01; pass a number to keep a fixed value.
+- Each wake ring follows its own panel's apparent velocity. Results in uniform
+  translating inflow are unchanged to round-off; they change under body rates or
+  distributed inflow.
+- Bridle line segments are charged at their own inflow, not the wing's mean.
+- With `is_with_viscous_drag_correction=True`, the Gaunaa 2024 correction receives
+  the full 3D relative velocity (the spanwise flow it models).
+- With `is_with_artificial_viscosity=True`, the regularization is also gated on
+  negative stall.
+- `Wing.preserve_section_order` (default False): skip the section re-sorting;
+  `update_wing_from_points` sets it, since its arrays already define the arc order.
+
+### Removed / breaking
+- `Solver` options `is_smooth_circulation`, `smoothness_factor`,
+  `is_artificial_damping`, `artificial_damping`,
+  `is_with_simonet_artificial_viscosity`, `simonet_artificial_viscosity_fva`
+  (stored, never read) and the `gamma_loop_type` values `simonet_stall`,
+  `non_linear_simonet_stall`, `non_linear_simonet_stall_newton_raphson` (they
+  raised `ImportError`). An unknown `gamma_loop_type` now raises `ValueError`.
+- `Solver.compute_aerodynamic_quantities` no longer returns `Umagw`; the relative
+  velocity is available from `Solver.compute_relative_velocity(gamma)`.
+- The Anderson loop no longer falls back to the base loop by default
+  (`anderson_fallback_to_base=True` restores it).
+
+### Added
+- `gamma_loop_type="casadi_newton"`: Newton solve of the circulation residual
+  with an exact CasADi Jacobian, globalised by pseudo-transient continuation,
+  with fallback to the base loop (`newton_max_iterations`,
+  `newton_pseudo_time_step`, `newton_fallback_to_base`, `polar_interpolation`).
+  Requires the optional `casadi` package.
+- `stagnation_patience` / `stagnation_rtol`: optional early stop of stagnating
+  circulation loops.
+- `BodyAerodynamics(..., leg_direction="rib" | "streamwise" | "perpendicular")`
+  for the attached-trailed legs (default `"rib"`, unchanged).
+- `results["drag_induced_trefftz"]`: Trefftz-plane induced drag as a far-wake
+  reference (uniform inflow only).
+- `VSM.core.panel_kernels`: the section force law as functions over an `xp`
+  namespace (`numpy` or `casadi`): `relative_flow`, `quarter_chord_velocity`,
+  `direction_angle`, `panel_forces`, `attached_trailed_forces` and row helpers.
+  `compute_results` uses them itself.
+- `BodyAerodynamics.compute_quarter_chord_AIC_matrices`: the induction of the
+  force-direction flow at the quarter chords, for callers that freeze it.
+- `Solver.casadi_panel_force_function()`: per-panel forces as a CasADi function.
+  With `table_inputs=True` (also on the residual function) the polar tables are
+  inputs, so one graph serves every deformed shape;
+  `Solver.casadi_polar_table_inputs()` and `casadi_stall_angle_inputs()` return
+  them in the input layout.
+- Documentation: aerodynamic model document (formulation, conventions,
+  validation) and the curved bound-vortex validation report.
+
+### Fixed
+- Circulation loops stop on non-finite circulation instead of running on.
+- `Solver`: SyntaxError in the pseudo-transient reject condition (hotfix on `main`
+  after 2.2.0, now also here).
+- `Wing.compute_projected_area` no longer normalises the caller's
+  `z_plane_vector` in place.
+
+### Performance
+- AIC assembly JIT-compiled with numba, then made allocation-free (vectors as
+  tuples): about 1 us -> 20 ns per filament evaluation, 7.3 -> 0.1 ms for a
+  36-panel AIC.
+- Curved bound-vortex correction computed once per shape and rotated under
+  rigid motion.
+- Faster Li/Gaunaa artificial viscosity, vectorised force law, Trefftz drag and
+  projected area, cheaper `casadi_newton` residual calls. A 36-panel V3
+  `casadi_newton` solve takes about 17 ms (about 50 ms before these changes).
+
+## [2.2.0] - 02/09/2026
+
+Release cut from the `improve_plotting` branch. This is the version used to
+generate the rigid-body stability derivatives (Table 8) of Poland et al. (2026),
+Wind Energ. Sci., "Computational aerodynamics for soft-wing kite design".
+
+### Fixed
+- `BodyAerodynamics.va` setter: the rotational inflow due to body rates is now
+  `va -= omega x (r - r0)` (apparent wind = freestream minus body-point velocity).
+  v2.1.2 used `+=`, i.e. the body rotating at `-omega`. **This flips the sign of
+  every rate derivative** (`d/dp_hat`, `d/dq_hat`, `d/dr_hat`) computed with
+  `stability_derivatives.py`; angle derivatives and trim angles are unaffected.
+  Roll, pitch and yaw damping derivatives are now negative, as physics requires.
+- `stability_derivatives.py::_evaluate_with_angles` now passes `body_axis`, so a
+  non-zero baseline roll/pitch rate is no longer silently applied about the z-axis.
+- `trim_angle.py`: removed leftover debug `matplotlib` plot/`plt.show()` from the
+  coarse sweep (blocked headless pipelines).
+- `Filament`: vortex-core regularization uses the radial projection of the
+  evaluation point onto the filament.
+- `WingGeometry`: section meshing is orientation-agnostic.
+- Elliptical-wing y-offset bug in tests fixed; bridle line index handling fixed.
+
+### Changed
+- **Breaking:** `BodyAerodynamics.va_initialize` / `va` setter no longer accept
+  `roll_rate`, `pitch_rate`, `yaw_rate`. Use `body_rates` (scalar or list of
+  magnitudes, rad/s), `body_axis` (matching axes, default +z) and
+  `rates_in_body_frame`. `compute_trim_angle` and
+  `compute_rigid_body_stability_derivatives` keep their `roll_rate/pitch_rate/yaw_rate`
+  keyword arguments.
+- **Breaking:** bridle-line dictionaries use header key `d` (line diameter)
+  instead of `diameter`, matching the SurfPlanAdapter export.
+- `BodyAerodynamics.instantiate` accepts `aerodynamic_center_location` and
+  `control_point_location` (chord fractions, defaults 0.25 / 0.75); AC and CP
+  chord weights are decoupled.
+- `Solver`: opt-in Anderson-accelerated inner circulation loop; opt-in
+  Li/Gaunaa spanwise artificial viscosity for post-stall stabilization;
+  `relaxation_factor` default restored to 0.01.
+- `Solver.solve` results include `q_ref` (reference dynamic pressure); coefficients
+  are normalised with an area-weighted reference velocity for distributed inflow.
+- Quasi-steady-state solver API and examples added and subsequently removed.
+
+### Added
+- `BodyAerodynamics.rotate(angle_deg|angle_rad, axis, point)` to rotate the full
+  geometry; body rates follow the geometry unless `rates_in_body_frame=True`.
+- `BodyAerodynamics.compute_flat_area`.
+- `src/VSM/plotly/`: interactive Plotly geometry plots with inflatable tubes,
+  curved canopy, Cp-scaled force fields (`fancy_interactive_plot.py` example).
+- Examples: `compute_trim_angle.py`, `calculate_max_roll.py`,
+  `attitude_and_yaw_rate_optimize.py`, `cmz_sensitivity_default.py`,
+  `varying_spanwise_va.py`.
+- Tests for gamma initialization, tube geometry, updated filament/solver tests.
+
 ## [2.1.0] - 11/02/2026
 
 ### Added
diff --git a/data/TUDELFT_V3_KITE/CAD_derived_geometry/struc_geometry_manually_adjusted.yaml b/data/TUDELFT_V3_KITE/CAD_derived_geometry/struc_geometry_manually_adjusted.yaml
index fdc68f0..4987dc6 100644
--- a/data/TUDELFT_V3_KITE/CAD_derived_geometry/struc_geometry_manually_adjusted.yaml
+++ b/data/TUDELFT_V3_KITE/CAD_derived_geometry/struc_geometry_manually_adjusted.yaml
@@ -1,8 +1,7 @@
-
 ##############################
 ## General ###################
 ##############################
-bridle_point_node: [0,0,0] #[x,y,z] --> location of kcu
+bridle_point_node: [0, 0, 0] #[x,y,z] --> location of kcu
 
 ## Mass
 pulley_mass: !!float 0.1 #[kg] #TODO: would be better to define these per pulley
@@ -15,7 +14,6 @@ dyneema:
   youngs_modulus: 550000000 #[Pa] -> k = E A / l0
   damping_per_stiffness: 0.0 # [/s] -> c = damping_per_stiffness * k
 
-
 ###########################
 ## Wing ###################
 ###########################
@@ -117,38 +115,38 @@ wing_elements:
   # line name, measured rest length "l0" [m], stiffness "k" [N/m], damping "c" [N.s/m], mass "m" [kg], linktype
   headers: [name, l0, k, c, m, linktype]
   data:
-  # leading-edge tube (LE[i] -- LE[i+1])
-  - [le_1, 0.984642, 2e3, 0, 1, "default"]
-  - [le_2, 1.327663, 2e3, 0, 1, "default"]
-  - [le_3, 1.326240, 2e3, 0, 1, "default"]
-  - [le_4, 1.323655, 2e3, 0, 1, "default"]
-  - [le_5, 1.347595, 2e3, 0, 1, "default"]
-
-  # struts (chords)
-  - [strut_1, 1.074728, 2e3, 0, 1, "default"]
-  - [strut_2, 1.511911, 2e3, 0, 1, "default"]
-  - [strut_3, 1.960560, 2e3, 0, 1, "default"]
-  - [strut_4, 2.154185, 2e3, 0, 1, "default"]
-  - [strut_5, 2.265501, 2e3, 0, 1, "default"]
-
-  # trailing-edge wire (TE[i] -- TE[i+1])
-  - [te_1, 0.903854, 1e3, 0, 0.2, "default"]
-  - [te_2, 1.351057, 1e3, 0, 0.2, "default"]
-  - [te_3, 1.350587, 1e3, 0, 0.2, "default"]
-  - [te_4, 1.350462, 1e3, 0, 0.2, "default"]
-  - [te_5, 1.321072, 1e3, 0, 0.2, "default"]
-
-  # diagonal springs (both directions per panel)
-  - [dia_1a, 1.360356, 1e3, 0, 0.1, "default"]
-  - [dia_1b, 1.780032, 1e3, 0, 0.1, "default"]
-  - [dia_2a, 2.108078, 1e3, 0, 0.1, "default"]
-  - [dia_2b, 2.251513, 1e3, 0, 0.1, "default"]
-  - [dia_3a, 2.449188, 1e3, 0, 0.1, "default"]
-  - [dia_3b, 2.455523, 1e3, 0, 0.1, "default"]
-  - [dia_4a, 2.601005, 1e3, 0, 0.1, "default"]
-  - [dia_4b, 2.563136, 1e3, 0, 0.1, "default"]
-  - [dia_5a, 2.629214, 1e3, 0, 0.1, "default"]
-  - [dia_5b, 2.629214, 1e3, 0, 0.1, "default"]
+    # leading-edge tube (LE[i] -- LE[i+1])
+    - [le_1, 0.984642, 2e3, 0, 1, 'default']
+    - [le_2, 1.327663, 2e3, 0, 1, 'default']
+    - [le_3, 1.326240, 2e3, 0, 1, 'default']
+    - [le_4, 1.323655, 2e3, 0, 1, 'default']
+    - [le_5, 1.347595, 2e3, 0, 1, 'default']
+
+    # struts (chords)
+    - [strut_1, 1.074728, 2e3, 0, 1, 'default']
+    - [strut_2, 1.511911, 2e3, 0, 1, 'default']
+    - [strut_3, 1.960560, 2e3, 0, 1, 'default']
+    - [strut_4, 2.154185, 2e3, 0, 1, 'default']
+    - [strut_5, 2.265501, 2e3, 0, 1, 'default']
+
+    # trailing-edge wire (TE[i] -- TE[i+1])
+    - [te_1, 0.903854, 1e3, 0, 0.2, 'default']
+    - [te_2, 1.351057, 1e3, 0, 0.2, 'default']
+    - [te_3, 1.350587, 1e3, 0, 0.2, 'default']
+    - [te_4, 1.350462, 1e3, 0, 0.2, 'default']
+    - [te_5, 1.321072, 1e3, 0, 0.2, 'default']
+
+    # diagonal springs (both directions per panel)
+    - [dia_1a, 1.360356, 1e3, 0, 0.1, 'default']
+    - [dia_1b, 1.780032, 1e3, 0, 0.1, 'default']
+    - [dia_2a, 2.108078, 1e3, 0, 0.1, 'default']
+    - [dia_2b, 2.251513, 1e3, 0, 0.1, 'default']
+    - [dia_3a, 2.449188, 1e3, 0, 0.1, 'default']
+    - [dia_3b, 2.455523, 1e3, 0, 0.1, 'default']
+    - [dia_4a, 2.601005, 1e3, 0, 0.1, 'default']
+    - [dia_4b, 2.563136, 1e3, 0, 0.1, 'default']
+    - [dia_5a, 2.629214, 1e3, 0, 0.1, 'default']
+    - [dia_5b, 2.629214, 1e3, 0, 0.1, 'default']
 
 #############################
 ## Bridle ###################
@@ -177,71 +175,71 @@ bridle_particles:
 
 bridle_connections:
   # name, ci, cj, ck (this is the third node, only exist when a pulley)
-  headers: [name, ci, cj, ck] 
+  headers: [name, ci, cj, ck]
   data:
     # front lines
-    - [a6,25,1,33] # pulley
-    - [A5,3,21]
-    - [A3,5,21]
-    - [A2,7,22]
-    - [A1,9,22]
-    - [A1,11,23]
-    - [A2,13,23]
-    - [A3,15,24]
-    - [A5,17,24]
-    - [a6,26,19,35] # pulley
-    - [AII,21,25]
-    - [AI,22,25]
-    - [AI,23,26]
-    - [AII,24,26]
-    - [amain,25,0]
-    - [amain,26,0]
+    - [a6, 25, 1, 33] # pulley
+    - [A5, 3, 21]
+    - [A3, 5, 21]
+    - [A2, 7, 22]
+    - [A1, 9, 22]
+    - [A1, 11, 23]
+    - [A2, 13, 23]
+    - [A3, 15, 24]
+    - [A5, 17, 24]
+    - [a6, 26, 19, 35] # pulley
+    - [AII, 21, 25]
+    - [AI, 22, 25]
+    - [AI, 23, 26]
+    - [AII, 24, 26]
+    - [amain, 25, 0]
+    - [amain, 26, 0]
     # rear lines
-    - [br6,33,2,36] # pulley
-    - [br5,4,27]
-    - [br3,6,27]
-    - [br2,8,28]
-    - [br1,10,28]
-    - [br1,12,29]
-    - [br2,14,29]
-    - [br3,16,30]
-    - [br5,18,30]
-    - [br6,35,20,37] # pulley
-    - [BRII,27,31]
-    - [BRI,28,31]
-    - [BRI,29,32]
-    - [BRII,30,32]
-    - [BR-main-1,31,33]
-    - [BR-main-1,32,35]
-    - [M-line,36,33,34] # pulley
-    - [M-line,34,35,37] # pulley
-    - [Steering Tape,36,0]
-    - [Power Tape,34,0]
-    - [Steering Tape,37,0]
+    - [br6, 33, 2, 36] # pulley
+    - [br5, 4, 27]
+    - [br3, 6, 27]
+    - [br2, 8, 28]
+    - [br1, 10, 28]
+    - [br1, 12, 29]
+    - [br2, 14, 29]
+    - [br3, 16, 30]
+    - [br5, 18, 30]
+    - [br6, 35, 20, 37] # pulley
+    - [BRII, 27, 31]
+    - [BRI, 28, 31]
+    - [BRI, 29, 32]
+    - [BRII, 30, 32]
+    - [BR-main-1, 31, 33]
+    - [BR-main-1, 32, 35]
+    - [M-line, 36, 33, 34] # pulley
+    - [M-line, 34, 35, 37] # pulley
+    - [Steering Tape, 36, 0]
+    - [Power Tape, 34, 0]
+    - [Steering Tape, 37, 0]
 
 bridle_lines:
   # line name, measured rest length "l0" [m], stiffness "k" [N/m], damping "c" [N.s/m], diameter "d" [m], density "rho" [kg/m^3]
   # dyneema has rho = 970
-  headers: [name, l0, diameter, material, linktype]
+  headers: [name, l0, d, material, linktype]
   data:
-  # front lines
-  - [a6, 11.372, 0.005, dyneema, "pulley"]
-  - [A5, 2.782, 0.005, dyneema, "noncompressive"]
-  - [A3, 3.253, 0.005, dyneema, "noncompressive"]
-  - [A2, 3.352, 0.005, dyneema, "noncompressive"]
-  - [A1, 3.520, 0.005, dyneema, "noncompressive"]
-  - [AII, 3.188, 0.01, dyneema, "noncompressive"]
-  - [AI, 3.420, 0.01, dyneema, "noncompressive"]
-  - [amain, 4.297, 0.02, dyneema, "noncompressive"]
-  # rear lines
-  - [br1, 4.448, 0.002, dyneema, "noncompressive"]
-  - [br2, 4.300, 0.002, dyneema, "noncompressive"]
-  - [br3, 4.560, 0.002, dyneema, "noncompressive"]
-  - [br5, 4.047, 0.002, dyneema, "noncompressive"]
-  - [br6, 14.172, 0.002, dyneema, "pulley"]
-  - [BRI, 2.388, 0.002, dyneema, "noncompressive"]
-  - [BRII, 1.890, 0.002, dyneema, "noncompressive"]
-  - [BR-main-1, 0.874, 0.002, dyneema, "noncompressive"]
-  - [M-line, 2.196, 0.002, dyneema, "pulley"]
-  - [Steering Tape, 1.506, 0.002, dyneema, "noncompressive"]
-  - [Power Tape, 3.129, 0.002, dyneema, "noncompressive"]
\ No newline at end of file
+    # front lines
+    - [a6, 11.372, 0.005, dyneema, 'pulley']
+    - [A5, 2.782, 0.005, dyneema, 'noncompressive']
+    - [A3, 3.253, 0.005, dyneema, 'noncompressive']
+    - [A2, 3.352, 0.005, dyneema, 'noncompressive']
+    - [A1, 3.520, 0.005, dyneema, 'noncompressive']
+    - [AII, 3.188, 0.01, dyneema, 'noncompressive']
+    - [AI, 3.420, 0.01, dyneema, 'noncompressive']
+    - [amain, 4.297, 0.02, dyneema, 'noncompressive']
+    # rear lines
+    - [br1, 4.448, 0.002, dyneema, 'noncompressive']
+    - [br2, 4.300, 0.002, dyneema, 'noncompressive']
+    - [br3, 4.560, 0.002, dyneema, 'noncompressive']
+    - [br5, 4.047, 0.002, dyneema, 'noncompressive']
+    - [br6, 14.172, 0.002, dyneema, 'pulley']
+    - [BRI, 2.388, 0.002, dyneema, 'noncompressive']
+    - [BRII, 1.890, 0.002, dyneema, 'noncompressive']
+    - [BR-main-1, 0.874, 0.002, dyneema, 'noncompressive']
+    - [M-line, 2.196, 0.002, dyneema, 'pulley']
+    - [Steering Tape, 1.506, 0.002, dyneema, 'noncompressive']
+    - [Power Tape, 3.129, 0.002, dyneema, 'noncompressive']
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/TUDELFT_V3_KITE.txt b/data/TUDELFT_V3_KITE/Surfplan_export/TUDELFT_V3_KITE.txt
new file mode 100644
index 0000000..717e097
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/TUDELFT_V3_KITE.txt
@@ -0,0 +1,214 @@
+Filename: TUDELFT_V3.sle
+Design name: 14 hydrav1 07
+Revision: 1.0
+Designer: 
+Export Date: jeu. 13/02/2025 16:57:57
+
+3d rib positions
+      LE X;          Y;          Z;       TE X;          Y;          Z;      VUP X;          Y;          Z
+24
+  4,156287;   0,916618;   0,520832;   4,156287;   0,916618;  -0,864107;   1,352509;   0,297952;   0,000000
+  3,967978;   1,791356;   0,709238;   3,955506;   1,787553;  -1,011852;   1,646267;   0,501952;  -0,013040
+  3,805350;   2,209568;   0,790880;   3,789238;   2,199439;  -1,189372;   1,676461;   1,053982;  -0,019032
+  3,501223;   2,540205;   0,864219;   3,485499;   2,522089;  -1,256822;   1,390325;   1,601815;  -0,023989
+  3,134335;   2,796493;   0,929298;   3,116753;   2,773988;  -1,310901;   1,379198;   1,765306;  -0,028558
+  2,798300;   3,092998;   0,986158;   2,780285;   3,065722;  -1,354755;   1,290075;   1,953351;  -0,032688
+  2,393198;   3,288677;   1,034831;   2,380855;   3,254511;  -1,390237;   0,824015;   2,280780;  -0,036327
+  1,959729;   3,398648;   1,075348;   1,946587;   3,361473;  -1,418530;   0,831221;   2,351276;  -0,039430
+  1,553762;   3,587398;   1,107733;   1,540958;   3,547442;  -1,440421;   0,777640;   2,426595;  -0,041958
+  1,111239;   3,671702;   1,132004;   1,107796;   3,627957;  -1,456442;   0,203071;   2,580468;  -0,043881
+  0,664252;   3,658834;   1,148177;   0,660721;   3,613797;  -1,466944;   0,204434;   2,607118;  -0,045175
+  0,225013;   3,741837;   1,156262;   0,220752;   3,696209;  -1,472144;   0,244401;   2,617018;  -0,045826
+ -0,225013;   3,741837;   1,156262;  -0,220752;   3,696209;  -1,472144;  -0,244401;   2,617018;  -0,045826
+ -0,664252;   3,658834;   1,148177;  -0,660721;   3,613797;  -1,466944;  -0,204434;   2,607118;  -0,045175
+ -1,111239;   3,671702;   1,132004;  -1,107796;   3,627957;  -1,456442;  -0,203071;   2,580468;  -0,043881
+ -1,553762;   3,587398;   1,107733;  -1,540958;   3,547442;  -1,440421;  -0,777640;   2,426595;  -0,041958
+ -1,959729;   3,398648;   1,075348;  -1,946587;   3,361473;  -1,418530;  -0,831221;   2,351276;  -0,039430
+ -2,393198;   3,288677;   1,034831;  -2,380855;   3,254511;  -1,390237;  -0,824015;   2,280780;  -0,036327
+ -2,798300;   3,092998;   0,986158;  -2,780285;   3,065722;  -1,354755;  -1,290075;   1,953351;  -0,032688
+ -3,134335;   2,796493;   0,929298;  -3,116753;   2,773988;  -1,310901;  -1,379198;   1,765306;  -0,028558
+ -3,501223;   2,540205;   0,864219;  -3,485499;   2,522089;  -1,256822;  -1,390325;   1,601815;  -0,023989
+ -3,805350;   2,209568;   0,790880;  -3,789238;   2,199439;  -1,189372;  -1,676461;   1,053982;  -0,019032
+ -3,967978;   1,791356;   0,709238;  -3,955506;   1,787553;  -1,011852;  -1,646267;   0,501952;  -0,013040
+ -4,156287;   0,916618;   0,520832;  -4,156287;   0,916618;  -0,864107;  -1,352509;   0,297952;   0,000000
+
+3d curved wingtip positions
+      LE X;          Y;          Z;       TE X;          Y;          Z;      VUP X;          Y;          Z
+7
+  4,100957;   0,921215;  -0,522180;   4,152601;   0,932607;  -0,866820;   1,357902;   0,301698;  -0,000239
+  4,105533;   0,971623;  -0,263184;   4,141856;   0,979216;  -0,874727;   1,373623;   0,312615;  -0,000937
+  4,099444;   1,049881;  -0,028112;   4,124523;   1,054402;  -0,887481;   1,398983;   0,330226;  -0,002063
+  4,085799;   1,154511;   0,183243;   4,101072;   1,156125;  -0,904737;   1,433293;   0,354052;  -0,003586
+  4,065552;   1,283430;   0,368472;   4,071974;   1,282342;  -0,926149;   1,475864;   0,383616;  -0,005476
+  4,039112;   1,434330;   0,522929;   4,037700;   1,431014;  -0,951369;   1,526010;   0,418440;  -0,007702
+  4,006563;   1,604842;   0,639766;   3,998721;   1,600098;  -0,980052;   1,583040;   0,458044;  -0,010233
+
+LE tube
+  Centre X;          Y;          Z;   Diam(mm)
+38
+  4,047384;   0,902987;  -0,806316;   0,112452
+  4,053468;   0,918198;  -0,576557;   0,113170
+  4,052015;   0,967544;  -0,320048;   0,115263
+  4,042557;   1,045392;  -0,087800;   0,118639
+  4,025810;   1,149644;   0,119933;   0,123207
+  4,002306;   1,277897;   0,300671;   0,128875
+  3,972396;   1,427448;   0,449839;   0,135551
+  3,936225;   1,595488;   0,560835;   0,143143
+  3,967429;   1,791188;   0,633460;   0,151561
+  3,804690;   2,209154;   0,709853;   0,162061
+  3,500590;   2,539476;   0,778809;   0,170831
+  3,133636;   2,795597;   0,840178;   0,178254
+  2,797590;   3,091923;   0,893902;   0,184529
+  2,392716;   3,287341;   0,939956;   0,189772
+  1,959218;   3,397202;   0,978330;   0,194060
+  1,553266;   3,585851;   1,009025;   0,197442
+  1,111106;   3,670013;   1,032043;   0,199952
+  0,664116;   3,657099;   1,047385;   0,201615
+  0,224849;   3,740081;   1,055068;   0,202418
+ -0,224849;   3,740081;   1,055068;   0,202418
+ -0,664116;   3,657099;   1,047385;   0,201615
+ -1,111106;   3,670013;   1,032043;   0,199952
+ -1,553266;   3,585851;   1,009025;   0,197442
+ -1,959218;   3,397202;   0,978330;   0,194060
+ -2,392716;   3,287341;   0,939956;   0,189772
+ -2,797590;   3,091923;   0,893902;   0,184529
+ -3,133636;   2,795597;   0,840178;   0,178254
+ -3,500590;   2,539476;   0,778809;   0,170831
+ -3,804690;   2,209154;   0,709853;   0,162061
+ -3,967429;   1,791188;   0,633460;   0,151561
+ -3,936225;   1,595488;   0,560835;   0,143143
+ -3,972396;   1,427448;   0,449839;   0,135551
+ -4,002306;   1,277897;   0,300671;   0,128875
+ -4,025810;   1,149644;   0,119933;   0,123207
+ -4,042557;   1,045392;  -0,087800;   0,118639
+ -4,052015;   0,967544;  -0,320048;   0,115263
+ -4,053468;   0,918198;  -0,576557;   0,113170
+ -4,047384;   0,902987;  -0,806316;   0,112452
+
+Strut 2
+  Centre X;          Y;          Z;    Diam(m)
+7
+  3,992381;   1,798912;   0,562351;   0,105697
+  4,013188;   1,805140;   0,397453;   0,099891
+  4,000806;   1,801365;   0,121209;   0,097265
+  3,980386;   1,795139;  -0,157776;   0,092566
+  3,959762;   1,788850;  -0,439564;   0,087908
+  3,938932;   1,782499;  -0,724157;   0,083203
+  3,919906;   1,776698;  -0,984103;   0,078907
+
+Strut 5
+  Centre X;          Y;          Z;    Diam(m)
+7
+  3,165429;   2,835935;   0,767330;   0,110490
+  3,198103;   2,878112;   0,526561;   0,128371
+  3,183835;   2,859850;   0,166347;   0,124894
+  3,159895;   2,829208;  -0,197326;   0,117965
+  3,135714;   2,798257;  -0,564655;   0,111190
+  3,111292;   2,766998;  -0,935639;   0,104346
+  3,088872;   2,738302;  -1,276210;   0,098064
+
+Strut 8
+  Centre X;          Y;          Z;    Diam(m)
+8
+  1,979699;   3,454888;   0,903062;   0,131981
+  2,006196;   3,530089;   0,681343;   0,141738
+  2,001981;   3,518165;   0,340947;   0,141175
+  1,987651;   3,477629;  -0,004804;   0,133769
+  1,973148;   3,436605;  -0,353542;   0,126804
+  1,958521;   3,395229;  -0,705269;   0,119783
+  1,943769;   3,353502;  -1,059985;   0,112703
+  1,930428;   3,315762;  -1,380803;   0,106299
+
+Strut 11
+  Centre X;          Y;          Z;    Diam(m)
+8
+  0,669201;   3,721894;   0,970331;   0,140834
+  0,676930;   3,820506;   0,734465;   0,142401
+  0,675849;   3,806722;   0,377913;   0,142268
+  0,672073;   3,758564;   0,015391;   0,134521
+  0,668249;   3,709798;  -0,350263;   0,127292
+  0,664392;   3,660613;  -0,719051;   0,120005
+  0,660502;   3,611011;  -1,090973;   0,112657
+  0,656965;   3,565894;  -1,429263;   0,105972
+
+Strut 14
+  Centre X;          Y;          Z;    Diam(m)
+8
+ -0,669201;   3,721894;   0,970331;   0,140834
+ -0,676930;   3,820506;   0,734465;   0,142401
+ -0,675849;   3,806722;   0,377913;   0,142268
+ -0,672073;   3,758564;   0,015391;   0,134521
+ -0,668249;   3,709798;  -0,350263;   0,127292
+ -0,664392;   3,660613;  -0,719051;   0,120005
+ -0,660502;   3,611011;  -1,090973;   0,112657
+ -0,656965;   3,565894;  -1,429263;   0,105972
+
+Strut 17
+  Centre X;          Y;          Z;    Diam(m)
+8
+ -1,979699;   3,454888;   0,903062;   0,131981
+ -2,006196;   3,530089;   0,681343;   0,141738
+ -2,001981;   3,518165;   0,340947;   0,141175
+ -1,987651;   3,477629;  -0,004804;   0,133769
+ -1,973148;   3,436605;  -0,353542;   0,126804
+ -1,958521;   3,395229;  -0,705269;   0,119783
+ -1,943769;   3,353502;  -1,059985;   0,112703
+ -1,930428;   3,315762;  -1,380803;   0,106299
+
+Strut 20
+  Centre X;          Y;          Z;    Diam(m)
+7
+ -3,165429;   2,835935;   0,767330;   0,110490
+ -3,198103;   2,878112;   0,526561;   0,128371
+ -3,183835;   2,859850;   0,166347;   0,124894
+ -3,159895;   2,829208;  -0,197326;   0,117965
+ -3,135714;   2,798257;  -0,564655;   0,111190
+ -3,111292;   2,766998;  -0,935639;   0,104346
+ -3,088872;   2,738302;  -1,276210;   0,098064
+
+Strut 23
+  Centre X;          Y;          Z;    Diam(m)
+7
+ -3,992381;   1,798912;   0,562351;   0,105697
+ -4,013188;   1,805140;   0,397453;   0,099891
+ -4,000806;   1,801365;   0,121209;   0,097265
+ -3,980386;   1,795139;  -0,157776;   0,092566
+ -3,959762;   1,788850;  -0,439564;   0,087908
+ -3,938932;   1,782499;  -0,724157;   0,083203
+ -3,919906;   1,776698;  -0,984103;   0,078907
+
+3d Bridle
+      TopX;          Y;          Z;    BottomX;          Y;          Z; Name      ;     Length;   Material;   Diameter
+  0,658922;   3,590858;   1,123196;   0,682069;   3,407109;   1,014197; a1        ;   0,214896;           ;   2,000000
+  1,937954;   3,337051;   1,051436;   1,898143;   3,164487;   0,942461; a2        ;   0,207939;           ;   2,000000
+  3,097783;   2,749708;   0,907650;   3,052266;   2,575994;   0,791988; a3        ;   0,213602;           ;   2,000000
+  3,921859;   1,777294;   0,692391;   3,825134;   1,648422;   0,616812; a4        ;   0,177977;           ;   2,000000
+  0,682069;   3,407109;   1,014197;   0,887905;   1,721653;   0,769331; A1        ;   1,715543;           ;   2,000000
+  1,898143;   3,164487;   0,942461;   1,532504;   1,590653;   0,731702; A2        ;   1,629437;           ;   2,000000
+  3,052266;   2,575994;   0,791988;   2,644934;   1,015550;   0,607355; A3        ;   1,623267;           ;   2,000000
+  3,825134;   1,648422;   0,616812;   2,172485;  -0,743695;   0,618402; A4        ;   2,907486;           ;   2,000000
+  0,887905;   1,721653;   0,769331;   1,113164;  -0,195037;   0,711827; AI        ;   1,930738;           ;   3,000000
+  1,532504;   1,590653;   0,731702;   1,113164;  -0,195037;   0,711827; AII       ;   1,834374;           ;   3,000000
+  2,644934;   1,015550;   0,607355;   2,172485;  -0,743695;   0,618402; AIII      ;   1,821612;           ;   3,000000
+  1,113164;  -0,195037;   0,711827;   0,941442;  -3,901961;   0,633144; amain     ;   3,711733;           ;   4,000000
+  2,172485;  -0,743695;   0,618402;   0,941442;  -3,901961;   0,633144; amain2    ;   3,389739;           ;   4,000000
+  0,941442;  -3,901961;   0,633144;   0,910001;  -4,099458;   0,630632; a5.1      ;   0,200000;           ;   5,000000
+  0,659453;   3,597625;   0,966125;   0,682069;   3,407109;   1,014197; b1        ;   0,197785;           ;   2,000000
+  1,939381;   3,341088;   0,901698;   1,898143;   3,164487;   0,942461; b2        ;   0,185876;           ;   2,000000
+  3,097418;   2,749241;   0,773223;   3,052266;   2,575994;   0,791988; b3        ;   0,180014;           ;   2,000000
+  3,908574;   1,773243;   0,589225;   3,825134;   1,648422;   0,616812; b4        ;   0,152655;           ;   2,000000
+  0,670112;   3,733556;   0,362112;   0,887905;   1,721653;   0,769331; e1        ;   2,064223;           ;   2,000000
+  1,977801;   3,449767;   0,326140;   1,532504;   1,590653;   0,731702; e2        ;   1,954245;           ;   2,000000
+  3,148563;   2,814703;   0,256835;   2,644934;   1,015550;   0,607355; e3        ;   1,900910;           ;   2,000000
+  0,654966;   3,540403;  -1,204082;   1,155974;  -0,902939;  -0,473306; br1       ;   4,530820;           ;   2,000000
+  1,920720;   3,288303;  -1,167853;   1,155974;  -0,902939;  -0,473306; br2       ;   4,316681;           ;   2,000000
+  3,069960;   2,714095;  -1,085876;   1,921865;  -1,309919;  -0,396111; br3       ;   4,241059;           ;   2,000000
+  3,891515;   1,768042;  -0,839226;   1,921865;  -1,309919;  -0,396111; br4       ;   3,680993;           ;   2,000000
+  4,142726;   0,875716;  -0,615380;   3,821673;   0,392361;  -0,630484; br5       ;   0,580461;           ;   2,000000
+  4,137746;   0,864315;  -0,791897;   3,821673;   0,392361;  -0,630484; br6       ;   0,590505;           ;   2,000000
+  1,155974;  -0,902939;  -0,473306;   1,087879;  -3,228214;  -0,089481; BR1       ;   2,357724;           ;   2,000000
+  1,921865;  -1,309919;  -0,396111;   1,087879;  -3,228214;  -0,089481; BR2       ;   2,114098;           ;   2,000000
+  3,821673;   0,392361;  -0,630484;   0,954417;  -3,906964;   0,021095; BR3       ;   5,208639;           ;   2,000000
+  1,087879;  -3,228214;  -0,089481;   0,954417;  -3,906964;   0,021095; BRI       ;   0,700529;           ;   3,000000
+  0,954417;  -3,906964;   0,021095;   0,910001;  -4,099458;   0,052295; brmain    ;   0,200000;           ;   4,000000
+
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/V3D_AVL.avl b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/V3D_AVL.avl
new file mode 100644
index 0000000..4519c16
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/V3D_AVL.avl
@@ -0,0 +1,116 @@
+Empty Project
+0.0                          | Mach
+0     0     0.0              | iYsym  iZsym  Zsym
+
+  25.000      2.630     11.180  | Sref   Cref   Bref
+ 0.00   0.00   0.00          | Xref   Yref   Zref
+ 0.00                        | CDp  (optional)
+
+
+
+#=================================================
+SURFACE                      | (keyword)
+14 hydrav1 07
+#Nchord    Cspace   [ Nspan Sspace ]
+ 10        1.0
+
+INDEX                        | (keyword)
+111                          | Lsurf
+
+YDUPLICATE
+0.0
+
+SCALE
+1.0  1.0  1.0
+
+TRANSLATE
+0.0  0.0  0.0
+
+ANGLE
+0.0                          | dAinc
+
+#__________________________________
+SECTION                                          |  (keyword)
+   0.0000    0.2250    0.0000    2.6288  0.9989    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_1.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.0081    0.6643   -0.0830    2.6155  0.9897    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_2.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.0243    1.1112   -0.0701    2.5888  0.9712    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_3.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.0485    1.5538   -0.1544    2.5485  0.9433    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_4.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.0809    1.9597   -0.3432    2.4942  0.9058    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_5.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.1214    2.3932   -0.4532    2.4253  0.8582    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_6.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.1701    2.7983   -0.6488    2.3411  0.8000    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_7.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.2270    3.1343   -0.9453    2.2404  0.7304    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_8.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.2920    3.5012   -1.2016    2.1212  0.6480    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_9.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.3654    3.8053   -1.5323    1.9803  0.5506    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_10.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.4470    3.9680   -1.9505    1.7211  0.4341    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_11.dat
+
+#______________
+SECTION                                          |  (keyword)
+   0.6354    4.1563   -2.8252    1.3849  0.0000    3    0   | Xle Yle Zle   Chord Ainc   [ Nspan Sspace ]
+
+AFIL 0.0 1.0
+G:\Shared drives\Hardware\01 - Kite and Bridle (KB)\Archived kites\V3.25\V3.25D\VSM Package\prof_12.dat
+
+#______________
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.dat b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.dat
new file mode 100644
index 0000000..0c7db24
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.dat
@@ -0,0 +1,151 @@
+rib_1
+ 1.0000000  0.0000000
+ 0.9577769  0.0053610
+ 0.9081751  0.0116589
+ 0.8585733  0.0179567
+ 0.8089715  0.0242546
+ 0.7887696  0.0268196
+ 0.7593697  0.0305525
+ 0.7097680  0.0368503
+ 0.6601662  0.0431482
+ 0.6105644  0.0494461
+ 0.5983523  0.0509966
+ 0.5609626  0.0557439
+ 0.5113608  0.0620418
+ 0.4617590  0.0683397
+ 0.4301315  0.0723554
+ 0.4121573  0.0746375
+ 0.3625555  0.0809354
+ 0.3129537  0.0872333
+ 0.2944570  0.0895817
+ 0.2864751  0.0899438
+ 0.2631344  0.0910025
+ 0.2431549  0.0919087
+ 0.2231755  0.0928149
+ 0.2031960  0.0937211
+ 0.1832166  0.0946273
+ 0.1750000  0.0950000
+ 0.1696407  0.0944874
+ 0.1632797  0.0938790
+ 0.1433727  0.0919749
+ 0.1234657  0.0900708
+ 0.1222984  0.0899592
+ 0.1039075  0.0859758
+ 0.0941392  0.0838600
+ 0.0842841  0.0817254
+ 0.0818568  0.0807479
+ 0.0751055  0.0780291
+ 0.0658445  0.0742997
+ 0.0565836  0.0705702
+ 0.0529896  0.0691229
+ 0.0478730  0.0658051
+ 0.0395115  0.0603832
+ 0.0311500  0.0549613
+ 0.0292787  0.0537479
+ 0.0290956  0.0535627
+ 0.0273482  0.0517953
+ 0.0256109  0.0500381
+ 0.0256009  0.0500279
+ 0.0238535  0.0482605
+ 0.0221062  0.0464932
+ 0.0203588  0.0447258
+ 0.0186115  0.0429584
+ 0.0168641  0.0411910
+ 0.0151168  0.0394236
+ 0.0133694  0.0376563
+ 0.0132474  0.0375328
+ 0.0121593  0.0355013
+ 0.0109895  0.0333173
+ 0.0098198  0.0311333
+ 0.0086500  0.0289492
+ 0.0074877  0.0267791
+ 0.0066048  0.0244503
+ 0.0057275  0.0221362
+ 0.0048502  0.0198222
+ 0.0039729  0.0175081
+ 0.0030956  0.0151940
+ 0.0022184  0.0128799
+ 0.0021212  0.0123489
+ 0.0013119  0.0099645
+ 0.0008206  0.0074949
+ 0.0003294  0.0050253
+ 0.0001647  0.0025126
+ 0.0000000  0.0000000
+ 0.0001647 -0.0025126
+ 0.0003294 -0.0050253
+ 0.0008206 -0.0074949
+ 0.0013119 -0.0099645
+ 0.0021212 -0.0123489
+ 0.0029306 -0.0147333
+ 0.0040443 -0.0169917
+ 0.0051580 -0.0192500
+ 0.0065570 -0.0213437
+ 0.0079559 -0.0234373
+ 0.0096161 -0.0253305
+ 0.0112764 -0.0272236
+ 0.0131695 -0.0288839
+ 0.0150627 -0.0305441
+ 0.0171563 -0.0319430
+ 0.0192500 -0.0333420
+ 0.0215083 -0.0344557
+ 0.0237667 -0.0355694
+ 0.0261511 -0.0363788
+ 0.0285355 -0.0371881
+ 0.0310051 -0.0376794
+ 0.0334747 -0.0381706
+ 0.0359874 -0.0383353
+ 0.0385000 -0.0385000
+ 0.0410126 -0.0383353
+ 0.0435253 -0.0381706
+ 0.0459949 -0.0376794
+ 0.0484645 -0.0371881
+ 0.0508489 -0.0363788
+ 0.0532333 -0.0355694
+ 0.0554917 -0.0344557
+ 0.0577500 -0.0333420
+ 0.0598437 -0.0319430
+ 0.0619373 -0.0305441
+ 0.0638305 -0.0288839
+ 0.0657236 -0.0272236
+ 0.0673839 -0.0253305
+ 0.0690441 -0.0234373
+ 0.0704430 -0.0213437
+ 0.0718420 -0.0192500
+ 0.0729557 -0.0169917
+ 0.0740694 -0.0147333
+ 0.0751055 -0.0127256
+ 0.0818568 -0.0003134
+ 0.0842841  0.0036228
+ 0.0941392  0.0180415
+ 0.1039075  0.0299185
+ 0.1222984  0.0475825
+ 0.1234657  0.0485200
+ 0.1433727  0.0624018
+ 0.1632797  0.0725964
+ 0.1696407  0.0752301
+ 0.1750000  0.0772601
+ 0.1832166  0.0800012
+ 0.2031960  0.0851738
+ 0.2231755  0.0884966
+ 0.2431549  0.0902580
+ 0.2631344  0.0907111
+ 0.2864751  0.0889438
+ 0.2944570  0.0885817
+ 0.3129537  0.0862333
+ 0.3625555  0.0799354
+ 0.4121573  0.0736375
+ 0.4301315  0.0713554
+ 0.4617590  0.0673397
+ 0.5113608  0.0610418
+ 0.5609626  0.0547439
+ 0.5983523  0.0499966
+ 0.6105644  0.0484461
+ 0.6601662  0.0421482
+ 0.7097680  0.0358503
+ 0.7593697  0.0295525
+ 0.7887696  0.0258196
+ 0.8089715  0.0232546
+ 0.8585733  0.0169567
+ 0.9081751  0.0106589
+ 0.9577769  0.0043610
+ 1.0000000 -0.0010000
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.png b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.png
new file mode 100644
index 0000000..6694b81
Binary files /dev/null and b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_1.png differ
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.dat b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.dat
new file mode 100644
index 0000000..5bec58d
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.dat
@@ -0,0 +1,150 @@
+rib_10
+ 1.0000000  0.0000000
+ 0.9577769  0.0036927
+ 0.9081751  0.0080307
+ 0.8585733  0.0123687
+ 0.8089715  0.0167067
+ 0.7887696  0.0184735
+ 0.7593697  0.0210447
+ 0.7097680  0.0253827
+ 0.6601662  0.0297207
+ 0.6105644  0.0340587
+ 0.5983523  0.0351267
+ 0.5609626  0.0383967
+ 0.5113608  0.0427347
+ 0.4617590  0.0470727
+ 0.4301315  0.0498387
+ 0.4121573  0.0514107
+ 0.3625555  0.0557487
+ 0.3129537  0.0600867
+ 0.2944570  0.0617043
+ 0.2864751  0.0620433
+ 0.2631344  0.0629229
+ 0.2431549  0.0635471
+ 0.2231755  0.0641713
+ 0.2031960  0.0647955
+ 0.1832166  0.0654197
+ 0.1750000  0.0656764
+ 0.1696407  0.0654557
+ 0.1632797  0.0651937
+ 0.1433727  0.0642449
+ 0.1234657  0.0630378
+ 0.1222984  0.0629670
+ 0.1039075  0.0607509
+ 0.0941392  0.0594096
+ 0.0842841  0.0580564
+ 0.0818568  0.0575028
+ 0.0751055  0.0559631
+ 0.0658445  0.0536768
+ 0.0565836  0.0513126
+ 0.0529896  0.0503951
+ 0.0478730  0.0484757
+ 0.0395115  0.0451268
+ 0.0311500  0.0416897
+ 0.0292787  0.0409204
+ 0.0290956  0.0408128
+ 0.0273482  0.0397851
+ 0.0256109  0.0387634
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+ 0.0238535  0.0376639
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+ 0.0203588  0.0354231
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+ 0.0168641  0.0331009
+ 0.0151168  0.0317578
+ 0.0133694  0.0305365
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+ 0.0013942  0.0105902
+ 0.0008721  0.0079655
+ 0.0003501  0.0053408
+ 0.0001750  0.0026704
+ 0.0000000  0.0000000
+ 0.0001750 -0.0026704
+ 0.0003501 -0.0053408
+ 0.0008721 -0.0079655
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+ 0.0022544 -0.0131243
+ 0.0031147 -0.0156584
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+ 0.0054819 -0.0204587
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+ 0.0182336 -0.0339487
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+ 0.0565758 -0.0378027
+ 0.0589759 -0.0366191
+ 0.0613761 -0.0354355
+ 0.0636012 -0.0339487
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+ 0.0733793 -0.0249089
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+ 0.6105644  0.0330587
+ 0.6601662  0.0287207
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+ 0.7593697  0.0200447
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+ 0.8089715  0.0157067
+ 0.8585733  0.0113687
+ 0.9081751  0.0070307
+ 0.9577769  0.0026927
+ 1.0000000 -0.0010000
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.png b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.png
new file mode 100644
index 0000000..0591d8d
Binary files /dev/null and b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_10.png differ
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_11.dat b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_11.dat
new file mode 100644
index 0000000..fa35a81
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_11.dat
@@ -0,0 +1,147 @@
+rib_11
+ 1.0000000  0.0000000
+ 0.9577769  0.0032589
+ 0.9081751  0.0070873
+ 0.8585733  0.0109156
+ 0.8089715  0.0147440
+ 0.7887696  0.0163032
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+ 0.7097680  0.0224008
+ 0.6601662  0.0262292
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+ 0.5983523  0.0310001
+ 0.5609626  0.0338859
+ 0.5113608  0.0377143
+ 0.4617590  0.0415427
+ 0.4301315  0.0439838
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+ 0.3625555  0.0491994
+ 0.3129537  0.0530278
+ 0.2944570  0.0544554
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+ 0.2631344  0.0556214
+ 0.2431549  0.0561723
+ 0.2231755  0.0567232
+ 0.2031960  0.0572741
+ 0.1832166  0.0578249
+ 0.1750000  0.0580515
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+ 0.1632797  0.0577855
+ 0.1433727  0.0572106
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diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_7.png b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_7.png
new file mode 100644
index 0000000..adeb036
Binary files /dev/null and b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_7.png differ
diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_8.dat b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_8.dat
new file mode 100644
index 0000000..f2d3b06
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_8.dat
@@ -0,0 +1,150 @@
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diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_8.png b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_8.png
new file mode 100644
index 0000000..9fb8db6
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diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_9.dat b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_9.dat
new file mode 100644
index 0000000..146d5b9
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_9.dat
@@ -0,0 +1,150 @@
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diff --git a/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_9.png b/data/TUDELFT_V3_KITE/Surfplan_export/profiles/prof_9.png
new file mode 100644
index 0000000..90ebec8
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diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/README.md b/data/TUDELFT_V3_KITE/cpx_distributions/README.md
new file mode 100644
index 0000000..d328489
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/README.md
@@ -0,0 +1,34 @@
+# TU Delft V3 kite — 2D Cp(x/c) distributions
+
+Chordwise pressure-coefficient (`cp_AOA_*.dat`) and skin-friction (`cf_AOA_*.dat`)
+distributions for the **TU Delft V3 LEI kite midspan airfoil section** (rigid 2D),
+from RANS CFD (Thijs/Kasper).
+
+File format: whitespace columns `# x y Cp` (or `... Cf`), i.e. column 0 = x/c,
+column 1 = y/c, column 2 = Cp (or Cf).
+
+## Contents
+
+| Angle of attack | Cp file | Cf file |
+|---|---|---|
+| 2° | `cp_AOA_2.dat` | `cf_AOA_2.dat` |
+| 6° | `cp_AOA_6.dat` | `cf_AOA_6.dat` |
+| 8° | `cp_AOA_8.dat` | `cf_AOA_8.dat` |
+
+These are the only angles for which the genuine V3 midspan section Cp distribution
+is available locally.
+
+## Provenance
+
+Copied from `WES_aero_sim_for_kite_design/data/`:
+- AoA 2° and 8°: `cp_cf_kasper/`
+- AoA 6°: `THIJS_CFD_alpha_6_V3_turbulent/postProcessing/surfaces/624/`
+
+The V3 section is identified by its airfoil geometry embedded in the files:
+~13.4% thickness, max camber y ≈ 0.0953 (chord normalised to 1).
+
+## Not included
+
+`Pointwise-Openfoam-toolchain/.../cp_AOA_10.dat` was **excluded**: it is a generic
+demo LEI profile from the Pointwise→OpenFOAM toolchain (~12.4% thickness,
+`tube_size=0.08, c_x=0.15, c_y=0.08`), **not** the V3 section.
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_2.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_2.dat
new file mode 100644
index 0000000..2d874b9
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_2.dat
@@ -0,0 +1,78 @@
+# x y Cf
+0.04276388755929555 0.06356556861711357 -5.3685492635529e-05
+0.03309840970977389 0.04825624850833229 -3.582704828999522e-08
+0.06326602268098154 0.03073779662338421 6.167786129151704e-05
+0.07384327410599092 0.01793640188203612 -0.0003192609764164146
+0.07660775307689469 0.01104437306879618 -0.000228764133061211
+0.07819996384396966 4.127780662861551e-05 -2.991057168005374e-06
+0.07202763627549755 -0.02108505542368243 -0.0004650481933519364
+0.06030740026776515 -0.03284898406007216 -0.000575092613061699
+0.05197415418742954 -0.03691972333053082 -0.0009533577132157866
+0.04841011551193194 -0.03797539582415126 -0.0012756171634708763
+0.04106259222822374 -0.03905070100038476 -0.002640959490398662
+0.0392041678823387 -0.03909985971367359 -0.002617383818930308
+0.03734550757143083 -0.03906061142774211 -0.0019512487320989223
+0.03549081466558211 -0.03893305307143092 -0.0004661649781977114
+0.02999235773064849 -0.03802447036198644 0.005671899478682422
+0.02819522910180473 -0.03754856560901278 0.007255265503267772
+0.02642274895181671 -0.03698778874565389 0.008437998523398466
+0.02467892578294952 -0.03634339841365394 0.009272998170146079
+0.02296771065963648 -0.03561683055468438 0.009818887434565653
+0.0212929661235993 -0.034809744871696 0.010121505031131855
+0.01965847119772771 -0.03392397792845034 0.010215736624071617
+0.01652493718577972 -0.03192453294918682 0.00988329670009842
+0.01503298224681096 -0.03081537691891334 0.009500925488249457
+0.01221552866592811 -0.02839075860041346 0.008406802228970986
+0.009641067793166569 -0.0257095339280482 0.00700174990074151
+0.0073328241508335 -0.02279597455813496 0.005439446132599986
+0.0004212849451338721 -0.0057241591407998 -9.821423814390917e-05
+0.0001929063854338941 -0.00387916574954884 -0.0001677770129790615
+3.715759961701779e-07 -0.000167057565479412 8.866677575636765e-06
+0.0003737770984357588 0.005393438845063324 0.0009648191807615032
+0.00106093352703043 0.009046469258067052 0.001906286770070402
+0.002091866108723892 0.01261774277269358 0.002862841031091262
+0.002733467311930074 0.01436259362075018 0.003226329655069884
+0.003457264975454637 0.01607498180921493 0.003373864267305056
+0.005118832334056953 0.01943119745211363 0.003244479859277398
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+0.1049056581150844 0.0884277941269339 0.0003608467874276816
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+0.04571639419151958 0.06586873418008751 -7.147357452011414e-05
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_6.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_6.dat
new file mode 100755
index 0000000..a415d17
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_6.dat
@@ -0,0 +1,103 @@
+# x y Cf
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+0.04571639419151958 0.06586873418008751 -0.000230212940186696
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_8.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_8.dat
new file mode 100644
index 0000000..8b35b73
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cf_AOA_8.dat
@@ -0,0 +1,104 @@
+# x y Cf
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+0.0332272197474035 0.0464048575148447 5.489757479981662e-06
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+0.04571639419151958 0.06586873418008751 3.101420874588672e-06
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_2.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_2.dat
new file mode 100644
index 0000000..547e619
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_2.dat
@@ -0,0 +1,45 @@
+# x y Cp
+0.04276388755929555 0.06356556861711357 -0.04348704157614704
+0.07295141860002202 0.01956758285390561 -0.05140991436041962
+0.07819996384396966 4.127780662861551e-05 -0.03197150713685026
+0.05197415418742954 -0.03691972333053082 -0.06706167387722287
+0.04476194045990555 -0.03868788115484954 -0.093378926915626
+0.02642274895181671 -0.03698778874565389 -0.3570219353414122
+3.715759961701779e-07 -0.000167057565479412 0.9276509910347256
+0.003457264975454637 0.01607498180921493 0.4005836069123416
+0.03445624798895486 0.05826760669966152 -1.2301611547758755
+0.05399675986092924 0.07144915477192883 -1.6426557894762874
+0.06587533521034071 0.07717347429902713 -1.7332221980026814
+0.07799552636294979 0.08179901473482278 -1.754837005226387
+0.09287072676787013 0.0861933468529139 -1.7249851475023648
+0.1159231761269345 0.09090247468259746 -1.6181096392589778
+0.1870216529466714 0.09529101859089956 -1.1515796372398734
+0.2204942894351738 0.09463860823428381 -1.0426813364294585
+0.2996809993542806 0.08811305015450889 -0.7066174109381166
+0.3332789074473506 0.08439957244416332 -0.6088681071632966
+0.3687000900536647 0.08026370487218884 -0.5287719422111052
+0.4056386418146471 0.07580832798897223 -0.4621736253348554
+0.4565948443542798 0.06951968856030251 -0.3890242262205662
+0.5603789570099812 0.0564429374001441 -0.2790317212774662
+0.6709889816587014 0.04232145397591842 -0.19137133392787326
+0.8985775863007591 0.0130638909219138 -0.036892685638763
+0.9944657367345657 0.000713131800744486 0.0471956570293942
+1.000043909301717 -9.851122780894417e-06 0.04151915164349936
+1.000216009864362 -0.000279890227385287 0.04597681465716156
+0.9955965259210384 6.290342386802663e-05 0.0850038637137151
+0.9837355911010561 0.001590970086407752 0.1131781895823859
+0.9160973581448664 0.01030344679663074 0.1857913336595896
+0.8247212136041724 0.02206815331132695 0.2449938402700558
+0.7835335477130784 0.02736682776276132 0.2578459502494688
+0.7458784011853928 0.03220726993377351 0.2591066500658992
+0.704181333714036 0.03756145616169322 0.2463676060298896
+0.6588094047445807 0.04337787974553729 0.2145042006489572
+0.4817165482691285 0.06587661484209784 0.004772282753139308
+0.4300565639732108 0.07230837544542264 -0.04186437951123492
+0.3799297067584973 0.0784194374404138 -0.07216280351532364
+0.3322904223011696 0.08401082699279248 -0.08709921870404717
+0.2878908640215147 0.0888395395812468 -0.08863313732386507
+0.237700137204438 0.09320459962989104 -0.0728984865243349
+0.1938680582880188 0.09475868486167134 -0.03756026982500518
+0.1781651575110249 0.09480029283794308 -0.03241020524223182
+0.04571639419151958 0.06586873418008751 -0.0433295353537881
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_6.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_6.dat
new file mode 100755
index 0000000..7da2c81
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_6.dat
@@ -0,0 +1,55 @@
+# x y Cp
+0.04276388755929555 0.06356556861711357 0.2831765988277916
+0.06177764393266522 0.03185174700135322 0.280866185252911
+0.07295141860002202 0.01956758285390561 0.25963219479103
+0.07815773161604739 -0.001817316543405341 0.3226553243711208
+0.07606735331573551 -0.0127367285437733 0.3146385901820192
+0.06744746907549139 -0.0269300909630248 0.342684532314071
+0.05709225787431717 -0.03471437303988962 0.234853067095021
+0.05020468513322063 -0.0374899309873329 0.284736464587777
+0.03734550757143083 -0.03906061142774211 0.15553190031453823
+0.03364428007162458 -0.03871749253601962 0.16954883769738988
+0.004414604365619548 -0.01804807451598938 0.9863929885981783
+0.002091866108723892 0.01261774277269358 -0.572820273285277
+0.03160995824607752 0.05579211544351708 -2.432245537380402
+0.04505807310843256 0.06612426369258909 -2.774111357485588
+0.05199666670865696 0.07034321801435707 -2.844549106106646
+0.06072447947721454 0.07485584093822345 -2.867363498348572
+0.07161579828052492 0.07949795292659076 -2.829796764734134
+0.08887221045490147 0.08513218823611196 -2.690124986734862
+0.1870216529466714 0.09529101859089956 -1.6416426798621342
+0.2889286523063009 0.08923439218084046 -1.0436464025123768
+0.3218640927071186 0.08569020328035809 -0.9017119855459382
+0.3567078053717327 0.08168243973732783 -0.7850028154244542
+0.3931790397022614 0.07732341274896151 -0.6877927119270986
+0.4437200012730869 0.07112018838118041 -0.5809462948976902
+0.5085472107892827 0.06300526498795977 -0.4741340541213232
+0.5731865715040237 0.05481455085516451 -0.388873206174795
+0.7259539967148313 0.03527045550632674 -0.232643572741015
+0.9854751216984708 0.0018711616089445 -0.003723889307472904
+0.9964097252760367 0.0004627131567309691 0.002753800976306192
+1.000216009864362 -0.000279890227385287 -0.004826394769678074
+0.9943997089817618 0.0002170811127827793 0.04868571797704922
+0.9808969261659528 0.001956677923828914 0.09055972385002321
+0.9537275624931341 0.005456518791413356 0.13731493299292824
+0.9119523907158397 0.01083728805011287 0.19026388337497901
+0.6469580977353171 0.04489502959157916 0.4433304082027882
+0.5852500607119644 0.05277533327406693 0.4920978858473208
+0.5467873640304859 0.05766507732352074 0.5105806429851942
+0.5208166631398357 0.06095334182358406 0.5137794956559404
+0.4947454515238927 0.06424033472807196 0.5061160251107084
+0.4557607131227709 0.06912084908337753 0.4691184029843818
+0.4173409525953314 0.07387363386197163 0.4012507759782244
+0.3208625976738804 0.08530086466606873 0.17770486868504992
+0.2986548580999721 0.08772078502814058 0.14367991508003292
+0.2773623986528947 0.08988862416845661 0.12653828962832644
+0.2570330318015585 0.09174362234289557 0.12949031449777215
+0.237700137204438 0.09320459962989104 0.16011249221611834
+0.2106187027457731 0.09448229827809655 0.269821412704075
+0.1938680582880188 0.09475868486167134 0.3075080023769274
+0.1634966219696773 0.09459572113384887 0.3147298735128626
+0.1498463100440565 0.09399700027730548 0.305568189083699
+0.1255076224116574 0.09176672636466693 0.270229612624369
+0.1147579180611961 0.0902151563016211 0.2651942739621602
+0.07359886901176031 0.07974335793549313 0.297013291348983
+0.04571639419151958 0.06586873418008751 0.284032411033685
diff --git a/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_8.dat b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_8.dat
new file mode 100644
index 0000000..ce2f14c
--- /dev/null
+++ b/data/TUDELFT_V3_KITE/cpx_distributions/cp_AOA_8.dat
@@ -0,0 +1,59 @@
+# x y Cp
+0.04276388755929555 0.06356556861711357 0.369754792445869
+0.05363791167027467 0.03629681937611275 0.369512825076681
+0.06023799864411321 0.03289369258862966 0.3803300404656592
+0.07295141860002202 0.01956758285390561 0.4525002946292716
+0.07538949147001935 0.0145561611100228 0.4564693840993162
+0.07801937057148131 0.003753985959382214 0.446882922242024
+0.07780867321095557 -0.005517988661109592 0.4783581156255348
+0.06613535838326418 -0.02824710359527738 0.5311348004808492
+0.05709225787431717 -0.03471437303988962 0.484086890575362
+0.05197415418742954 -0.03691972333053082 0.508181593031596
+0.04106259222822374 -0.03905070100038476 0.42315148127032
+0.03734550757143083 -0.03906061142774211 0.428382483023469
+0.0135954264885147 -0.02963656804659672 0.9678991392350302
+0.009641067793166569 -0.0257095339280482 1.0009297902025698
+0.006285174856966845 -0.02126020489420191 0.9426534686983296
+0.00106093352703043 0.009046469258067052 -1.0429979768839335
+0.02885514542407597 0.05321522293646556 -2.97701516742313
+0.04040120622143255 0.06291072902875927 -3.321950362170818
+0.05399675986092924 0.07144915477192883 -3.429003607616636
+0.06587533521034071 0.07717347429902713 -3.376878702524202
+0.07799552636294979 0.08179901473482278 -3.254197144337922
+0.1870216529466714 0.09529101859089956 -1.8327874863459042
+0.2784114042826095 0.09028704743105777 -1.1996806915789198
+0.3106619665783404 0.08692971356968779 -1.0297320028124064
+0.3448969536063716 0.08306230186370865 -0.8915062484382064
+0.3931790397022614 0.07732341274896151 -0.7482650769295596
+0.4437200012730869 0.07112018838118041 -0.6372226271092232
+0.5085472107892827 0.06300526498795977 -0.5230590187758586
+0.5859002138042717 0.05319596110636245 -0.4121258594586978
+0.6824259822772941 0.04085560183950949 -0.298421444448391
+0.8093373719000714 0.02455167460498058 -0.17313674963583736
+0.9660562316502389 0.004372544645865283 -0.0386406821187513
+0.993768270458061 0.000802973252239986 -0.02128853044019792
+1.000216009864362 -0.000279890227385287 -0.03062803960527396
+0.9943997089817618 0.0002170811127827793 0.0244731662116314
+0.9818912176243451 0.001828584093814853 0.06971150300101164
+0.960286674485311 0.004611622190581761 0.11726533977474018
+0.9274984236403104 0.00883501341243748 0.1677274935281196
+0.8530499807859604 0.01842190075726221 0.2484797697022198
+0.6588094047445807 0.04337787974553729 0.4164065158335706
+0.5852500607119644 0.05277533327406693 0.4895377938887346
+0.5208166631398357 0.06095334182358406 0.571407956570911
+0.4173409525953314 0.07387363386197163 0.7448626251611238
+0.3922624879189146 0.07693223386512749 0.7709991898354575
+0.3799297067584973 0.0784194374404138 0.7726402699823532
+0.3677534071070601 0.07987434616211128 0.7634659759410414
+0.3557467275473164 0.08129361982160917 0.741382005954384
+0.3322904223011696 0.08401082699279248 0.654753629482031
+0.2570330318015585 0.09174362234289557 0.14088055807058458
+0.237700137204438 0.09320459962989104 0.05907810318650006
+0.2284148315498924 0.09375889647752622 0.04303112368943488
+0.2193869169365694 0.09418446720371103 0.04419401824806442
+0.2106187027457731 0.09448229827809655 0.06265088115711293
+0.1707025886246218 0.09474637369056492 0.2715407898968978
+0.1565453765113058 0.0943433883278578 0.2872016129793902
+0.09590926783756276 0.08644641392015195 0.2638217468843654
+0.0596424493908252 0.07383844242791567 0.350286588153118
+0.04571639419151958 0.06586873418008751 0.3689025452585844
diff --git a/docs/plotly_render.png b/docs/plotly_render.png
new file mode 100644
index 0000000..404ef33
Binary files /dev/null and b/docs/plotly_render.png differ
diff --git a/examples/TUDELFT_V3_KITE/fancy_interactive_plot.py b/examples/TUDELFT_V3_KITE/fancy_interactive_plot.py
new file mode 100644
index 0000000..ab0b13a
--- /dev/null
+++ b/examples/TUDELFT_V3_KITE/fancy_interactive_plot.py
@@ -0,0 +1,85 @@
+"""Interactive "fancy" 3D Plotly plot of the TU Delft V3 kite.
+
+Renders the physical inflatable structure of the LEI kite -- the leading-edge
+tube, the chordwise strut tubes and the curved single-skin canopy -- reconstructed
+from a raw Surfplan export, together with the VSM aerodynamic loading drawn as
+chord-normal force vectors distributed over the chord by a measured Cp(x/c).
+
+Requirements (already handled by the data shipped in the repo):
+  - a Surfplan export directory: a ``<name>.txt`` plus a ``profiles/`` directory,
+  - (optional) a directory of ``cp_AOA_<deg>.dat`` chordwise Cp distributions.
+
+SurfplanAdapter must be installed (a dependency of VSM) -- it converts the raw
+export into the VSM geometry the plot needs (cached on first run).
+"""
+
+from pathlib import Path
+
+from VSM.core.BodyAerodynamics import BodyAerodynamics
+from VSM.plot_geometry_plotly import interactive_plot, save_high_res_render
+
+PROJECT_DIR = Path(__file__).resolve().parents[2]
+DATA_DIR = PROJECT_DIR / "data" / "TUDELFT_V3_KITE"
+RESULTS_DIR = PROJECT_DIR / "results" / "TUDELFT_V3_KITE"
+
+# Set True to also write a high-resolution PNG (results/.../plotly_render.png)
+SAVE_HIGH_RES_PNG = True
+
+
+def main():
+    # --- inflow / operating point -------------------------------------------
+    Umag = 10.0  # [m/s]
+    angle_of_attack = 8.0  # [deg]
+    side_slip = 0.0  # [deg]
+
+    # --- build the aerodynamic model ----------------------------------------
+    # The Breukels-regression CAD geometry needs no ML models, so it runs
+    # out of the box. Any other aero_geometry_*.yaml works too.
+    aero_geometry = (
+        DATA_DIR / "CAD_derived_geometry" / "aero_geometry_CAD_breukels_regression.yaml"
+    )
+    body_aero = BodyAerodynamics.instantiate(
+        n_panels=36,
+        file_path=aero_geometry,
+        spanwise_panel_distribution="uniform",
+    )
+
+    # --- data for the fancy plot --------------------------------------------
+    # Raw Surfplan export (tubes + airfoil profiles) and the Cp database.
+    surfplan_dir = DATA_DIR / "Surfplan_export"
+    cp_distributions_dir = DATA_DIR / "cpx_distributions"
+
+    # --- interactive fancy plot ---------------------------------------------
+    # Passing ``surfplan_dir`` switches on the inflatable tubes + curved canopy.
+    # Passing ``cp_distributions_dir`` distributes each panel's VSM force over the
+    # chord using the closest-AoA measured Cp(x/c). Omit either to fall back.
+    fig = interactive_plot(
+        body_aero,
+        vel=Umag,
+        angle_of_attack=angle_of_attack,
+        side_slip=side_slip,
+        title="TUDELFT_V3_KITE",
+        surfplan_dir=surfplan_dir,
+        cp_distributions_dir=cp_distributions_dir,
+        is_with_canopy=True,  # curved canopy over the tubes (False -> tubes only)
+        is_with_tube_rings=False,  # True -> also draw the tube construction rings
+        is_show=True,  # open the interactive plot in a browser
+        # is_save=True,  # uncomment to also write an interactive .html
+        # save_path=str(RESULTS_DIR / "fancy_plot.html"),
+    )
+
+    # --- optional: high-resolution PNG (matches the interactive view) --------
+    if SAVE_HIGH_RES_PNG:
+        save_high_res_render(
+            fig,
+            RESULTS_DIR / "plotly_render.png",
+            base_size=2000,  # square canvas -> undistorted; then cropped to the kite
+            scale=4,
+            show_legend=False,
+            show_axes=False,
+            show_annotations=False,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/TUDELFT_V3_KITE/tutorial.py b/examples/TUDELFT_V3_KITE/tutorial.py
index f5460fd..edf75a0 100644
--- a/examples/TUDELFT_V3_KITE/tutorial.py
+++ b/examples/TUDELFT_V3_KITE/tutorial.py
@@ -49,7 +49,7 @@ def main():
     PROJECT_DIR = Path(__file__).resolve().parents[2]
 
     ### 2. defining settings
-    n_panels = 50
+    n_panels = 100
     spanwise_panel_distribution = "uniform"
     solver_base_version = Solver(reference_point=np.array([0.0, 0.0, 0.0]))
 
@@ -62,28 +62,30 @@ def main():
         file_path=(cad_derived_geometry_dir / "aero_geometry_CAD_CFD_polars.yaml"),
         spanwise_panel_distribution=spanwise_panel_distribution,
     )
-    body_aero_CAD_CFD_polars_with_bridles = BodyAerodynamics.instantiate(
-        n_panels=n_panels,
-        # file_path=(cad_derived_geometry_dir / "config_kite_CAD_CFD_polars.yaml"),
-        file_path=(cad_derived_geometry_dir / "config_kite_CAD_CFD_polars.yaml"),
-        spanwise_panel_distribution=spanwise_panel_distribution,
-        bridle_path=(
-            cad_derived_geometry_dir / "struc_geometry_manually_adjusted.yaml"
-        ),
-    )
-    body_aero_CAD_neuralfoil = BodyAerodynamics.instantiate(
-        n_panels=n_panels,
-        file_path=(cad_derived_geometry_dir / "aero_geometry_CAD_neuralfoil.yaml"),
-        spanwise_panel_distribution=spanwise_panel_distribution,
-    )
-    body_aero_masure_regression = BodyAerodynamics.instantiate(
-        n_panels=n_panels,
-        file_path=(
-            cad_derived_geometry_dir / "aero_geometry_CAD_masure_regression.yaml"
-        ),
-        ml_models_dir=(Path(PROJECT_DIR) / "data" / "ml_models"),
-        spanwise_panel_distribution=spanwise_panel_distribution,
-    )
+    # body_aero_CAD_CFD_polars_with_bridles = BodyAerodynamics.instantiate(
+    #     n_panels=n_panels,
+    #     file_path=(
+    #         cad_derived_geometry_dir
+    #         / "config_kite_CAD_CFD_polars_converged_fitted_pchip.yaml"
+    #     ),
+    #     spanwise_panel_distribution=spanwise_panel_distribution,
+    #     bridle_path=(
+    #         cad_derived_geometry_dir / "struc_geometry_manually_adjusted.yaml"
+    #     ),
+    # )
+    # body_aero_CAD_neuralfoil = BodyAerodynamics.instantiate(
+    #     n_panels=n_panels,
+    #     file_path=(cad_derived_geometry_dir / "aero_geometry_CAD_neuralfoil.yaml"),
+    #     spanwise_panel_distribution=spanwise_panel_distribution,
+    # )
+    # body_aero_masure_regression = BodyAerodynamics.instantiate(
+    #     n_panels=n_panels,
+    #     file_path=(
+    #         cad_derived_geometry_dir / "aero_geometry_CAD_masure_regression.yaml"
+    #     ),
+    #     ml_models_dir=(Path(PROJECT_DIR) / "data" / "ml_models"),
+    #     spanwise_panel_distribution=spanwise_panel_distribution,
+    # )
 
     # Step 2: Set inflow conditions for each aerodynamic object
     """
@@ -97,40 +99,40 @@ def main():
     body_aero_CAD_CFD_polars.va_initialize(
         Umag, angle_of_attack, side_slip, body_rates=yaw_rate
     )
-    body_aero_CAD_CFD_polars_with_bridles.va_initialize(
-        Umag,
-        angle_of_attack,
-        side_slip,
-        body_rates=yaw_rate,
-        body_axis=np.array([0, 0, 1]),
-    )
-    body_aero_CAD_neuralfoil.va_initialize(
-        Umag,
-        angle_of_attack,
-        side_slip,
-        body_rates=yaw_rate,
-        body_axis=np.array([0, 0, 1]),
-    )
-    body_aero_masure_regression.va_initialize(
-        Umag,
-        angle_of_attack,
-        side_slip,
-        body_rates=yaw_rate,
-        body_axis=np.array([0, 0, 1]),
-    )
+    # body_aero_CAD_CFD_polars_with_bridles.va_initialize(
+    #     Umag,
+    #     angle_of_attack,
+    #     side_slip,
+    #     body_rates=yaw_rate,
+    #     body_axis=np.array([0, 0, 1]),
+    # )
+    # body_aero_CAD_neuralfoil.va_initialize(
+    #     Umag,
+    #     angle_of_attack,
+    #     side_slip,
+    #     body_rates=yaw_rate,
+    #     body_axis=np.array([0, 0, 1]),
+    # )
+    # body_aero_masure_regression.va_initialize(
+    #     Umag,
+    #     angle_of_attack,
+    #     side_slip,
+    #     body_rates=yaw_rate,
+    #     body_axis=np.array([0, 0, 1]),
+    # )
 
     # Step 3: Plot the kite geometry using Matplotlib
     """
     Visualize the panel mesh, control points, and aerodynamic centers for the selected BodyAerodynamics object.
     """
-    plot_geometry(
-        body_aero_CAD_CFD_polars,
-        title="TUDELFT_V3_KITE",
-        data_type=".pdf",
-        save_path=".",
-        is_save=False,
-        is_show=True,
-    )
+    # plot_geometry(
+    #     body_aero_CAD_CFD_polars,
+    #     title="TUDELFT_V3_KITE",
+    #     data_type=".pdf",
+    #     save_path=".",
+    #     is_save=False,
+    #     is_show=True,
+    # )
 
     # Step 4: Create an interactive plot using Plotly
     """
@@ -138,7 +140,7 @@ def main():
     This allows for interactive exploration of the geometry and panel arrangement.
     """
     interactive_plot(
-        body_aero_CAD_CFD_polars_with_bridles,
+        body_aero_CAD_CFD_polars,
         vel=Umag,
         angle_of_attack=angle_of_attack,
         side_slip=side_slip,
@@ -154,105 +156,128 @@ def main():
         # / "interactive_plot.html",
     )
 
-    # Step 5: Plot polar curves for different angles of attack and side slip angles, and save results
+    # Step 4b: "Fancy" plot -- render the inflatable leading-edge and strut tubes
+    # and the curved single-skin canopy from a raw Surfplan export.
     """
-    Compare the aerodynamic performance of different models by plotting lift, drag, and side force coefficients
-    as a function of angle of attack (alpha sweep) and side slip (beta sweep).
-    Literature/CFD data can be included for validation.
+    Passing ``surfplan_dir`` (a directory holding the Surfplan '<name>.txt' export
+    and its 'profiles/' directory) switches on the physically-detailed plot. The
+    export is converted with SurfplanAdapter (cached under the export directory),
+    and the tube diameters + airfoil top-surfaces are drawn on the wing. The plain
+    plot above is unaffected when ``surfplan_dir`` is omitted.
     """
-    save_folder = Path(PROJECT_DIR) / "results" / "TUDELFT_V3_KITE"
+    # interactive_plot(
+    #     body_aero_CAD_CFD_polars,
+    #     vel=Umag,
+    #     angle_of_attack=angle_of_attack,
+    #     side_slip=side_slip,
+    #     title="TUDELFT_V3_KITE (inflatable tubes + canopy)",
+    #     surfplan_dir=Path(PROJECT_DIR)
+    #     / "data"
+    #     / "TUDELFT_V3_KITE"
+    #     / "Surfplan_export",
+    #     is_with_canopy=True,  # curved canopy over the tubes; False -> tubes only
+    #     is_with_tube_rings=False,  # True -> also show tube construction rings
+    # )
 
-    # Step 5a: Plot alpha sweep (angle of attack)
-    path_cfd_lebesque_alpha = (
-        Path(PROJECT_DIR)
-        / "data"
-        / "TUDELFT_V3_KITE"
-        / "3D_polars_literature"
-        / "CFD_RANS_Rey_10e5_Poland2025_alpha_sweep_beta_0.csv"
-    )
-    path_wt_data = (
-        Path(PROJECT_DIR)
-        / "data"
-        / "TUDELFT_V3_KITE"
-        / "3D_polars_literature"
-        / "V3_CL_CD_CS_alpha_sweep_for_beta_0_WindTunnel_Poland_2025_Rey_560e4.csv"
-    )
-    plot_polars(
-        solver_list=[
-            solver_base_version,
-            solver_base_version,
-            solver_base_version,
-            solver_base_version,
-        ],
-        body_aero_list=[
-            body_aero_CAD_CFD_polars,
-            body_aero_CAD_CFD_polars_with_bridles,
-            body_aero_CAD_neuralfoil,
-            body_aero_masure_regression,
-        ],
-        label_list=[
-            "VSM CAD CFD Polars",
-            "VSM CAD CFD Polars with Bridles",
-            "VSM CAD NeuralFoil",
-            "VSM CAD Masure Regression",
-            "CFD_RANS_Rey_10e5_Poland2025_alpha_sweep_beta_0",
-            "Wind Tunnel Data Poland 2025",
-        ],
-        literature_path_list=[path_cfd_lebesque_alpha, path_wt_data],
-        angle_range=[0, 5, 8, 10, 12, 15, 20, 25],
-        angle_type="angle_of_attack",
-        angle_of_attack=0,
-        side_slip=0,
-        yaw_rate=0,
-        Umag=Umag,
-        title="alphasweep",
-        data_type=".pdf",
-        save_path=Path(save_folder),
-        is_save=True,
-        is_show=False,
-    )
+    # # Step 5: Plot polar curves for different angles of attack and side slip angles, and save results
+    # """
+    # Compare the aerodynamic performance of different models by plotting lift, drag, and side force coefficients
+    # as a function of angle of attack (alpha sweep) and side slip (beta sweep).
+    # Literature/CFD data can be included for validation.
+    # """
+    # save_folder = Path(PROJECT_DIR) / "results" / "TUDELFT_V3_KITE"
 
-    # Step 5b: Plot beta sweep (side slip)
-    path_cfd_lebesque_beta = (
-        Path(PROJECT_DIR)
-        / "data"
-        / "TUDELFT_V3_KITE"
-        / "3D_polars_literature"
-        / "CFD_RANS_Rey_10e5_Poland2025_beta_sweep_alpha_13_02.csv"
-    )
-    plot_polars(
-        solver_list=[
-            solver_base_version,
-            solver_base_version,
-            solver_base_version,
-            solver_base_version,
-        ],
-        body_aero_list=[
-            body_aero_CAD_CFD_polars,
-            body_aero_CAD_CFD_polars_with_bridles,
-            body_aero_CAD_neuralfoil,
-            body_aero_masure_regression,
-        ],
-        label_list=[
-            "VSM CAD CFD Polars",
-            "VSM CAD CFD Polars with Bridles",
-            "VSM CAD NeuralFoil",
-            "VSM CAD Masure Regression",
-            "CFD_RANS_Rey_10e5_Poland2025_beta_sweep_alpha_13_02",
-        ],
-        literature_path_list=[path_cfd_lebesque_beta],
-        angle_range=[0, 3, 6, 9, 12],
-        angle_type="side_slip",
-        angle_of_attack=6.8,
-        side_slip=0,
-        yaw_rate=0,
-        Umag=3.15,
-        title="betasweep",
-        data_type=".pdf",
-        save_path=Path(save_folder),
-        is_save=True,
-        is_show=True,
-    )
+    # # Step 5a: Plot alpha sweep (angle of attack)
+    # path_cfd_lebesque_alpha = (
+    #     Path(PROJECT_DIR)
+    #     / "data"
+    #     / "TUDELFT_V3_KITE"
+    #     / "3D_polars_literature"
+    #     / "CFD_RANS_Rey_10e5_Poland2025_alpha_sweep_beta_0.csv"
+    # )
+    # path_wt_data = (
+    #     Path(PROJECT_DIR)
+    #     / "data"
+    #     / "TUDELFT_V3_KITE"
+    #     / "3D_polars_literature"
+    #     / "windtunnel_alpha_sweep_beta_00_0_Poland_2025_Rey_5e5.csv"
+    # )
+    # plot_polars(
+    #     solver_list=[
+    #         solver_base_version,
+    #         solver_base_version,
+    #         solver_base_version,
+    #         solver_base_version,
+    #     ],
+    #     body_aero_list=[
+    #         body_aero_CAD_CFD_polars,
+    #         body_aero_CAD_CFD_polars_with_bridles,
+    #         body_aero_CAD_neuralfoil,
+    #         body_aero_masure_regression,
+    #     ],
+    #     label_list=[
+    #         "VSM CAD CFD Polars",
+    #         "VSM CAD CFD Polars with Bridles",
+    #         "VSM CAD NeuralFoil",
+    #         "VSM CAD Masure Regression",
+    #         "CFD_RANS_Rey_10e5_Poland2025_alpha_sweep_beta_0",
+    #         "Wind Tunnel Data Poland 2025",
+    #     ],
+    #     literature_path_list=[path_cfd_lebesque_alpha, path_wt_data],
+    #     angle_range=[0, 5, 8, 10, 12, 15, 20, 25],
+    #     angle_type="angle_of_attack",
+    #     angle_of_attack=0,
+    #     side_slip=0,
+    #     yaw_rate=0,
+    #     Umag=Umag,
+    #     title="alphasweep",
+    #     data_type=".pdf",
+    #     save_path=Path(save_folder),
+    #     is_save=True,
+    #     is_show=False,
+    # )
+
+    # # Step 5b: Plot beta sweep (side slip)
+    # path_cfd_lebesque_beta = (
+    #     Path(PROJECT_DIR)
+    #     / "data"
+    #     / "TUDELFT_V3_KITE"
+    #     / "3D_polars_literature"
+    #     / "CFD_RANS_Re1e6_beta_sweep_alpha_13_Vire2022_CorrectedByPoland2025.csv"
+    # )
+    # plot_polars(
+    #     solver_list=[
+    #         solver_base_version,
+    #         solver_base_version,
+    #         solver_base_version,
+    #         solver_base_version,
+    #     ],
+    #     body_aero_list=[
+    #         body_aero_CAD_CFD_polars,
+    #         body_aero_CAD_CFD_polars_with_bridles,
+    #         body_aero_CAD_neuralfoil,
+    #         body_aero_masure_regression,
+    #     ],
+    #     label_list=[
+    #         "VSM CAD CFD Polars",
+    #         "VSM CAD CFD Polars with Bridles",
+    #         "VSM CAD NeuralFoil",
+    #         "VSM CAD Masure Regression",
+    #         "CFD_RANS_Re1e6_beta_sweep_alpha_13_Vire2022_CorrectedByPoland2025",
+    #     ],
+    #     literature_path_list=[path_cfd_lebesque_beta],
+    #     angle_range=[0, 3, 6, 9, 12],
+    #     angle_type="side_slip",
+    #     angle_of_attack=6.8,
+    #     side_slip=0,
+    #     yaw_rate=0,
+    #     Umag=3.15,
+    #     title="betasweep",
+    #     data_type=".pdf",
+    #     save_path=Path(save_folder),
+    #     is_save=True,
+    #     is_show=True,
+    # )
 
 
 if __name__ == "__main__":
diff --git a/pyproject.toml b/pyproject.toml
index 0f938d5..e7eea64 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,6 +51,7 @@ dependencies = [
   "numba",
   "screeninfo",
   "odfpy",
+  "SurfplanAdapter",
 ]
 [project.optional-dependencies]
 dev = [
diff --git a/src/VSM/core/Solver.py b/src/VSM/core/Solver.py
index 96d3766..bb03468 100644
--- a/src/VSM/core/Solver.py
+++ b/src/VSM/core/Solver.py
@@ -6,6 +6,26 @@
 from VSM.core import panel_kernels
 
 
+
+def _table_symbols(n_panels: int, alpha_tables) -> dict:
+    """Symbolic ``(n_panels, n_alpha)`` polar tables for ``table_inputs``."""
+    import casadi as ca
+
+    n_alpha = int(np.asarray(alpha_tables[0]).size)
+    return {name: ca.MX.sym(f"{name}_table", n_panels, n_alpha) for name in ("cl", "cd", "cm")}
+
+
+def _with_table_inputs(inputs: list, input_names: list, table_syms):
+    """Append the table inputs (if any) and default them to NaN, so a call
+    that omits them (CasADi fills omitted inputs with 0 by default) gives NaN
+    rather than a silently zero polar."""
+    if table_syms is None:
+        return inputs, input_names, {}
+    inputs = inputs + [table_syms[k] for k in ("cl", "cd", "cm")]
+    input_names = input_names + ["cl_table", "cd_table", "cm_table"]
+    defaults = [0.0] * (len(inputs) - 3) + [float("nan")] * 3
+    return inputs, input_names, {"default_in": defaults}
+
 class Solver:
     """Solver for aerodynamic circulation distribution and force computation.
 
@@ -203,6 +223,7 @@ def __init__(
         self._newton_reject_ratio = 0.5
         self._casadi_newton_cache: dict = {}
         self._casadi_newton_loop_cache: dict = {}
+        self._casadi_table_input_cache: dict = {}  # polar tables as inputs
         #: Diagnostic: did the last casadi_newton solve fall back to base?
         self.last_fallback = False
         #: Diagnostic: residual evaluations used by the last casadi_newton solve.
@@ -1030,12 +1051,91 @@ def _casadi_newton_key(self, alpha_tables, cl_tables, cd_tables, cm_tables) -> t
             digest.hexdigest(),
         )
 
-    def _casadi_polar_lookups(self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None):
+    def _casadi_table_input_key(self, alpha_tables) -> tuple:
+        """Cache key of the functions that take the polar tables as INPUTS
+        (``table_inputs=True``): the graph depends on the panel count, the
+        shared alpha grid, the interpolation and the regularization settings,
+        not on the table values, so one function serves every shape whose
+        blended polars change but keep the grid."""
+        self._check_table_inputs_supported(alpha_tables)
+        grid = np.ascontiguousarray(alpha_tables[0], dtype=float)
+        return (
+            "table_inputs",
+            self.n_panels,
+            bool(self.is_with_artificial_viscosity),
+            float(self.artificial_viscosity_factor),
+            self.polar_interpolation,
+            hashlib.blake2b(grid.tobytes(), digest_size=16).hexdigest(),
+        )
+
+    def _check_table_inputs_supported(self, alpha_tables) -> None:
+        if self.polar_interpolation != "linear":
+            # A parametric cubic spline refits its coefficients on every call
+            # (~0.1 s per evaluation), which defeats the purpose.
+            raise ValueError(
+                "Polar tables as CasADi inputs need polar_interpolation='linear' "
+                f"(got {self.polar_interpolation!r})."
+            )
+        if not all(
+            table.shape == alpha_tables[0].shape and np.array_equal(table, alpha_tables[0])
+            for table in alpha_tables[1:]
+        ):
+            raise ValueError(
+                "Polar tables as CasADi inputs need every panel polar on one "
+                "shared alpha grid."
+            )
+
+    def casadi_polar_table_inputs(self) -> dict:
+        """The current panels' polar tables in the layout of the ``table_inputs``
+        CasADi functions (:meth:`_casadi_newton_function` and
+        :meth:`casadi_panel_force_function` with ``table_inputs=True``):
+        ``{"cl_table", "cd_table", "cm_table"}``, each ``(n_panels, n_alpha)``,
+        row ``i`` = panel ``i``'s coefficients on the shared alpha grid
+        (``panel.panel_polar_data[:, 0]``, radians).
+
+        Pass them on every call, next to the geometry: a deformed shape blends
+        its section polars with new weights, so the values change while the
+        grid, and with it the function, stays the same.
+        """
+        alpha_tables, cl_tables, cd_tables, cm_tables = self._polar_tables()
+        self._check_table_inputs_supported(alpha_tables)
+        return {
+            "cl_table": np.vstack(cl_tables),
+            "cd_table": np.vstack(cd_tables),
+            "cm_table": np.vstack(cm_tables),
+        }
+
+    def casadi_stall_angle_inputs(self) -> dict:
+        """``{"stall_angles", "stall_angles_neg"}`` [rad] of the current panel
+        polars, the gate inputs of the residual function as
+        :meth:`gamma_loop_casadi_newton` passes them (plus / minus infinity
+        without artificial viscosity). They are derived from the tables, so a
+        caller passing new tables passes these new values with them."""
+        if self.is_with_artificial_viscosity:
+            return {
+                "stall_angles": self._panel_stall_angles(),
+                "stall_angles_neg": self._panel_negative_stall_angles(),
+            }
+        return {
+            "stall_angles": np.full(self.n_panels, np.inf),
+            "stall_angles_neg": np.full(self.n_panels, -np.inf),
+        }
+
+    def _casadi_polar_lookups(
+        self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None, table_symbols=None
+    ):
         """Clamped CasADi lookups ``alpha (n) -> cl, cd, cm (n)`` of the
         per-panel polars, shared by :meth:`_build_casadi_newton_function` and
         :meth:`_build_casadi_panel_force_function` (see the former for the
         interpolation). Returns ``(cl_of, cd_of, cm_of, smooth)``, ``smooth``
         True for "bspline" polars.
+
+        ``table_symbols`` (``{"cl": MX, "cd": MX, "cm": MX}``, each
+        ``(n, n_alpha)``) makes the table VALUES symbolic inputs instead of
+        constants: one 1-D parametric linear interpolant on the shared grid,
+        mapped over the panels. It reproduces the constant table exactly
+        (same nodes, same clamping); only the shared-grid linear case is
+        supported (see :meth:`_check_table_inputs_supported`).
         """
         import casadi as ca
 
@@ -1059,6 +1159,33 @@ def _casadi_polar_lookups(self, alpha_tables, cl_tables, cd_tables=None, cm_tabl
         kind = self.polar_interpolation
         smooth = kind == "bspline"
 
+        if table_symbols is not None:
+            self._check_table_inputs_supported(alpha_tables)
+            grid = np.asarray(alpha_tables[0], dtype=float)
+            lo, hi = float(grid[0]), float(grid[-1])
+            row_interpolant = ca.interpolant("polar_row", "linear", [grid], 1)
+            alpha_row = ca.MX.sym("alpha_row")
+            values_row = ca.MX.sym("values_row", grid.size)
+            # One panel's lookup; .map(n) evaluates it column-wise over panels.
+            row_lookup = ca.Function(
+                "polar_row_lookup",
+                [alpha_row, values_row],
+                [row_interpolant(ca.fmin(ca.fmax(alpha_row, lo), hi), values_row)],
+            ).map(n)
+
+            def make_parametric_lookup(table):
+                def lookup(alpha_vec):
+                    return row_lookup(alpha_vec.T, table.T).T
+
+                return lookup
+
+            return (
+                make_parametric_lookup(table_symbols["cl"]),
+                make_parametric_lookup(table_symbols["cd"]),
+                make_parametric_lookup(table_symbols["cm"]),
+                smooth,
+            )
+
         def make_lookup(name, value_tables):
             """Clamped lookup ``alpha_vec (n) -> values (n)`` of one polar column."""
             if shared_grid:
@@ -1104,7 +1231,9 @@ def lookup(alpha_vec):
         cm_of = make_lookup("cm", cm_tables)
         return cl_of, cd_of, cm_of, smooth
 
-    def _build_casadi_newton_function(self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None):
+    def _build_casadi_newton_function(
+        self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None, table_inputs=False
+    ):
         """Build the CasADi function of the PER-PANEL section physics that
         :meth:`gamma_loop_casadi_newton` assembles its residual and exact
         Jacobian from.
@@ -1154,12 +1283,18 @@ def _build_casadi_newton_function(self, alpha_tables, cl_tables, cd_tables=None,
         Jacobian), ``cl``, ``cd``, ``cm`` at ``alpha`` and their corner-
         averaged counterparts ``cl_avg``, ``cd_avg``, ``cm_avg``. With
         "bspline" polars the averaged outputs equal the exact ones.
+
+        ``table_inputs=True`` appends the inputs ``cl_table``, ``cd_table``,
+        ``cm_table`` (each ``(n, n_alpha)``, :meth:`casadi_polar_table_inputs`)
+        and takes the table values from them instead of embedding them; see
+        :meth:`_casadi_newton_function`.
         """
         import casadi as ca
 
         n = self.n_panels
+        table_syms = _table_symbols(n, alpha_tables) if table_inputs else None
         cl_of, cd_of, cm_of, smooth = self._casadi_polar_lookups(
-            alpha_tables, cl_tables, cd_tables, cm_tables
+            alpha_tables, cl_tables, cd_tables, cm_tables, table_symbols=table_syms
         )
 
         v_rel = ca.MX.sym("v_rel", n, 3)
@@ -1244,9 +1379,12 @@ def averaged(lookup):
         seeds = [ca.DM(np.eye(3)[k][None, :].repeat(n, axis=0)) for k in range(3)]
         dh_dvrel = ca.horzcat(*[ca.jtimes(h_for_jacobian, v_rel, seed) for seed in seeds])
 
+        inputs = [v_rel, x_airf, y_airf, z_airf, chord, width, stall_angles, stall_angles_neg, l_gamma]
+        input_names = ["v_rel", "x_airf", "y_airf", "z_airf", "chord", "width", "stall_angles", "stall_angles_neg", "l_gamma"]
+        inputs, input_names, options = _with_table_inputs(inputs, input_names, table_syms)
         return ca.Function(
-            "vsm_panel_residual",
-            [v_rel, x_airf, y_airf, z_airf, chord, width, stall_angles, stall_angles_neg, l_gamma],
+            "vsm_panel_residual_tables" if table_inputs else "vsm_panel_residual",
+            inputs,
             [
                 h,
                 dh_dvrel,
@@ -1262,11 +1400,29 @@ def averaged(lookup):
                 averaged(cd_of),
                 averaged(cm_of),
             ],
-            ["v_rel", "x_airf", "y_airf", "z_airf", "chord", "width", "stall_angles", "stall_angles_neg", "l_gamma"],
+            input_names,
             ["h", "dh_dvrel", "mu", "alpha", "umag", "gate", "h_avg", "cl", "cd", "cm", "cl_avg", "cd_avg", "cm_avg"],
+            options,
         )
 
-    def _casadi_newton_function(self):
+    def _casadi_newton_function(self, table_inputs: bool = False):
+        """The per-panel residual function (:meth:`_build_casadi_newton_function`)
+        of the current panels, cached.
+
+        ``table_inputs=False`` (default, what :meth:`gamma_loop_casadi_newton`
+        uses): the polar tables are constants of the graph, cached per table
+        set. ``table_inputs=True``: the tables are the extra inputs
+        ``cl_table, cd_table, cm_table``, cached per alpha grid, so a caller
+        that re-trims deformed shapes builds the function once and passes
+        :meth:`casadi_polar_table_inputs` (and :meth:`casadi_stall_angle_inputs`)
+        per shape. Same outputs to round-off; needs ``polar_interpolation=
+        "linear"`` and one shared alpha grid. Omitted table inputs default to
+        NaN, so a call that forgets them fails loudly instead of using zeros.
+        """
+        if table_inputs:
+            return self._cached_table_input_function(
+                "newton", self._build_casadi_newton_function
+            )
         alpha_tables, cl_tables, cd_tables, cm_tables = self._polar_tables()
         key = self._casadi_newton_key(alpha_tables, cl_tables, cd_tables, cm_tables)
         fn = self._casadi_newton_cache.get(key)
@@ -1282,7 +1438,7 @@ def _casadi_newton_function(self):
         return fn
 
     def _build_casadi_panel_force_function(
-        self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None
+        self, alpha_tables, cl_tables, cd_tables=None, cm_tables=None, table_inputs=False
     ):
         """CasADi function of the per-panel section forces, the force law of
         ``compute_results`` (``VSM.core.panel_kernels.panel_forces``) on the
@@ -1291,8 +1447,9 @@ def _build_casadi_panel_force_function(
         import casadi as ca
 
         n = self.n_panels
+        table_syms = _table_symbols(n, alpha_tables) if table_inputs else None
         cl_of, cd_of, cm_of, _ = self._casadi_polar_lookups(
-            alpha_tables, cl_tables, cd_tables, cm_tables
+            alpha_tables, cl_tables, cd_tables, cm_tables, table_symbols=table_syms
         )
         v_rel = ca.MX.sym("v_rel", n, 3)
         v_qc = ca.MX.sym("v_qc", n, 3)
@@ -1310,15 +1467,21 @@ def _build_casadi_panel_force_function(
             alpha, umag, cl, cd, cm, chord, width, x_airf, y_airf, z_airf, rho,
             alpha_dir=alpha_dir, xp=ca,
         )
-        return ca.Function(
-            "vsm_panel_forces",
+        inputs, input_names, options = _with_table_inputs(
             [v_rel, v_qc, x_airf, y_airf, z_airf, chord, width, rho],
-            [force, moment_local, alpha, alpha_dir, umag, cl, cd, cm],
             ["v_rel", "v_qc", "x_airf", "y_airf", "z_airf", "chord", "width", "rho"],
+            table_syms,
+        )
+        return ca.Function(
+            "vsm_panel_forces_tables" if table_inputs else "vsm_panel_forces",
+            inputs,
+            [force, moment_local, alpha, alpha_dir, umag, cl, cd, cm],
+            input_names,
             ["force", "moment_local", "alpha", "alpha_dir", "umag", "cl", "cd", "cm"],
+            options,
         )
 
-    def casadi_panel_force_function(self):
+    def casadi_panel_force_function(self, table_inputs: bool = False):
         """CasADi function of the per-panel section forces of the current
         panels (the ones of the last :meth:`solve`), cached per polar set like
         the ``casadi_newton`` residual function.
@@ -1343,9 +1506,16 @@ def casadi_panel_force_function(self):
         vortex force (``panel_kernels.attached_trailed_forces``) and the
         viscous drag correction. Taking ``v_qc`` rather than the matrices keeps
         the graph O(n); the caller forms the dense product.
+
+        ``table_inputs=True`` appends ``cl_table, cd_table, cm_table`` as
+        inputs (see :meth:`_casadi_newton_function`).
         """
         import casadi  # noqa: F401  (clear error when the optional dep is missing)
 
+        if table_inputs:
+            return self._cached_table_input_function(
+                "panel_forces", self._build_casadi_panel_force_function
+            )
         alpha_tables, cl_tables, cd_tables, cm_tables = self._polar_tables()
         key = ("panel_forces",) + self._casadi_newton_key(
             alpha_tables, cl_tables, cd_tables, cm_tables
@@ -1360,6 +1530,20 @@ def casadi_panel_force_function(self):
             self._casadi_newton_cache[key] = fn
         return fn
 
+    def _cached_table_input_function(self, kind: str, builder):
+        """Function of ``kind`` with the polar tables as inputs, built once
+        per (panel count, alpha grid, settings) and then reused for any
+        table values."""
+        alpha_tables, cl_tables, cd_tables, cm_tables = self._polar_tables()
+        key = (kind,) + self._casadi_table_input_key(alpha_tables)
+        fn = self._casadi_table_input_cache.get(key)
+        if fn is None:
+            fn = builder(alpha_tables, cl_tables, cd_tables, cm_tables, table_inputs=True)
+            if len(self._casadi_table_input_cache) >= 32:
+                self._casadi_table_input_cache.pop(next(iter(self._casadi_table_input_cache)))
+            self._casadi_table_input_cache[key] = fn
+        return fn
+
     def _casadi_newton_loop_function(self):
         """The residual function restricted to the six outputs the Newton loop
         reads (``h, dh_dvrel, mu, alpha, umag, gate``): the same graph, minus
diff --git a/src/VSM/plot_geometry_plotly.py b/src/VSM/plot_geometry_plotly.py
index 236b62b..0d99386 100644
--- a/src/VSM/plot_geometry_plotly.py
+++ b/src/VSM/plot_geometry_plotly.py
@@ -1,46 +1,62 @@
 import os
-from typing import List, Tuple, Dict, Any
+from pathlib import Path
+from typing import List, Tuple, Dict, Any, Optional
 import numpy as np
 import plotly.graph_objects as go
 from VSM.core.Solver import Solver
 import yaml
 
 
-def add_panel_edges(fig: go.Figure, panel: Any, is_first: bool, is_last: bool) -> None:
-    """Add panel edges to the figure with different thicknesses."""
-    leadinge_edge_line_color = "black"
-    leadinge_edge_line_width = 15
-    # Leading edge
-    if is_first:
-        # Include 1-side edge
-        fig.add_trace(
-            go.Scatter3d(
-                x=[panel.LE_point_1[0], panel.TE_point_1[0]],
-                y=[panel.LE_point_1[1], panel.TE_point_1[1]],
-                z=[panel.LE_point_1[2], panel.TE_point_1[2]],
-                mode="lines",
-                line=dict(
-                    color=leadinge_edge_line_color, width=leadinge_edge_line_width
-                ),
-                name="Leading Edge",
-                showlegend=False,
+def add_panel_edges(
+    fig: go.Figure,
+    panel: Any,
+    is_first: bool,
+    is_last: bool,
+    is_thin_outline: bool = False,
+) -> None:
+    """Add panel edges to the figure.
+
+    By default the leading edge is drawn as a thick black line. With
+    ``is_thin_outline`` (used by the fancy plot, where the inflatable leading-edge
+    tube already marks the leading edge) all panel edges are thin grey outlines
+    and the thick leading-edge line is omitted.
+    """
+    edge_color = "blue" if is_thin_outline else "black"
+    if is_thin_outline:
+        leadinge_edge_line_color = "blue"
+        leadinge_edge_line_width = 1.5
+    else:
+        leadinge_edge_line_color = "black"
+        leadinge_edge_line_width = 15
+        # Thick tip edges only make sense for the thick leading-edge style.
+        if is_first:
+            fig.add_trace(
+                go.Scatter3d(
+                    x=[panel.LE_point_1[0], panel.TE_point_1[0]],
+                    y=[panel.LE_point_1[1], panel.TE_point_1[1]],
+                    z=[panel.LE_point_1[2], panel.TE_point_1[2]],
+                    mode="lines",
+                    line=dict(
+                        color=leadinge_edge_line_color, width=leadinge_edge_line_width
+                    ),
+                    name="Leading Edge",
+                    showlegend=False,
+                )
             )
-        )
-    elif is_last:
-        # Include 1-side edge
-        fig.add_trace(
-            go.Scatter3d(
-                x=[panel.LE_point_2[0], panel.TE_point_2[0]],
-                y=[panel.LE_point_2[1], panel.TE_point_2[1]],
-                z=[panel.LE_point_2[2], panel.TE_point_2[2]],
-                mode="lines",
-                line=dict(
-                    color=leadinge_edge_line_color, width=leadinge_edge_line_width
-                ),
-                name="Leading Edge",
-                showlegend=False,
+        elif is_last:
+            fig.add_trace(
+                go.Scatter3d(
+                    x=[panel.LE_point_2[0], panel.TE_point_2[0]],
+                    y=[panel.LE_point_2[1], panel.TE_point_2[1]],
+                    z=[panel.LE_point_2[2], panel.TE_point_2[2]],
+                    mode="lines",
+                    line=dict(
+                        color=leadinge_edge_line_color, width=leadinge_edge_line_width
+                    ),
+                    name="Leading Edge",
+                    showlegend=False,
+                )
             )
-        )
     # Standard leading edge
     fig.add_trace(
         go.Scatter3d(
@@ -61,7 +77,7 @@ def add_panel_edges(fig: go.Figure, panel: Any, is_first: bool, is_last: bool) -
             y=[panel.TE_point_1[1], panel.TE_point_2[1]],
             z=[panel.TE_point_1[2], panel.TE_point_2[2]],
             mode="lines",
-            line=dict(color="black", width=2),
+            line=dict(color=edge_color, width=2),
             name="Trailing Edge",
             showlegend=is_first,
         )
@@ -77,7 +93,7 @@ def add_panel_edges(fig: go.Figure, panel: Any, is_first: bool, is_last: bool) -
                 y=[points[0][1], points[1][1]],
                 z=[points[0][2], points[1][2]],
                 mode="lines",
-                line=dict(color="black", width=0.8),
+                line=dict(color=edge_color, width=0.8),
                 name=(
                     "Side Edge" if is_first and i == 0 else None
                 ),  # Legend only for the first side edge
@@ -175,6 +191,7 @@ def add_aerodynamic_vectors(
     force_vector: np.ndarray,
     scale: float = 0.5,
     max_force: float = 1.0,
+    origin: np.ndarray = None,
 ):
     """
     Add aerodynamic force vectors to a given panel on the plot.
@@ -184,9 +201,12 @@ def add_aerodynamic_vectors(
         panel: Panel object
         force_of_panel: Aerodynamic force vector
         scale: Scaling factor for the force vector
+        origin: Optional vector origin. Defaults to the panel aerodynamic centre;
+            pass a point on the canopy surface to make the vectors emanate from
+            the surface when the panels are hidden.
     """
     # Compute vector endpoint
-    aerodynamic_center = panel.aerodynamic_center
+    aerodynamic_center = panel.aerodynamic_center if origin is None else origin
     vector = (force_vector / max_force) * scale * 0.5
     vector_endpoint = aerodynamic_center + vector
 
@@ -411,7 +431,7 @@ def add_bridle_system(fig: go.Figure, wing_aero: object, is_first: bool = True)
     elif hasattr(wing_aero, "_bridle_line_system") and wing_aero._bridle_line_system:
         # Handle basic bridle line system (backward compatibility)
         for i, bridle_line in enumerate(wing_aero._bridle_line_system):
-            p1, p2, _, _, diameter, _ = bridle_line
+            p1, p2, diameter = bridle_line
 
             # Calculate thickness based on diameter (simple scaling)
             thickness = max(2, min(12, diameter * 1000))  # Assume diameter in meters
@@ -435,6 +455,11 @@ def create_3D_plot(
     forces_of_panels: List[np.ndarray],
     is_with_aerodynamic_details: bool,
     is_with_bridles: bool = False,
+    tube_data: Optional[Dict[str, Any]] = None,
+    canopy_grid: Optional[Tuple[np.ndarray, np.ndarray, np.ndarray]] = None,
+    is_with_tube_rings: bool = False,
+    is_with_panels: bool = True,
+    cp_magnitude_fn=None,
 ) -> go.Figure:
     """
     Creates an interactive 3D plot of wing geometry using Plotly.
@@ -444,10 +469,26 @@ def create_3D_plot(
         forces_of_panels: List of force vectors for each panel
         is_with_aerodynamic_details: Boolean to show/hide aerodynamic visualization details
         is_with_bridles: Boolean to show/hide bridle system visualization
+        tube_data: Optional inflatable-tube geometry from
+            ``VSM.plotly.build_tube_data``; when given, the leading-edge and strut
+            tubes are drawn as surfaces.
+        canopy_grid: Optional ``(X, Y, Z)`` curved-canopy surface from
+            ``VSM.plotly.build_canopy_grid``; when given, it replaces the flat
+            panel surfaces.
+        is_with_tube_rings: Also draw the tube construction rings (for inspection).
+        is_with_panels: Draw the panel outlines. When ``False`` and a canopy is
+            present, the force vectors are lifted onto the canopy surface instead
+            of starting at the flat panel aerodynamic centres.
 
     Returns:
         plotly.graph_objects.Figure
     """
+    from VSM.plotly.tube_geometry import (
+        add_strut_surfaces,
+        add_le_tube_surface,
+        add_tube_rings,
+    )
+
     panels = wing_aero.panels
 
     chord_average = np.max([panel.chord for panel in panels])
@@ -460,6 +501,14 @@ def create_3D_plot(
     if is_with_bridles:
         add_bridle_system(fig, wing_aero, is_first=True)
 
+    draw_flat_surface = canopy_grid is None
+    # In the fancy plot the inflatable LE tube marks the leading edge, so draw the
+    # panels as thin grey outlines instead of the thick black leading-edge line.
+    is_thin_outline = tube_data is not None
+    # On the canopy the per-panel force vectors are replaced by a set of equal,
+    # surface-normal vectors distributed across the span (see below).
+    use_distributed_vectors = canopy_grid is not None
+
     # Add geometric elements
     for i, panel in enumerate(panels):
         is_first = False
@@ -472,20 +521,158 @@ def create_3D_plot(
         if is_with_aerodynamic_details:
             add_filaments(fig, panel, is_first)
 
-        add_panel_edges(fig, panel, is_first, is_last)
-        add_panel_surface(fig, panel, is_first)
-        add_aerodynamic_vectors(
+        if is_with_panels:
+            add_panel_edges(
+                fig, panel, is_first, is_last, is_thin_outline=is_thin_outline
+            )
+        if draw_flat_surface and is_with_panels:
+            add_panel_surface(fig, panel, is_first)
+
+        if not use_distributed_vectors:
+            add_aerodynamic_vectors(
+                fig,
+                panel,
+                is_first,
+                np.array(forces_of_panels[i]),
+                scale=chord_average,
+                max_force=max_force,
+            )
+
+    # Draw order (so the leading-edge tube ends up rendered on top): struts sit
+    # under the canopy, then the canopy, then the force vectors, and finally the
+    # leading-edge tube last so it is never occluded by the semi-transparent canopy.
+    if tube_data is not None:
+        add_strut_surfaces(fig, tube_data)
+
+    if canopy_grid is not None:
+        add_canopy_surface(fig, canopy_grid)
+
+    if use_distributed_vectors:
+        add_distributed_surface_vectors(
             fig,
-            panel,
-            is_first,
-            np.array(forces_of_panels[i]),
+            canopy_grid,
+            forces_of_panels,
             scale=chord_average,
-            max_force=max_force,
+            cp_magnitude_fn=cp_magnitude_fn,
         )
 
+    if tube_data is not None:
+        add_le_tube_surface(fig, tube_data)
+        if is_with_tube_rings:
+            add_tube_rings(fig, tube_data)
+
     return fig
 
 
+def add_canopy_surface(
+    fig: go.Figure, canopy_grid: Tuple[np.ndarray, np.ndarray, np.ndarray]
+) -> None:
+    """Add the curved single-skin canopy as a uniformly coloured surface."""
+    canopy_color = "lightgrey"
+    x, y, z = canopy_grid
+    fig.add_trace(
+        go.Surface(
+            x=x,
+            y=y,
+            z=z,
+            surfacecolor=np.zeros_like(z),
+            colorscale=[[0, canopy_color], [1, canopy_color]],
+            showscale=False,
+            opacity=0.85,
+            name="Canopy",
+            showlegend=True,
+            lighting=dict(ambient=0.75, diffuse=0.8, specular=0.05, roughness=0.9),
+        )
+    )
+
+
+def add_distributed_surface_vectors(
+    fig: go.Figure,
+    canopy_grid: Tuple[np.ndarray, np.ndarray, np.ndarray],
+    forces_of_panels: List[np.ndarray],
+    scale: float,
+    n_chord: int = 10,
+    n_span: int = None,
+    cp_magnitude_fn=None,
+) -> None:
+    """Draw a grid of chord-normal force vectors on the canopy.
+
+    One row of ``n_chord`` nodes is placed at each panel centre (uniformly over
+    the chord); every node carries one arrow oriented normal to the local chord
+    line (the flat-panel "up", perpendicular to chord and span), pointing to the
+    outward/suction side. This gives a regular ``n_span x n_chord`` grid of
+    vectors covering the whole canopy.
+
+    Each panel's VSM force is distributed over its chord nodes: the arrow lengths
+    reflect both the spanwise loading (the panel force from VSM) and the chordwise
+    shape (the ``|Cp|`` weighting, or an even split without a Cp distribution).
+    The node forces of a panel sum back to that panel's force.
+
+    Args:
+        fig: Plotly figure.
+        canopy_grid: ``(X, Y, Z)`` canopy surface arrays, each ``(S, P)`` with
+            ``S`` spanwise stations and ``P`` chordwise points.
+        forces_of_panels: Per-panel aerodynamic force vectors from VSM; their
+            magnitudes set the total per panel that is distributed chordwise.
+        scale: Length scale (typically the average chord).
+        n_chord: Nodes per row, distributed over the chord. Defaults to 10.
+        n_span: Number of spanwise rows. Defaults to one row per panel (the
+            midpoints between the ``S`` canopy stations, i.e. ``S - 1`` rows).
+        cp_magnitude_fn: Optional callable mapping chord fraction ``x/c`` to a
+            (positive) pressure-coefficient magnitude, used as the chordwise
+            weighting of each panel's force. Without it the force is spread evenly.
+    """
+    from VSM.plotly.canopy_geometry import distributed_surface_vector_field
+
+    origins, normals, lengths_1d = distributed_surface_vector_field(
+        canopy_grid,
+        forces_of_panels,
+        scale,
+        n_chord=n_chord,
+        n_span=n_span,
+        cp_magnitude_fn=cp_magnitude_fn,
+    )
+    if len(origins) == 0:
+        return
+    lengths = lengths_1d[:, None]
+    endpoints = origins + normals * lengths
+
+    # All stems as one trace (None-separated segments), all heads as one cone trace.
+    stem_x, stem_y, stem_z = [], [], []
+    for start, end in zip(origins, endpoints):
+        stem_x += [start[0], end[0], None]
+        stem_y += [start[1], end[1], None]
+        stem_z += [start[2], end[2], None]
+    fig.add_trace(
+        go.Scatter3d(
+            x=stem_x,
+            y=stem_y,
+            z=stem_z,
+            mode="lines",
+            line=dict(color="red", width=3.9),
+            name="Aerodynamic Vectors",
+            showlegend=True,
+        )
+    )
+    fig.add_trace(
+        go.Cone(
+            x=endpoints[:, 0],
+            y=endpoints[:, 1],
+            z=endpoints[:, 2],
+            u=normals[:, 0] * lengths[:, 0],
+            v=normals[:, 1] * lengths[:, 0],
+            w=normals[:, 2] * lengths[:, 0],
+            sizemode="absolute",
+            sizeref=0.048 * scale,
+            anchor="tip",
+            colorscale=[[0, "red"], [1, "red"]],
+            showscale=False,
+            name="Arrowheads",
+            showlegend=False,
+        )
+    )
+
+
 def compute_kite_geometry_ranges(panels: List[Any]) -> Tuple[Dict[str, float], float]:
     """Calculate axis ranges and tick spacing."""
     all_points = []
@@ -507,14 +694,18 @@ def compute_axis_parameters_from_fig(
     fig: go.Figure,
 ) -> Tuple[Dict[str, float], float]:
     """Calculate axis ranges and tick spacing based on the figure's data."""
-    # Extract all data points from the figure traces
+    # Extract all data points from the figure traces, skipping gap separators
+    # (``None``/NaN) that traces may use to draw multiple segments at once.
     all_points = []
     for trace in fig.data:
         if isinstance(trace, go.Scatter3d):
-            all_points.extend(zip(trace.x, trace.y, trace.z))
+            for px, py, pz in zip(trace.x, trace.y, trace.z):
+                if px is None or py is None or pz is None:
+                    continue
+                all_points.append((px, py, pz))
 
     # Convert to numpy array for calculations
-    all_points = np.array(all_points)
+    all_points = np.array(all_points, dtype=float)
 
     # Calculate ranges for each axis
     ranges = {
@@ -598,12 +789,15 @@ def update_fig_layout(
                     f"{kite_geometry_ranges['z'][1]:.2f}",
                 ],  # Label for the ticks
             ),
-            aspectmode="data",  # Allow different axis lengths
-            aspectratio=dict(x=1, y=1, z=1),  # Keep aspect ratio 1:1:1
+            # "data" keeps the scene true-to-scale (equal units on every axis);
+            # do NOT force a cube aspectratio here, that squeezes the wide wing.
+            aspectmode="data",
             camera=dict(
                 up=dict(x=0, y=0, z=1),
                 center=dict(x=0, y=0, z=0),
-                eye=dict(x=1.5, y=1.5, z=1.5),
+                eye=dict(
+                    x=-1.92, y=-1.44, z=0.30
+                ),  # rotated ~180 deg, low and further out
             ),
             bgcolor="white",
         ),
@@ -754,6 +948,11 @@ def update_plot(
     is_with_aerodynamic_details: bool,
     is_with_bridles: bool,
     title: str,
+    tube_data: Optional[Dict[str, Any]] = None,
+    canopy_grid: Optional[Tuple[np.ndarray, np.ndarray, np.ndarray]] = None,
+    is_with_tube_rings: bool = False,
+    is_with_panels: bool = True,
+    cp_magnitude_fn=None,
 ):
     # Update AoA and rerun VSM
     results = running_VSM(
@@ -773,6 +972,11 @@ def update_plot(
         results["F_distribution"],
         is_with_aerodynamic_details,
         is_with_bridles,
+        tube_data=tube_data,
+        canopy_grid=canopy_grid,
+        is_with_tube_rings=is_with_tube_rings,
+        is_with_panels=is_with_panels,
+        cp_magnitude_fn=cp_magnitude_fn,
     )
     fig = add_case_information(
         fig,
@@ -804,6 +1008,11 @@ def interactive_plot(
     is_save: bool = False,
     filename="wing_geometry",
     is_show: bool = True,
+    surfplan_dir: Optional[Path] = None,
+    is_with_canopy: bool = True,
+    is_with_tube_rings: bool = False,
+    is_with_panels: Optional[bool] = None,
+    cp_distributions_dir: Optional[Path] = None,
 ):
     """
     Creates and optionally saves multiple views of the wing geometry with interactive AoA slider.
@@ -823,7 +1032,57 @@ def interactive_plot(
         is_save: Whether to save the plot
         filename: Base filename for saved files
         is_show: Whether to display the plot
+        surfplan_dir: Optional path to a raw Surfplan export (a ``<name>.txt`` plus
+            a ``profiles/`` directory). When given, the "fancy" plot is drawn: the
+            export is converted with SurfplanAdapter (cached) and the inflatable
+            leading-edge and strut tubes -- and, by default, the curved single-skin
+            canopy -- are rendered on the wing. Leaving it ``None`` keeps the plain
+            plot unchanged.
+        is_with_canopy: When a Surfplan export is given, draw the curved canopy
+            (lofted airfoil top-surfaces) in place of the flat panel surfaces.
+        is_with_tube_rings: Also draw the tube construction rings (for inspection).
+        is_with_panels: Draw the panel outlines. Defaults to ``False`` for the
+            fancy plot (a Surfplan export is given) and ``True`` otherwise. When
+            off, the force vectors are lifted onto the canopy surface.
+        cp_distributions_dir: Optional directory of ``cp_AOA_<deg>.dat`` chordwise
+            pressure-coefficient files. When given, the closest available angle of
+            attack is selected and its ``|Cp|(x/c)`` scales the chordwise force
+            vector magnitudes (the single mid-span distribution is applied at
+            every span station).
+
+    Returns:
+        plotly.graph_objects.Figure: The created figure.
     """
+    # Default: hide the panel outlines for the fancy plot, show them otherwise.
+    if is_with_panels is None:
+        is_with_panels = surfplan_dir is None
+
+    # Build the optional fancy-plot geometry once (reused on every slider update).
+    tube_data = None
+    canopy_grid = None
+    if surfplan_dir is not None:
+        from VSM.plotly.surfplan_runner import ensure_surfplan_processed
+        from VSM.plotly.tube_geometry import build_tube_data
+        from VSM.plotly.canopy_geometry import load_contour_table, build_canopy_grid
+
+        processed_dir = ensure_surfplan_processed(Path(surfplan_dir))
+        tube_data = build_tube_data(wing_aero.panels, processed_dir)
+        if is_with_canopy:
+            contour_table = load_contour_table(processed_dir)
+            canopy_grid = build_canopy_grid(wing_aero.panels, contour_table)
+
+    # Optional Cp-based scaling of the chordwise force vectors.
+    cp_magnitude_fn = None
+    if cp_distributions_dir is not None:
+        from VSM.plotly.cp_distributions import build_cp_magnitude_fn
+
+        cp_magnitude_fn, matched_aoa = build_cp_magnitude_fn(
+            Path(cp_distributions_dir), angle_of_attack
+        )
+        print(
+            f"Cp loading from closest available AoA = {matched_aoa:g} deg "
+            f"(operating AoA = {angle_of_attack:g} deg)"
+        )
 
     # Create the figure with a default orientation
     fig = go.Figure()
@@ -848,6 +1107,11 @@ def interactive_plot(
         is_with_aerodynamic_details,
         is_with_bridles,
         title,
+        tube_data=tube_data,
+        canopy_grid=canopy_grid,
+        is_with_tube_rings=is_with_tube_rings,
+        is_with_panels=is_with_panels,
+        cp_magnitude_fn=cp_magnitude_fn,
     )
 
     # Save or show the plot if requested
@@ -859,4 +1123,95 @@ def interactive_plot(
         fig.write_html(save_path)
 
     if is_show:
-        fig.show()
+        import tempfile
+        import webbrowser
+
+        with tempfile.NamedTemporaryFile(
+            suffix=".html", delete=False, mode="w", encoding="utf-8"
+        ) as f:
+            tmp_path = f.name
+            fig.write_html(f, include_plotlyjs=True, full_html=True)
+        print(f"Interactive plot saved to: {tmp_path}")
+        webbrowser.open(f"file://{tmp_path}")
+
+    return fig
+
+
+def save_high_res_render(
+    fig: go.Figure,
+    save_path,
+    base_size: int = 2000,
+    scale: int = 4,
+    show_legend: bool = False,
+    show_axes: bool = False,
+    show_annotations: bool = False,
+    transparent_background: bool = False,
+    pad_frac: float = 0.02,
+) -> str:
+    """Export a clean, to-scale high-resolution PNG of the kite.
+
+    A Plotly 3D scene is only rendered undistorted on a **square** canvas (on a
+    non-square canvas Plotly stretches it, which squeezes the kite). So this
+    renders on a square canvas -- guaranteeing correct proportions -- then crops
+    tightly to the drawn content. The legend, axes and annotations are removed by
+    default for a clean figure.
+
+    Args:
+        fig: The Plotly figure (e.g. from :func:`interactive_plot`).
+        save_path: Output ``.png`` path; parent directories are created.
+        base_size: Square canvas size in pixels before supersampling.
+        scale: Supersampling factor (final render is ``base_size * scale`` square,
+            then cropped). Defaults to 4.
+        show_legend: Keep the trace legend. Defaults to False.
+        show_axes: Keep the 3D axes. Defaults to False.
+        show_annotations: Keep the corner case-info text. Defaults to False.
+        transparent_background: Transparent instead of white background.
+        pad_frac: Padding around the cropped content, as a fraction of its size.
+
+    Returns:
+        str: The path the PNG was written to.
+    """
+    import io
+    from PIL import Image
+
+    render_fig = go.Figure(fig)
+    render_fig.update_layout(showlegend=show_legend, margin=dict(l=0, r=0, t=0, b=0))
+    if not show_annotations:
+        render_fig.layout.annotations = ()
+        render_fig.layout.title = None
+    if not show_axes:
+        render_fig.update_scenes(
+            xaxis_visible=False, yaxis_visible=False, zaxis_visible=False
+        )
+    bg = "rgba(0,0,0,0)" if transparent_background else "white"
+    render_fig.update_layout(paper_bgcolor=bg, plot_bgcolor=bg)
+
+    # Square canvas => Plotly does not distort the 3D scene.
+    png = render_fig.to_image(
+        format="png", width=base_size, height=base_size, scale=scale
+    )
+    image = Image.open(io.BytesIO(png)).convert("RGBA")
+
+    # Crop tightly to the drawn content (non-background pixels).
+    rgb = np.asarray(image)[:, :, :3].astype(int)
+    alpha = np.asarray(image)[:, :, 3]
+    if transparent_background:
+        content = alpha > 0
+    else:
+        content = rgb.sum(axis=2) < 3 * 250  # anything not near-white
+    ys, xs = np.where(content)
+    if len(xs):
+        pad = int(pad_frac * max(xs.max() - xs.min(), ys.max() - ys.min()))
+        left = max(xs.min() - pad, 0)
+        right = min(xs.max() + pad, image.width)
+        top = max(ys.min() - pad, 0)
+        bottom = min(ys.max() + pad, image.height)
+        image = image.crop((left, top, right, bottom))
+
+    save_path = Path(save_path)
+    save_path.parent.mkdir(parents=True, exist_ok=True)
+    image.save(str(save_path))
+    print(
+        f"High-resolution render saved to: {save_path} ({image.width} x {image.height} px)"
+    )
+    return str(save_path)
diff --git a/src/VSM/plotly/__init__.py b/src/VSM/plotly/__init__.py
new file mode 100644
index 0000000..a204699
--- /dev/null
+++ b/src/VSM/plotly/__init__.py
@@ -0,0 +1,24 @@
+"""Helpers for the interactive Plotly wing plot.
+
+The public ``interactive_plot`` entry point lives in ``VSM.plot_geometry_plotly``
+(unchanged location for backward compatibility). This subpackage holds the
+supporting machinery for the opt-in "fancy" plot that renders the inflatable
+tubes and the curved single-skin canopy of a LEI kite from a raw Surfplan export.
+"""
+
+from VSM.plotly.surfplan_runner import ensure_surfplan_processed
+from VSM.plotly.tube_geometry import (
+    add_tube_rings,
+    add_tube_surfaces,
+    build_tube_data,
+)
+from VSM.plotly.canopy_geometry import build_canopy_grid, load_contour_table
+
+__all__ = [
+    "ensure_surfplan_processed",
+    "build_tube_data",
+    "add_tube_surfaces",
+    "add_tube_rings",
+    "build_canopy_grid",
+    "load_contour_table",
+]
diff --git a/src/VSM/plotly/canopy_geometry.py b/src/VSM/plotly/canopy_geometry.py
new file mode 100644
index 0000000..419c6bc
--- /dev/null
+++ b/src/VSM/plotly/canopy_geometry.py
@@ -0,0 +1,332 @@
+"""Curved single-skin canopy geometry for the interactive Plotly wing plot.
+
+The canopy is rendered as a loft of the real airfoil top-surfaces (the ``.dat``
+profiles produced by SurfplanAdapter) placed on the *drawn* wing panels. Only the
+upper contour is used (LEI kites are single-skin), so the inflatable tubes stay
+visible from below. See ``VSM.plotly.tube_geometry`` for the tubes and
+``VSM.plotly.surfplan_runner`` for how the source files are generated.
+
+All geometry is anchored on the drawn ``Panel`` objects (their LE/TE points and
+local frame), so the result is independent of the coordinate frame of the source
+Surfplan export.
+"""
+
+from pathlib import Path
+from typing import Any, List, Tuple
+
+import numpy as np
+import yaml
+
+
+def read_upper_contour(dat_file_path: Path) -> np.ndarray:
+    """Read an airfoil ``.dat`` file and return its upper contour, LE-to-TE.
+
+    The ``.dat`` files use the standard ordering starting at the trailing edge
+    ``(1, 0)``, running forward over the upper surface to the leading edge
+    ``(0, 0)``, then back over the lower surface to the trailing edge. The upper
+    surface is the run up to the minimum-x (leading edge) point.
+
+    Args:
+        dat_file_path (Path): Path to the airfoil coordinate file. The first line
+            is a name label and is skipped; remaining lines are ``x y`` pairs
+            normalised by the chord.
+
+    Returns:
+        np.ndarray: Upper contour as an ``(n, 2)`` array ordered from the leading
+            edge ``(0, 0)`` to the trailing edge ``(1, 0)``.
+    """
+    points = []
+    for line in Path(dat_file_path).read_text().splitlines():
+        parts = line.split()
+        if len(parts) != 2:
+            continue
+        try:
+            points.append((float(parts[0]), float(parts[1])))
+        except ValueError:
+            continue
+    points = np.asarray(points, dtype=float)
+    le_index = int(np.argmin(points[:, 0]))
+    upper = points[: le_index + 1]  # TE -> LE
+    return upper[::-1]  # LE -> TE
+
+
+def resample_contour(contour: np.ndarray, n_points: int = 40) -> np.ndarray:
+    """Resample a contour to a fixed point count by arc length.
+
+    Uses cumulative chord-length parameterisation so that the leading- and
+    trailing-edge endpoints are preserved. This lets contours from different
+    airfoils be blended point-by-point.
+
+    Args:
+        contour (np.ndarray): ``(n, 2)`` contour ordered LE-to-TE.
+        n_points (int): Number of output points. Defaults to 40.
+
+    Returns:
+        np.ndarray: ``(n_points, 2)`` resampled contour.
+    """
+    segment_lengths = np.linalg.norm(np.diff(contour, axis=0), axis=1)
+    arc_length = np.concatenate([[0.0], np.cumsum(segment_lengths)])
+    total = arc_length[-1]
+    if total <= 0:
+        return np.repeat(contour[:1], n_points, axis=0)
+    sample = np.linspace(0.0, total, n_points)
+    x = np.interp(sample, arc_length, contour[:, 0])
+    y = np.interp(sample, arc_length, contour[:, 1])
+    return np.column_stack([x, y])
+
+
+def load_contour_table(
+    processed_dir: Path, n_points: int = 40
+) -> List[Tuple[float, np.ndarray]]:
+    """Build a span-fraction -> upper-contour table from the generated aero yaml.
+
+    Reads ``aero_geometry.yaml`` written by SurfplanAdapter: each wing section
+    provides its span position (``LE_y``) and an ``airfoil_id`` whose
+    ``info_dict`` carries the ``dat_file_path``. Span positions are normalised to
+    fractions of the half-span so the table can be sampled against any drawn wing
+    regardless of scale or frame.
+
+    Args:
+        processed_dir (Path): Directory holding ``aero_geometry.yaml`` and the
+            ``profiles/`` referenced by relative ``dat_file_path`` entries.
+        n_points (int): Point count each contour is resampled to. Defaults to 40.
+
+    Returns:
+        List[Tuple[float, np.ndarray]]: ``(span_fraction, contour)`` pairs sorted
+            by ascending span fraction, each contour an ``(n_points, 2)`` array.
+    """
+    processed_dir = Path(processed_dir)
+    aero = yaml.safe_load((processed_dir / "aero_geometry.yaml").read_text())
+    sections = aero["wing_sections"]
+    airfoils = aero["wing_airfoils"]
+
+    section_headers = sections["headers"]
+    idx_id = section_headers.index("airfoil_id")
+    idx_le_y = section_headers.index("LE_y")
+
+    airfoil_headers = airfoils["headers"]
+    a_idx_id = airfoil_headers.index("airfoil_id")
+    a_idx_info = airfoil_headers.index("info_dict")
+    dat_by_id = {
+        row[a_idx_id]: row[a_idx_info]["dat_file_path"] for row in airfoils["data"]
+    }
+
+    contour_cache = {}
+    span_y = np.array([abs(row[idx_le_y]) for row in sections["data"]])
+    half_span = span_y.max() if span_y.max() > 0 else 1.0
+
+    table = []
+    for row in sections["data"]:
+        airfoil_id = row[idx_id]
+        if airfoil_id not in contour_cache:
+            dat_path = processed_dir / dat_by_id[airfoil_id]
+            contour_cache[airfoil_id] = resample_contour(
+                read_upper_contour(dat_path), n_points
+            )
+        fraction = abs(row[idx_le_y]) / half_span
+        table.append((fraction, contour_cache[airfoil_id]))
+
+    table.sort(key=lambda item: item[0])
+    return table
+
+
+def interpolate_contour_at(
+    span_fraction: float, contour_table: List[Tuple[float, np.ndarray]]
+) -> np.ndarray:
+    """Linearly blend the two contours bracketing a span fraction.
+
+    Args:
+        span_fraction (float): Target fraction of the half-span, in ``[0, 1]``.
+        contour_table (List[Tuple[float, np.ndarray]]): Output of
+            :func:`load_contour_table`; contours share a common point count.
+
+    Returns:
+        np.ndarray: Interpolated contour, clamped to the end contours outside the
+            table's fraction range.
+    """
+    fractions = [f for f, _ in contour_table]
+    if span_fraction <= fractions[0]:
+        return contour_table[0][1]
+    if span_fraction >= fractions[-1]:
+        return contour_table[-1][1]
+    upper = int(np.searchsorted(fractions, span_fraction))
+    lower = upper - 1
+    f_lo, c_lo = contour_table[lower]
+    f_hi, c_hi = contour_table[upper]
+    weight = (span_fraction - f_lo) / (f_hi - f_lo) if f_hi > f_lo else 0.0
+    return (1.0 - weight) * c_lo + weight * c_hi
+
+
+def _edge_stations(panels: List[Any]) -> List[dict]:
+    """Collect the ``n_panels + 1`` spanwise edge stations of the drawn wing.
+
+    Each station carries its leading- and trailing-edge points, the chord vector
+    and length, and the local up-vector (averaged across adjacent panels), all
+    taken from the ``Panel`` objects so the canopy sits exactly on the drawn wing.
+    """
+    stations = []
+
+    def make_station(le_point, te_point, up_vectors):
+        chord_vector = np.asarray(te_point) - np.asarray(le_point)
+        chord_length = float(np.linalg.norm(chord_vector))
+        chord_hat = chord_vector / max(chord_length, 1e-12)
+        up = np.mean(up_vectors, axis=0)
+        up = up / max(np.linalg.norm(up), 1e-12)
+        return {
+            "le_point": np.asarray(le_point, dtype=float),
+            "te_point": np.asarray(te_point, dtype=float),
+            "chord_length": chord_length,
+            "chord_hat": chord_hat,
+            "up": up,
+        }
+
+    stations.append(
+        make_station(panels[0].LE_point_1, panels[0].TE_point_1, [panels[0].x_airf])
+    )
+    for index, panel in enumerate(panels):
+        neighbours = [panel.x_airf]
+        if index + 1 < len(panels):
+            neighbours.append(panels[index + 1].x_airf)
+        stations.append(make_station(panel.LE_point_2, panel.TE_point_2, neighbours))
+    return stations
+
+
+def build_canopy_grid(
+    panels: List[Any], contour_table: List[Tuple[float, np.ndarray]]
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Build the lofted canopy surface grid over the drawn wing panels.
+
+    For every panel-edge station the local upper contour (blended from the rib
+    profiles at that span fraction) is placed on the drawn chord: the contour's
+    chordwise coordinate runs along the station chord and its height along the
+    local up-vector, both scaled by the station chord length.
+
+    Args:
+        panels (List[Any]): Drawn ``Panel`` objects, ordered tip to tip.
+        contour_table (List[Tuple[float, np.ndarray]]): Output of
+            :func:`load_contour_table`.
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray, np.ndarray]: ``X``, ``Y``, ``Z`` arrays of
+            shape ``(n_panels + 1, n_points)`` suitable for a Plotly surface.
+    """
+    stations = _edge_stations(panels)
+    all_y = np.array([abs(station["le_point"][1]) for station in stations])
+    half_span = all_y.max() if all_y.max() > 0 else 1.0
+
+    rows = []
+    for station in stations:
+        fraction = abs(station["le_point"][1]) / half_span
+        contour = interpolate_contour_at(fraction, contour_table)
+        chord_length = station["chord_length"]
+        points = (
+            station["le_point"][None, :]
+            + contour[:, 0:1] * chord_length * station["chord_hat"][None, :]
+            + contour[:, 1:2] * chord_length * station["up"][None, :]
+        )
+        rows.append(points)
+
+    grid = np.stack(rows, axis=0)  # (n_stations, n_points, 3)
+    return grid[:, :, 0], grid[:, :, 1], grid[:, :, 2]
+
+
+def distributed_surface_vector_field(
+    canopy_grid: Tuple[np.ndarray, np.ndarray, np.ndarray],
+    forces_of_panels: List[np.ndarray],
+    scale: float,
+    n_chord: int = 10,
+    n_span: int = None,
+    cp_magnitude_fn=None,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Chord-normal force-vector field over the canopy (origins, directions, lengths).
+
+    One row of ``n_chord`` nodes is placed at each panel centre, uniformly over
+    the chord (at the true 5%-95% chord fractions). Every node's arrow points
+    normal to the local chord line (the flat-panel "up", outward/suction side).
+    Each panel's VSM force is distributed over its chord nodes, weighted by the
+    ``|Cp|`` chordwise loading (an even split without a Cp distribution) and
+    normalised to length so the largest panel force plots at the maximum chord
+    length (``scale``).
+
+    Args:
+        canopy_grid: ``(X, Y, Z)`` canopy arrays, each ``(S, P)``.
+        forces_of_panels: Per-panel VSM force vectors.
+        scale: Length scale (typically the maximum chord); the largest panel
+            force plots at this length.
+        n_chord: Nodes per row over the chord. Defaults to 10.
+        n_span: Number of spanwise rows. Defaults to one per panel (``S - 1``).
+        cp_magnitude_fn: Optional ``x/c -> |Cp|`` weighting callable.
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray, np.ndarray]: ``origins`` ``(N, 3)``,
+            unit ``directions`` ``(N, 3)`` and ``lengths`` ``(N,)``. Empty arrays
+            if there is no force.
+    """
+    x, y, z = canopy_grid
+    grid = np.stack([x, y, z], axis=-1)  # (S, P, 3)
+
+    force_magnitudes = np.linalg.norm(np.asarray(forces_of_panels), axis=1)
+    if force_magnitudes.size == 0 or np.max(force_magnitudes) <= 0:
+        return np.empty((0, 3)), np.empty((0, 3)), np.empty((0,))
+
+    panel_grid = 0.5 * (grid[:-1] + grid[1:])  # (S - 1, P, 3), one row per panel
+    n_rows, n_points, _ = panel_grid.shape
+
+    if n_span is None:
+        n_span = n_rows
+    span_rows = np.unique(
+        np.clip(np.round(np.linspace(0, n_rows - 1, n_span)), 0, n_rows - 1).astype(int)
+    )
+    d_span = np.gradient(panel_grid, axis=0)  # (R, P, 3)
+
+    targets = np.linspace(0.05, 0.95, n_chord)
+    if cp_magnitude_fn is not None:
+        chord_weights = np.asarray(cp_magnitude_fn(targets), dtype=float)
+    else:
+        chord_weights = np.ones(n_chord)
+    chord_weights = chord_weights / max(chord_weights.sum(), 1e-12)
+
+    origins = []
+    directions = []
+    node_forces = []
+    for row in span_rows:
+        pts = panel_grid[row]
+        chord_vector = pts[-1] - pts[0]
+        chord_length = np.linalg.norm(chord_vector)
+        chord_hat = chord_vector / max(chord_length, 1e-12)
+
+        span_vector = d_span[row].mean(axis=0)
+        chord_normal = np.cross(chord_vector, span_vector)
+        cn = np.linalg.norm(chord_normal)
+        if cn < 1e-9:
+            continue
+        chord_normal = chord_normal / cn
+        if chord_normal[2] < 0:  # outward (suction) side
+            chord_normal = -chord_normal
+
+        panel_force = force_magnitudes[row] if row < len(force_magnitudes) else 0.0
+
+        chord_fraction = ((pts - pts[0]) @ chord_hat) / max(chord_length, 1e-12)
+        order = np.argsort(chord_fraction)
+        frac_sorted = chord_fraction[order]
+        pts_sorted = pts[order]
+        for k, target in enumerate(targets):
+            point = np.array(
+                [np.interp(target, frac_sorted, pts_sorted[:, j]) for j in range(3)]
+            )
+            origins.append(point)
+            directions.append(chord_normal)
+            node_forces.append(panel_force * chord_weights[k])
+
+    if not origins:
+        return np.empty((0, 3)), np.empty((0, 3)), np.empty((0,))
+
+    origins = np.array(origins)
+    directions = np.array(directions)
+    node_forces = np.array(node_forces)
+    newton_to_metre = scale / max(float(force_magnitudes.max()), 1e-12)
+    # Anchor the per-vector size to a base of 10 chordwise nodes, so adding more
+    # nodes makes the field denser without shrinking each arrow (at n_chord=10 the
+    # factor is 1 and the largest panel's arrows still sum to the max chord).
+    lengths = node_forces * newton_to_metre * (n_chord / 10.0)
+    return origins, directions, lengths
diff --git a/src/VSM/plotly/cp_distributions.py b/src/VSM/plotly/cp_distributions.py
new file mode 100644
index 0000000..29c1ad0
--- /dev/null
+++ b/src/VSM/plotly/cp_distributions.py
@@ -0,0 +1,97 @@
+"""Chordwise pressure-coefficient (Cp) database for the fancy Plotly plot.
+
+A directory of ``cp_AOA_<deg>.dat`` files (whitespace columns ``x/c, y/c, Cp``
+around the 2D airfoil section) is treated as a small database keyed by angle of
+attack. Given an operating angle of attack, the closest available distribution is
+selected and its **upper (suction) surface** ``|Cp|(x/c)`` is used to scale the
+magnitude of the chordwise force vectors on the canopy.
+
+Per the modelling assumption in use, the single mid-span section distribution is
+applied to every spanwise station (all airfoils share this geometry).
+"""
+
+import re
+from pathlib import Path
+from typing import Callable, Dict, Tuple
+
+import numpy as np
+
+
+def load_upper_surface_cp(dat_file_path: Path) -> Tuple[np.ndarray, np.ndarray]:
+    """Extract the upper (suction) surface ``Cp(x/c)`` from a Cp ``.dat`` loop.
+
+    The file lists ``x/c, y/c, Cp`` around the closed airfoil section. The upper
+    surface is the leading-edge-to-trailing-edge branch with the more negative
+    (suction) mean Cp; it is returned sorted by ``x/c``.
+
+    Args:
+        dat_file_path (Path): Path to a ``cp_AOA_*.dat`` file.
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: ``x/c`` (ascending) and ``Cp`` arrays.
+    """
+    data = np.loadtxt(dat_file_path)
+    x = data[:, 0]
+    cp = data[:, 2]
+    n = len(x)
+    i_le = int(np.argmin(x))
+    i_te = int(np.argmax(x))
+
+    def branch(a: int, b: int) -> np.ndarray:
+        if a <= b:
+            return np.arange(a, b + 1)
+        return np.concatenate([np.arange(a, n), np.arange(0, b + 1)])
+
+    branch_a = branch(i_le, i_te)
+    branch_b = branch(i_te, i_le)
+    upper = branch_a if cp[branch_a].mean() < cp[branch_b].mean() else branch_b
+
+    x_upper = x[upper]
+    cp_upper = cp[upper]
+    order = np.argsort(x_upper)
+    return x_upper[order], cp_upper[order]
+
+
+def load_cp_database(cp_dir: Path) -> Dict[float, Tuple[np.ndarray, np.ndarray]]:
+    """Load all ``cp_AOA_<deg>.dat`` files, keyed by angle of attack in degrees.
+
+    Args:
+        cp_dir (Path): Directory of ``cp_AOA_*.dat`` files.
+
+    Returns:
+        Dict[float, Tuple[np.ndarray, np.ndarray]]: Mapping ``aoa_deg -> (x/c, Cp)``
+            for the upper surface.
+    """
+    database = {}
+    for path in sorted(Path(cp_dir).glob("cp_AOA_*.dat")):
+        match = re.search(r"AOA_(-?\d+(?:\.\d+)?)", path.stem)
+        if not match:
+            continue
+        database[float(match.group(1))] = load_upper_surface_cp(path)
+    if not database:
+        raise FileNotFoundError(f"No 'cp_AOA_*.dat' files found in {cp_dir}.")
+    return database
+
+
+def build_cp_magnitude_fn(
+    cp_dir: Path, angle_of_attack: float
+) -> Tuple[Callable[[np.ndarray], np.ndarray], float]:
+    """Return a ``|Cp|(x/c)`` interpolator for the closest available AoA.
+
+    Args:
+        cp_dir (Path): Directory of ``cp_AOA_*.dat`` files.
+        angle_of_attack (float): Operating angle of attack in degrees.
+
+    Returns:
+        Tuple[Callable, float]: A function mapping ``x/c`` (array) to ``|Cp|`` and
+            the matched database angle of attack.
+    """
+    database = load_cp_database(cp_dir)
+    available = np.array(sorted(database))
+    matched_aoa = float(available[np.argmin(np.abs(available - angle_of_attack))])
+    x_upper, cp_upper = database[matched_aoa]
+
+    def magnitude(x_over_c: np.ndarray) -> np.ndarray:
+        return np.abs(np.interp(x_over_c, x_upper, cp_upper))
+
+    return magnitude, matched_aoa
diff --git a/src/VSM/plotly/surfplan_runner.py b/src/VSM/plotly/surfplan_runner.py
new file mode 100644
index 0000000..f7680fb
--- /dev/null
+++ b/src/VSM/plotly/surfplan_runner.py
@@ -0,0 +1,95 @@
+"""Run SurfplanAdapter to produce the geometry files the fancy plot needs.
+
+The interactive "fancy" plot needs physical tube diameters and airfoil profiles
+that are not present in a plain VSM aero-geometry yaml. They live in a raw
+Surfplan export (a ``.txt`` plus a ``profiles/`` directory). This module drives
+SurfplanAdapter to convert that export into VSM-readable yaml files, caching the
+result so the conversion only runs when the export changes.
+"""
+
+from pathlib import Path
+from typing import Optional
+
+
+def _find_surfplan_txt(surfplan_dir: Path) -> Path:
+    """Return the single ``.txt`` export in ``surfplan_dir`` (error if not one)."""
+    txt_files = sorted(surfplan_dir.glob("*.txt"))
+    if not txt_files:
+        raise FileNotFoundError(
+            f"No Surfplan '.txt' export found in {surfplan_dir}. Expected a raw "
+            "Surfplan export (a '<name>.txt' plus a 'profiles/' directory)."
+        )
+    if len(txt_files) > 1:
+        raise ValueError(
+            f"Multiple '.txt' files found in {surfplan_dir}: "
+            f"{[f.name for f in txt_files]}. Keep only the Surfplan export."
+        )
+    return txt_files[0]
+
+
+def ensure_surfplan_processed(
+    surfplan_dir: Path, output_dir: Optional[Path] = None
+) -> Path:
+    """Convert a raw Surfplan export into VSM geometry yamls, with caching.
+
+    Args:
+        surfplan_dir (Path): Directory containing the Surfplan ``.txt`` export and
+            a ``profiles/`` subdirectory of airfoil ``.dat`` files.
+        output_dir (Optional[Path]): Where to write the generated files. Defaults
+            to ``surfplan_dir / "vsm_processed"``.
+
+    Returns:
+        Path: The directory holding the generated ``aero_geometry.yaml``,
+            ``struc_geometry_all_in_surfplan.yaml`` and ``profiles/``.
+
+    Raises:
+        ImportError: If SurfplanAdapter is not installed.
+        FileNotFoundError: If the export ``.txt`` or ``profiles/`` is missing.
+    """
+    surfplan_dir = Path(surfplan_dir)
+    if output_dir is None:
+        output_dir = surfplan_dir / "vsm_processed"
+    output_dir = Path(output_dir)
+
+    txt_path = _find_surfplan_txt(surfplan_dir)
+    profile_load_dir = surfplan_dir / "profiles"
+    if not profile_load_dir.is_dir():
+        raise FileNotFoundError(
+            f"No 'profiles/' directory in {surfplan_dir}; the fancy plot needs the "
+            "airfoil '.dat' files from the Surfplan export."
+        )
+
+    aero_yaml = output_dir / "aero_geometry.yaml"
+    struc_yaml = output_dir / "struc_geometry_all_in_surfplan.yaml"
+    if (
+        aero_yaml.exists()
+        and struc_yaml.exists()
+        and aero_yaml.stat().st_mtime >= txt_path.stat().st_mtime
+        and struc_yaml.stat().st_mtime >= txt_path.stat().st_mtime
+    ):
+        return output_dir
+
+    try:
+        from SurfplanAdapter.process_wing import main_process_wing
+        from SurfplanAdapter.process_bridle_lines import main_process_bridle_lines
+        from SurfplanAdapter.generate_yaml import main_generate_yaml
+    except ImportError as error:
+        raise ImportError(
+            "The fancy plot requires SurfplanAdapter to convert the raw Surfplan "
+            "export. Install it with 'pip install SurfplanAdapter'."
+        ) from error
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    ribs_data = main_process_wing.main(
+        surfplan_txt_file_path=txt_path,
+        profile_load_dir=profile_load_dir,
+        profile_save_dir=output_dir / "profiles",
+        is_make_plots=False,
+    )
+    bridle_lines = main_process_bridle_lines.main(txt_path)
+    main_generate_yaml.main(
+        ribs_data=ribs_data,
+        bridle_lines=bridle_lines,
+        yaml_file_path=output_dir / "config_kite.yaml",
+    )
+    return output_dir
diff --git a/src/VSM/plotly/tube_geometry.py b/src/VSM/plotly/tube_geometry.py
new file mode 100644
index 0000000..0609302
--- /dev/null
+++ b/src/VSM/plotly/tube_geometry.py
@@ -0,0 +1,557 @@
+"""Inflatable-tube geometry for the interactive Plotly wing plot.
+
+Builds lofted 3D surfaces for the leading-edge tube and the chordwise strut tubes
+of a LEI kite, using the physical tube-diameter tables produced by SurfplanAdapter
+(``leading_edge_tubes`` and ``strut_tubes`` in
+``struc_geometry_all_in_surfplan.yaml``) together with the airfoil top-surface
+contours (see ``VSM.plotly.canopy_geometry``).
+
+Diameters are physical (metres) and sourced from the generated tables; positions
+are anchored on the *drawn* ``Panel`` objects, so tubes always sit on the wing
+that is actually plotted, independent of the Surfplan export's coordinate frame:
+
+* Leading-edge tube: centred just aft of the drawn LE polyline, diameter
+  interpolated from the ``leading_edge_tubes`` table by span fraction (this
+  captures the tapering tip, which ``t * chord`` does not).
+* Struts: placed at the ``is_strut`` rib span fractions, shaped to follow the
+  local airfoil top-surface and offset downward so the tube top touches the
+  canopy underside. Per-strut LE/TE diameters come from the ``strut_tubes`` table
+  paired tip-to-tip with the strut ribs (both are symmetric and span-ordered, so
+  the pairing does not rely on the table's node indices).
+"""
+
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Tuple
+
+import numpy as np
+import plotly.graph_objects as go
+import yaml
+
+from VSM.plotly.canopy_geometry import interpolate_contour_at, load_contour_table
+
+TUBE_COLOR = "black"
+TUBE_OPACITY = 1.0
+RING_COLOR = "#08306b"
+
+
+def circle_points(
+    center: np.ndarray,
+    axis: np.ndarray,
+    diameter: float,
+    n: int = 24,
+    ref: np.ndarray = None,
+) -> np.ndarray:
+    """Ring of ``n`` points of the given diameter, perpendicular to ``axis``.
+
+    Args:
+        center (np.ndarray): Ring centre, shape ``(3,)``.
+        axis (np.ndarray): Tube axis at this station; the ring lies in the plane
+            normal to it.
+        diameter (float): Ring diameter.
+        n (int): Number of points around the ring. Defaults to 24.
+        ref (np.ndarray): Optional reference vector fixing the azimuthal origin of
+            the ring. Passing the *same* ``ref`` for every ring of a tube keeps
+            consecutive rings consistently oriented, which avoids the surface
+            twisting where the tube tangent swings (e.g. at a wingtip). Falls back
+            to an automatic helper when ``ref`` is parallel to the axis.
+
+    Returns:
+        np.ndarray: ``(n, 3)`` ring points, closed (first point repeated at end).
+    """
+    axis = np.asarray(axis, dtype=float)
+    axis = axis / max(np.linalg.norm(axis), 1e-12)
+    if ref is not None:
+        helper = np.asarray(ref, dtype=float)
+        if abs(axis @ (helper / max(np.linalg.norm(helper), 1e-12))) > 0.98:
+            helper = np.array([0.0, 0.0, 1.0])
+            if abs(axis @ helper) > 0.9:
+                helper = np.array([0.0, 1.0, 0.0])
+    else:
+        helper = np.array([0.0, 0.0, 1.0])
+        if abs(axis @ helper) > 0.9:
+            helper = np.array([0.0, 1.0, 0.0])
+    e1 = np.cross(axis, helper)
+    e1 = e1 / max(np.linalg.norm(e1), 1e-12)
+    e2 = np.cross(axis, e1)
+    theta = np.linspace(0.0, 2.0 * np.pi, n)
+    radius = 0.5 * float(diameter)
+    return (
+        np.asarray(center, dtype=float)[None, :]
+        + radius * np.cos(theta)[:, None] * e1[None, :]
+        + radius * np.sin(theta)[:, None] * e2[None, :]
+    )
+
+
+def rings_along_centerline(
+    centers: np.ndarray, diameters: np.ndarray, n: int = 24
+) -> List[np.ndarray]:
+    """Sweep circular rings along a centreline with twist-free framing.
+
+    The ring frame is parallel-transported from one station to the next (the
+    previous in-plane axis is projected onto the new normal plane) instead of
+    being chosen independently per ring. This keeps the swept surface from
+    twisting or pinching where the centreline curves sharply -- e.g. where the
+    leading-edge tube turns chordwise at a wingtip.
+
+    Args:
+        centers (np.ndarray): ``(m, 3)`` centreline points.
+        diameters (np.ndarray): ``(m,)`` local tube diameters.
+        n (int): Number of points around each ring. Defaults to 24.
+
+    Returns:
+        List[np.ndarray]: One ``(n, 3)`` ring per centreline station.
+    """
+    centers = np.asarray(centers, dtype=float)
+    diameters = np.asarray(diameters, dtype=float)
+    m = len(centers)
+
+    tangents = np.zeros_like(centers)
+    tangents[1:-1] = centers[2:] - centers[:-2]
+    tangents[0] = centers[1] - centers[0]
+    tangents[-1] = centers[-1] - centers[-2]
+    tangents /= np.linalg.norm(tangents, axis=1, keepdims=True).clip(1e-12)
+
+    def _seed_axis(tangent):
+        ref = np.array([0.0, 0.0, 1.0])
+        if abs(tangent @ ref) > 0.9:
+            ref = np.array([0.0, 1.0, 0.0])
+        axis = ref - (ref @ tangent) * tangent
+        return axis / max(np.linalg.norm(axis), 1e-12)
+
+    theta = np.linspace(0.0, 2.0 * np.pi, n)
+    e1 = _seed_axis(tangents[0])
+    rings = []
+    for index in range(m):
+        # Parallel-transport the previous in-plane axis onto this normal plane.
+        e1 = e1 - (e1 @ tangents[index]) * tangents[index]
+        if np.linalg.norm(e1) < 1e-8:
+            e1 = _seed_axis(tangents[index])
+        e1 /= max(np.linalg.norm(e1), 1e-12)
+        e2 = np.cross(tangents[index], e1)
+        radius = 0.5 * diameters[index]
+        rings.append(
+            centers[index][None, :]
+            + radius * np.cos(theta)[:, None] * e1[None, :]
+            + radius * np.sin(theta)[:, None] * e2[None, :]
+        )
+    return rings
+
+
+def load_le_diameter_interp(processed_dir: Path) -> Callable[[float], float]:
+    """Return a leading-edge diameter(span-fraction) interpolator.
+
+    Builds the interpolator from the ``leading_edge_tubes`` segment table: each
+    segment midpoint gives a ``(|y| fraction, diameter)`` sample. Fractions are
+    of the half-span so the curve can be sampled against any drawn wing.
+
+    Args:
+        processed_dir (Path): Directory holding
+            ``struc_geometry_all_in_surfplan.yaml``.
+
+    Returns:
+        Callable[[float], float]: Function mapping a span fraction in ``[0, 1]``
+            to a leading-edge tube diameter in metres.
+    """
+    struc = yaml.safe_load(
+        (Path(processed_dir) / "struc_geometry_all_in_surfplan.yaml").read_text()
+    )
+    particles = {
+        int(row[0]): np.asarray(row[1:4], dtype=float)
+        for row in struc["wing_particles"]["data"]
+    }
+    le = struc["leading_edge_tubes"]
+    idx_ci = le["headers"].index("ci")
+    idx_cj = le["headers"].index("cj")
+    idx_d = le["headers"].index("diameter")
+
+    y_mid = []
+    diam = []
+    for row in le["data"]:
+        ci = particles[int(row[idx_ci])]
+        cj = particles[int(row[idx_cj])]
+        y_mid.append(0.5 * (ci[1] + cj[1]))
+        diam.append(float(row[idx_d]))
+    y_mid = np.abs(np.asarray(y_mid))
+    diam = np.asarray(diam)
+    half_span = y_mid.max() if y_mid.max() > 0 else 1.0
+    fractions = y_mid / half_span
+
+    order = np.argsort(fractions)
+    frac_sorted = fractions[order]
+    diam_sorted = diam[order]
+
+    def interp(span_fraction: float) -> float:
+        return float(np.interp(span_fraction, frac_sorted, diam_sorted))
+
+    return interp
+
+
+def load_strut_diameter_table(processed_dir: Path) -> List[Tuple[float, float]]:
+    """Per-strut ``(diam_le, diam_te)`` pairs in span order (tip to tip).
+
+    Args:
+        processed_dir (Path): Directory holding
+            ``struc_geometry_all_in_surfplan.yaml``.
+
+    Returns:
+        List[Tuple[float, float]]: Leading- and trailing-edge strut-tube diameters
+            in metres, in the table's listed (span) order.
+    """
+    struc = yaml.safe_load(
+        (Path(processed_dir) / "struc_geometry_all_in_surfplan.yaml").read_text()
+    )
+    st = struc["strut_tubes"]
+    idx_le = st["headers"].index("strut_diam_le")
+    idx_te = st["headers"].index("strut_diam_te")
+    return [(float(row[idx_le]), float(row[idx_te])) for row in st["data"]]
+
+
+def load_strut_span_fractions(processed_dir: Path) -> np.ndarray:
+    """Signed span fractions of the strut ribs, from the generated aero yaml.
+
+    Uses the ``is_strut`` flag in each section's airfoil ``info_dict``. Fractions
+    are ``LE_y`` normalised by the half-span, returned sorted by descending span
+    (``+tip`` to ``-tip``) to match the ``strut_tubes`` table order.
+
+    Args:
+        processed_dir (Path): Directory holding ``aero_geometry.yaml``.
+
+    Returns:
+        np.ndarray: Signed span fractions of the strut ribs, high to low.
+    """
+    aero = yaml.safe_load((Path(processed_dir) / "aero_geometry.yaml").read_text())
+    sections = aero["wing_sections"]
+    airfoils = aero["wing_airfoils"]
+    idx_id = sections["headers"].index("airfoil_id")
+    idx_le_y = sections["headers"].index("LE_y")
+    a_idx_id = airfoils["headers"].index("airfoil_id")
+    a_idx_info = airfoils["headers"].index("info_dict")
+    is_strut_by_id = {
+        row[a_idx_id]: bool(row[a_idx_info].get("is_strut", False))
+        for row in airfoils["data"]
+    }
+
+    span_y = np.array([abs(row[idx_le_y]) for row in sections["data"]])
+    half_span = span_y.max() if span_y.max() > 0 else 1.0
+    strut_y = [
+        row[idx_le_y]
+        for row in sections["data"]
+        if is_strut_by_id.get(row[idx_id], False)
+    ]
+    fractions = np.array(sorted(strut_y, reverse=True)) / half_span
+    return fractions
+
+
+def _drawn_stations(panels: List[Any]) -> Dict[str, np.ndarray]:
+    """Spanwise edge stations of the drawn wing as stacked arrays.
+
+    Returns leading/trailing points, chord lengths, chord and up unit vectors, and
+    signed span fractions for the ``n_panels + 1`` panel edges, sorted by span.
+    """
+    le = [panels[0].LE_point_1] + [p.LE_point_2 for p in panels]
+    te = [panels[0].TE_point_1] + [p.TE_point_2 for p in panels]
+    up = [panels[0].x_airf]
+    for index, panel in enumerate(panels):
+        neighbours = [panel.x_airf]
+        if index + 1 < len(panels):
+            neighbours.append(panels[index + 1].x_airf)
+        up.append(np.mean(neighbours, axis=0))
+
+    le = np.asarray(le, dtype=float)
+    te = np.asarray(te, dtype=float)
+    up = np.asarray(up, dtype=float)
+    up = up / np.linalg.norm(up, axis=1, keepdims=True).clip(1e-12)
+    chord_vec = te - le
+    chord_len = np.linalg.norm(chord_vec, axis=1)
+    chord_hat = chord_vec / chord_len[:, None].clip(1e-12)
+
+    half_span = np.abs(le[:, 1]).max()
+    half_span = half_span if half_span > 0 else 1.0
+    fraction = le[:, 1] / half_span
+
+    order = np.argsort(fraction)
+    return {
+        "le": le[order],
+        "te": te[order],
+        "up": up[order],
+        "chord_len": chord_len[order],
+        "chord_hat": chord_hat[order],
+        "fraction": fraction[order],
+    }
+
+
+def _station_at(stations: Dict[str, np.ndarray], signed_fraction: float) -> dict:
+    """Interpolate a drawn-wing station at a signed span fraction."""
+    frac = stations["fraction"]
+    le = np.array(
+        [np.interp(signed_fraction, frac, stations["le"][:, i]) for i in range(3)]
+    )
+    te = np.array(
+        [np.interp(signed_fraction, frac, stations["te"][:, i]) for i in range(3)]
+    )
+    up = np.array(
+        [np.interp(signed_fraction, frac, stations["up"][:, i]) for i in range(3)]
+    )
+    up = up / max(np.linalg.norm(up), 1e-12)
+    chord_vec = te - le
+    chord_len = float(np.linalg.norm(chord_vec))
+    chord_hat = chord_vec / max(chord_len, 1e-12)
+    return {
+        "le": le,
+        "te": te,
+        "up": up,
+        "chord_len": chord_len,
+        "chord_hat": chord_hat,
+    }
+
+
+def _tip_extension(le_center, te_point, le_diameter, n=8, taper=1.0):
+    """Centreline points + diameters continuing from a tip LE to the TE.
+
+    Extends the leading-edge tube at the wingtip so it runs on to the trailing
+    edge of the outer panel, at (by default) constant diameter for a clean tube.
+    """
+    ts = np.linspace(0.0, 1.0, n + 1)[1:]  # exclude 0 (the LE centre already exists)
+    le_center = np.asarray(le_center, dtype=float)
+    te_point = np.asarray(te_point, dtype=float)
+    points = [le_center + t * (te_point - le_center) for t in ts]
+    diams = [le_diameter * (1.0 - (1.0 - taper) * t) for t in ts]
+    return points, diams
+
+
+def build_le_tube(
+    panels: List[Any],
+    le_diameter_interp: Callable[[float], float],
+    extend_tips: bool = True,
+) -> List[np.ndarray]:
+    """Build the leading-edge tube as a list of cross-section rings.
+
+    The tube runs spanwise along the drawn LE polyline; each ring is centred just
+    aft of the LE point (so the tube front touches the drawn leading edge) and its
+    diameter is looked up from the LE table at the local span fraction. With
+    ``extend_tips`` the tube wraps around each wingtip and continues toward the
+    trailing edge, tapering closed.
+
+    Args:
+        panels (List[Any]): Drawn ``Panel`` objects, ordered tip to tip.
+        le_diameter_interp (Callable[[float], float]): Output of
+            :func:`load_le_diameter_interp`.
+        extend_tips (bool): Wrap the tube from each tip toward the trailing edge.
+
+    Returns:
+        List[np.ndarray]: One ``(n, 3)`` ring per centreline station.
+    """
+    stations = _drawn_stations(panels)
+    le = stations["le"]
+    te = stations["te"]
+    fraction = np.abs(stations["fraction"])
+    diameters = np.array([le_diameter_interp(f) for f in fraction])
+    centers = le + 0.5 * diameters[:, None] * stations["chord_hat"]
+
+    center_list = list(centers)
+    diam_list = list(diameters)
+    if extend_tips:
+        neg_pts, neg_d = _tip_extension(centers[0], te[0], diameters[0])
+        pos_pts, pos_d = _tip_extension(centers[-1], te[-1], diameters[-1])
+        # Prepend the -tip wrap (reversed: TE -> LE) and append the +tip wrap.
+        center_list = neg_pts[::-1] + center_list + pos_pts
+        diam_list = neg_d[::-1] + diam_list + pos_d
+
+    return rings_along_centerline(np.array(center_list), np.array(diam_list))
+
+
+def build_strut(
+    stations: Dict[str, np.ndarray],
+    contour_table,
+    signed_fraction: float,
+    diam_le: float,
+    diam_te: float,
+    le_tube_diameter: float = 0.0,
+) -> List[np.ndarray]:
+    """Build one chordwise strut tube hugging the canopy underside.
+
+    The local airfoil top-surface is placed on the drawn wing at the strut span,
+    then each chord station is offset downward by the local strut radius so the
+    tube top touches the canopy. Diameter tapers linearly from ``diam_le`` at the
+    leading edge to ``diam_te`` at the trailing edge.
+
+    The strut starts at the aft edge of the leading-edge tube (chord fraction
+    ``le_tube_diameter / chord`` -- the LE tube spans that far back from the drawn
+    LE) rather than at the leading edge, so it butts against the LE tube instead
+    of passing straight through it.
+
+    Args:
+        stations (Dict[str, np.ndarray]): Output of :func:`_drawn_stations`.
+        contour_table: Output of ``canopy_geometry.load_contour_table``.
+        signed_fraction (float): Signed span fraction of the strut rib.
+        diam_le (float): Strut-tube diameter at the leading edge, metres.
+        diam_te (float): Strut-tube diameter at the trailing edge, metres.
+        le_tube_diameter (float): Local leading-edge tube diameter, metres; sets
+            where the strut starts. Zero means start at the leading edge.
+
+    Returns:
+        List[np.ndarray]: One ``(n, 3)`` ring per chordwise station.
+    """
+    station = _station_at(stations, signed_fraction)
+    contour = interpolate_contour_at(abs(signed_fraction), contour_table)
+    chord_len = station["chord_len"]
+
+    # Start the strut partway into the leading-edge tube (half its depth) so the
+    # forward cap is buried inside the LE tube -- no gap, no through-poke.
+    le_tube_depth_fraction = le_tube_diameter / max(chord_len, 1e-12)
+    start_fraction = min(0.5 * le_tube_depth_fraction, 0.9)
+    cx = contour[:, 0]
+    cy = contour[:, 1]
+    keep = cx > start_fraction
+    if start_fraction > 0 and keep.any():
+        cx = np.concatenate([[start_fraction], cx[keep]])
+        cy = np.concatenate(
+            [[float(np.interp(start_fraction, contour[:, 0], cy))], cy[keep]]
+        )
+
+    canopy_points = (
+        station["le"][None, :]
+        + cx[:, None] * chord_len * station["chord_hat"][None, :]
+        + cy[:, None] * chord_len * station["up"][None, :]
+    )
+    diameters = diam_le + (diam_te - diam_le) * cx
+    centers = canopy_points - 0.5 * diameters[:, None] * station["up"][None, :]
+    return rings_along_centerline(centers, diameters)
+
+
+def build_tube_data(panels: List[Any], processed_dir: Path) -> Dict[str, Any]:
+    """Assemble leading-edge and strut tube rings for the drawn wing.
+
+    Args:
+        panels (List[Any]): Drawn ``Panel`` objects, ordered tip to tip.
+        processed_dir (Path): Directory of SurfplanAdapter-generated files.
+
+    Returns:
+        Dict[str, Any]: ``{"le": [rings], "struts": [[rings], ...]}``.
+    """
+    processed_dir = Path(processed_dir)
+    le_interp = load_le_diameter_interp(processed_dir)
+    contour_table = load_contour_table(processed_dir)
+    strut_fractions = load_strut_span_fractions(processed_dir)
+    strut_table = load_strut_diameter_table(processed_dir)
+
+    le_rings = build_le_tube(panels, le_interp)
+
+    stations = _drawn_stations(panels)
+    struts = []
+    for index, signed_fraction in enumerate(strut_fractions):
+        if index < len(strut_table):
+            diam_le, diam_te = strut_table[index]
+        else:
+            # Fallback: size the strut from the local LE tube diameter.
+            diam_le = le_interp(abs(signed_fraction))
+            diam_te = 0.6 * diam_le
+        le_tube_diameter = le_interp(abs(signed_fraction))
+        struts.append(
+            build_strut(
+                stations,
+                contour_table,
+                signed_fraction,
+                diam_le,
+                diam_te,
+                le_tube_diameter=le_tube_diameter,
+            )
+        )
+    return {"le": le_rings, "struts": struts}
+
+
+def _rings_to_surface(
+    rings: List[np.ndarray],
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Stack a list of rings into ``X``, ``Y``, ``Z`` surface arrays."""
+    grid = np.stack(rings, axis=0)  # (n_rings, n_circle, 3)
+    return grid[:, :, 0], grid[:, :, 1], grid[:, :, 2]
+
+
+def _add_surface(fig: go.Figure, rings: List[np.ndarray], name: str, show_legend: bool):
+    """Add one tube as a uniformly coloured, semi-transparent surface."""
+    x, y, z = _rings_to_surface(rings)
+    fig.add_trace(
+        go.Surface(
+            x=x,
+            y=y,
+            z=z,
+            surfacecolor=np.zeros_like(z),
+            colorscale=[[0, TUBE_COLOR], [1, TUBE_COLOR]],
+            showscale=False,
+            opacity=TUBE_OPACITY,
+            name=name,
+            showlegend=show_legend,
+            lighting=dict(ambient=0.75, diffuse=0.8, specular=0.05, roughness=0.9),
+        )
+    )
+
+
+def _add_ring_cap(fig: go.Figure, ring: np.ndarray) -> None:
+    """Close an open tube end with a filled (fan-triangulated) cap disc."""
+    ring = np.asarray(ring)[:-1]  # drop the duplicated closing point
+    n = len(ring)
+    center = ring.mean(axis=0)
+    verts = np.vstack([center[None, :], ring])  # index 0 is the centre
+    i = [0] * n
+    j = [1 + t for t in range(n)]
+    k = [1 + (t + 1) % n for t in range(n)]
+    fig.add_trace(
+        go.Mesh3d(
+            x=verts[:, 0],
+            y=verts[:, 1],
+            z=verts[:, 2],
+            i=i,
+            j=j,
+            k=k,
+            color=TUBE_COLOR,
+            opacity=TUBE_OPACITY,
+            name="Tube cap",
+            showlegend=False,
+        )
+    )
+
+
+def _add_tube_with_caps(
+    fig: go.Figure, rings: List[np.ndarray], name: str, show_legend: bool
+):
+    """Add a tube surface and close both open ends with caps."""
+    _add_surface(fig, rings, name, show_legend)
+    _add_ring_cap(fig, rings[0])
+    _add_ring_cap(fig, rings[-1])
+
+
+def add_strut_surfaces(fig: go.Figure, tube_data: Dict[str, Any]) -> None:
+    """Add the strut tube surfaces (with end caps) to the figure."""
+    for index, strut in enumerate(tube_data["struts"]):
+        _add_tube_with_caps(fig, strut, "Strut tube", show_legend=(index == 0))
+
+
+def add_le_tube_surface(fig: go.Figure, tube_data: Dict[str, Any]) -> None:
+    """Add the leading-edge tube surface (with end caps) to the figure."""
+    _add_tube_with_caps(fig, tube_data["le"], "Leading-edge tube", show_legend=True)
+
+
+def add_tube_surfaces(fig: go.Figure, tube_data: Dict[str, Any]) -> None:
+    """Add the leading-edge and strut tube surfaces to the figure."""
+    add_le_tube_surface(fig, tube_data)
+    add_strut_surfaces(fig, tube_data)
+
+
+def add_tube_rings(fig: go.Figure, tube_data: Dict[str, Any]) -> None:
+    """Add the construction rings (wireframe circles) for inspection."""
+    all_rings = list(tube_data["le"]) + [
+        r for strut in tube_data["struts"] for r in strut
+    ]
+    for index, ring in enumerate(all_rings):
+        fig.add_trace(
+            go.Scatter3d(
+                x=ring[:, 0],
+                y=ring[:, 1],
+                z=ring[:, 2],
+                mode="lines",
+                line=dict(color=RING_COLOR, width=1),
+                name="Tube rings",
+                showlegend=(index == 0),
+            )
+        )
diff --git a/src/VSM/stability_derivatives.py b/src/VSM/stability_derivatives.py
index 0489689..527008c 100644
--- a/src/VSM/stability_derivatives.py
+++ b/src/VSM/stability_derivatives.py
@@ -183,6 +183,7 @@ def _evaluate_with_angles(alpha_deg: float, beta_deg: float) -> CoeffDict:
             angle_of_attack=alpha_deg,
             side_slip=beta_deg,
             body_rates=[rates["r"], rates["q"], rates["p"]],
+            body_axis=[[0, 0, 1], [0, 1, 0], [1, 0, 0]],
             reference_point=reference_point,
         )
         return _solve_and_extract()
diff --git a/src/VSM/trim_angle.py b/src/VSM/trim_angle.py
index 1207c8c..cb2402e 100644
--- a/src/VSM/trim_angle.py
+++ b/src/VSM/trim_angle.py
@@ -132,10 +132,6 @@ def _compute_derivative(alpha_deg: float, delta_deg: float) -> float:
         cmy_coarse.append(cmy_value)
 
     cmy_coarse_array = np.asarray(cmy_coarse)
-    import matplotlib.pyplot as plt
-
-    plt.plot(alpha_coarse, cmy_coarse_array, marker="o")
-    plt.show()
     valid_mask = ~np.isnan(cmy_coarse_array)
     valid_alphas = alpha_coarse[valid_mask]
     valid_cmy = cmy_coarse_array[valid_mask]
diff --git a/tests/AirfoilAerodynamics/test_masure.py b/tests/AirfoilAerodynamics/test_masure.py
index 76abfc7..f4e9148 100644
--- a/tests/AirfoilAerodynamics/test_masure.py
+++ b/tests/AirfoilAerodynamics/test_masure.py
@@ -6,6 +6,14 @@
 from pathlib import Path
 from VSM.core.AirfoilAerodynamics import AirfoilAerodynamics
 
+# The regression models are git-ignored, so they are absent on a clean checkout (CI).
+pytestmark = pytest.mark.skipif(
+    not Path(__file__)
+    .parent.parent.parent.joinpath("data", "ml_models", "ET_re1e6.pkl")
+    .exists(),
+    reason="Real model files not available",
+)
+
 
 class TestMasureRegressionReal:
     """Test cases for masure_regression with real model file integration."""
@@ -196,12 +204,6 @@ def test_masure_regression_different_parameters(
             assert airfoil.CD is not None, f"CD should not be None for param set {i}"
             assert airfoil.CM is not None, f"CM should not be None for param set {i}"
 
-    @pytest.mark.skipif(
-        not Path(__file__)
-        .parent.parent.parent.joinpath("data", "models", "ET_re1e6.pkl")
-        .exists(),
-        reason="Real model files not available",
-    )
     def test_masure_regression_requires_model_files(
         self, masure_params, small_alpha_range, test_reynolds, masure_config_path
     ):
diff --git a/tests/Solver/test_casadi_polar_table_inputs.py b/tests/Solver/test_casadi_polar_table_inputs.py
new file mode 100644
index 0000000..34ed806
--- /dev/null
+++ b/tests/Solver/test_casadi_polar_table_inputs.py
@@ -0,0 +1,168 @@
+"""Polar tables as inputs of the CasADi panel functions (``table_inputs=True``).
+
+Contract: with the tables passed as inputs, the residual and panel-force
+functions give the same outputs as the functions with the tables embedded;
+one function, built once, serves a second shape whose blended polars differ
+(no rebuild); omitted tables give NaN, not a silently zero polar; and the
+function still expands to SX.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+ca = pytest.importorskip("casadi")
+
+REPO = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(REPO / "tests" / "BodyAerodynamics"))
+
+from panel_kernel_cases import V3_FILE  # noqa: E402
+from VSM.core.BodyAerodynamics import BodyAerodynamics  # noqa: E402
+from VSM.core.Solver import Solver  # noqa: E402
+
+RTOL = 1e-12
+
+
+def _v3(distribution):
+    body = BodyAerodynamics.instantiate(
+        n_panels=20, file_path=V3_FILE, spanwise_panel_distribution=distribution
+    )
+    body.va_initialize(10.0, 8.0, 3.0)
+    return body
+
+
+def _solved(body, **solver_kwargs):
+    solver = Solver(gamma_loop_type="casadi_newton", allowed_error=1e-10, **solver_kwargs)
+    results = solver.solve(body)
+    return solver, np.asarray(results["gamma_distribution"], dtype=float)
+
+
+def _newton_inputs(solver, gamma):
+    v_rel = solver.compute_relative_velocity(gamma)
+    n = solver.n_panels
+    laplacian = (
+        solver._build_spanwise_laplacian()
+        if solver.is_with_artificial_viscosity
+        else np.zeros((n, n))
+    )
+    return {
+        "v_rel": v_rel,
+        "x_airf": solver.x_airf_array,
+        "y_airf": solver.y_airf_array,
+        "z_airf": solver.z_airf_array,
+        "chord": solver.chord_array,
+        "width": solver.width_array,
+        "l_gamma": laplacian @ gamma,
+        **solver.casadi_stall_angle_inputs(),
+    }
+
+
+def _force_inputs(solver, gamma):
+    v_rel = solver.compute_relative_velocity(gamma)
+    return {
+        "v_rel": v_rel,
+        "v_qc": v_rel,
+        "x_airf": solver.x_airf_array,
+        "y_airf": solver.y_airf_array,
+        "z_airf": solver.z_airf_array,
+        "chord": solver.chord_array,
+        "width": solver.width_array,
+        "rho": solver.rho,
+    }
+
+
+def _assert_outputs_equal(out_tables, out_const):
+    assert set(out_tables) == set(out_const)
+    for key, value in out_const.items():
+        expected = np.array(value, dtype=float)
+        actual = np.array(out_tables[key], dtype=float)
+        scale = max(np.max(np.abs(expected)), 1e-300)
+        np.testing.assert_allclose(actual, expected, rtol=0, atol=RTOL * scale, err_msg=key)
+
+
+@pytest.mark.parametrize("viscosity", [False, True])
+def test_table_inputs_reproduce_embedded_tables(viscosity):
+    """Residual function (all 13 outputs, incl. the corner-averaged ones and
+    the viscosity lift slope) and panel-force function, against the
+    embedded-table functions of the same shape."""
+    solver, gamma = _solved(_v3("uniform"), is_with_artificial_viscosity=viscosity)
+    tables = solver.casadi_polar_table_inputs()
+    assert tables["cl_table"].shape == (solver.n_panels, solver.panels[0].panel_polar_data.shape[0])
+
+    args = _newton_inputs(solver, gamma)
+    if viscosity:  # put a panel past stall so the gate and mu are exercised
+        args["stall_angles"] = args["stall_angles"] - 0.3
+    _assert_outputs_equal(
+        solver._casadi_newton_function(table_inputs=True)(**args, **tables),
+        solver._casadi_newton_function()(**args),
+    )
+    f_args = _force_inputs(solver, gamma)
+    _assert_outputs_equal(
+        solver.casadi_panel_force_function(table_inputs=True)(**f_args, **tables),
+        solver.casadi_panel_force_function()(**f_args),
+    )
+
+
+def test_one_function_serves_a_second_shape_without_rebuild():
+    """Shapes A and B have the same panel count and alpha grid but differently
+    blended polars. The table-input function built for A, given B's tables and
+    geometry, equals a fresh embedded-table build for B, and no new
+    table-input function is built."""
+    solver_a, gamma_a = _solved(_v3("uniform"))
+    fn = solver_a._casadi_newton_function(table_inputs=True)
+    force_fn = solver_a.casadi_panel_force_function(table_inputs=True)
+    cache_size = len(solver_a._casadi_table_input_cache)
+
+    body_b = _v3("cosine")
+    tables_a = solver_a.casadi_polar_table_inputs()
+    solver_a.solve(body_b)  # same solver object, now on shape B
+    gamma_b = solver_a.solve(body_b)["gamma_distribution"]
+    tables_b = solver_a.casadi_polar_table_inputs()
+    assert np.max(np.abs(tables_b["cl_table"] - tables_a["cl_table"])) > 1e-3  # the polars differ
+
+    assert solver_a._casadi_newton_function(table_inputs=True) is fn
+    assert solver_a.casadi_panel_force_function(table_inputs=True) is force_fn
+    assert len(solver_a._casadi_table_input_cache) == cache_size
+
+    fresh_b, _ = _solved(_v3("cosine"))
+    _assert_outputs_equal(
+        fn(**_newton_inputs(solver_a, gamma_b), **tables_b),
+        fresh_b._casadi_newton_function()(**_newton_inputs(solver_a, gamma_b)),
+    )
+    _assert_outputs_equal(
+        force_fn(**_force_inputs(solver_a, gamma_b), **tables_b),
+        fresh_b.casadi_panel_force_function()(**_force_inputs(solver_a, gamma_b)),
+    )
+
+
+def test_omitted_tables_give_nan_not_zero():
+    solver, gamma = _solved(_v3("uniform"))
+    out = solver._casadi_newton_function(table_inputs=True)(**_newton_inputs(solver, gamma))
+    assert np.all(np.isnan(np.array(out["h"], dtype=float)))
+    out = solver.casadi_panel_force_function(table_inputs=True)(**_force_inputs(solver, gamma))
+    assert np.all(np.isnan(np.array(out["force"], dtype=float)))
+
+
+def test_table_input_functions_expand_to_sx():
+    solver, gamma = _solved(_v3("uniform"), is_with_artificial_viscosity=True)
+    tables = solver.casadi_polar_table_inputs()
+    for fn, args in (
+        (solver._casadi_newton_function(table_inputs=True), _newton_inputs(solver, gamma)),
+        (solver.casadi_panel_force_function(table_inputs=True), _force_inputs(solver, gamma)),
+    ):
+        _assert_outputs_equal(fn.expand()(**args, **tables), fn(**args, **tables))
+
+
+def test_newton_loop_is_unchanged_and_does_not_build_table_functions():
+    solver, _ = _solved(_v3("uniform"))
+    assert solver._casadi_table_input_cache == {}
+
+
+def test_unsupported_cases_raise_clearly():
+    solver, _ = _solved(_v3("uniform"), polar_interpolation="bspline")
+    with pytest.raises(ValueError, match="polar_interpolation='linear'"):
+        solver._casadi_newton_function(table_inputs=True)
+    with pytest.raises(ValueError, match="polar_interpolation='linear'"):
+        solver.casadi_polar_table_inputs()
diff --git a/tests/plotting/test_tube_geometry.py b/tests/plotting/test_tube_geometry.py
new file mode 100644
index 0000000..a518998
--- /dev/null
+++ b/tests/plotting/test_tube_geometry.py
@@ -0,0 +1,261 @@
+"""Tests for the fancy Plotly plot: inflatable tubes and curved canopy.
+
+Unit tests cover the pure geometry helpers (ring math, contour handling,
+interpolation). The integration tests exercise the full SurfplanAdapter
+conversion and figure assembly against the bundled TUDELFT V3 Surfplan export;
+they are skipped if SurfplanAdapter or the export data are unavailable.
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from VSM.plotly.canopy_geometry import (
+    interpolate_contour_at,
+    read_upper_contour,
+    resample_contour,
+)
+from VSM.plotly.tube_geometry import circle_points
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+SURFPLAN_DIR = REPO_ROOT / "data" / "TUDELFT_V3_KITE" / "Surfplan_export"
+CP_DIR = REPO_ROOT / "data" / "TUDELFT_V3_KITE" / "cpx_distributions"
+DRAWN_YAML = (
+    REPO_ROOT
+    / "data"
+    / "TUDELFT_V3_KITE"
+    / "CAD_derived_geometry"
+    / "aero_geometry_CAD_breukels_regression.yaml"
+)
+
+
+def _has_surfplan():
+    try:
+        import SurfplanAdapter  # noqa: F401
+    except ImportError:
+        return False
+    return SURFPLAN_DIR.is_dir()
+
+
+requires_surfplan = pytest.mark.skipif(
+    not _has_surfplan(), reason="SurfplanAdapter or Surfplan export data unavailable"
+)
+
+
+# --------------------------------------------------------------------------- #
+# Unit tests -- pure geometry helpers
+# --------------------------------------------------------------------------- #
+
+
+def test_circle_points_radius_and_orthogonality():
+    center = np.array([1.0, 2.0, 3.0])
+    axis = np.array([0.0, 1.0, 0.0])
+    diameter = 0.4
+    ring = circle_points(center, axis, diameter, n=32)
+
+    # Every point lies at the radius from the centre.
+    radii = np.linalg.norm(ring - center, axis=1)
+    assert np.allclose(radii, 0.5 * diameter, atol=1e-9)
+
+    # The ring lies in the plane normal to the axis (constant projection).
+    projections = (ring - center) @ axis
+    assert np.allclose(projections, 0.0, atol=1e-9)
+
+
+def test_circle_points_axis_normalisation_invariant():
+    center = np.zeros(3)
+    ring_unit = circle_points(center, np.array([0.0, 0.0, 1.0]), 1.0)
+    ring_scaled = circle_points(center, np.array([0.0, 0.0, 5.0]), 1.0)
+    assert np.allclose(ring_unit, ring_scaled, atol=1e-9)
+
+
+def test_resample_contour_count_and_endpoints():
+    contour = np.array([[0.0, 0.0], [0.3, 0.08], [0.7, 0.05], [1.0, 0.0]])
+    resampled = resample_contour(contour, n_points=25)
+    assert resampled.shape == (25, 2)
+    assert np.allclose(resampled[0], contour[0], atol=1e-9)
+    assert np.allclose(resampled[-1], contour[-1], atol=1e-9)
+
+
+def test_interpolate_contour_at_blends_and_clamps():
+    c_lo = np.array([[0.0, 0.0], [1.0, 0.0]])
+    c_hi = np.array([[0.0, 0.0], [1.0, 1.0]])
+    table = [(0.0, c_lo), (1.0, c_hi)]
+
+    # Midpoint is the average of the two contours.
+    mid = interpolate_contour_at(0.5, table)
+    assert np.allclose(mid, 0.5 * (c_lo + c_hi))
+
+    # Outside the range clamps to the end contours.
+    assert np.allclose(interpolate_contour_at(-1.0, table), c_lo)
+    assert np.allclose(interpolate_contour_at(2.0, table), c_hi)
+
+
+def test_read_upper_contour_ordering(tmp_path):
+    # Minimal Selig-style .dat: TE -> upper -> LE -> lower -> TE.
+    dat = tmp_path / "prof.dat"
+    dat.write_text(
+        "test_profile\n" "1.0 0.0\n" "0.5 0.08\n" "0.0 0.0\n" "0.5 -0.04\n" "1.0 0.0\n"
+    )
+    upper = read_upper_contour(dat)
+    # Ordered LE (x=0) to TE (x=1), all upper-surface points.
+    assert upper[0, 0] == pytest.approx(0.0)
+    assert upper[-1, 0] == pytest.approx(1.0)
+    assert np.all(np.diff(upper[:, 0]) > 0)
+    assert np.all(upper[:, 1] >= 0.0)
+
+
+# --------------------------------------------------------------------------- #
+# Integration tests -- full conversion + figure assembly
+# --------------------------------------------------------------------------- #
+
+
+@requires_surfplan
+def test_ensure_surfplan_processed_and_cache():
+    from VSM.plotly.surfplan_runner import ensure_surfplan_processed
+
+    processed = ensure_surfplan_processed(SURFPLAN_DIR)
+    aero = processed / "aero_geometry.yaml"
+    struc = processed / "struc_geometry_all_in_surfplan.yaml"
+    assert aero.exists() and struc.exists()
+
+    # Second call is a cache hit: the generated file is not rewritten.
+    first_mtime = aero.stat().st_mtime
+    ensure_surfplan_processed(SURFPLAN_DIR)
+    assert aero.stat().st_mtime == first_mtime
+
+
+@requires_surfplan
+def test_build_tube_and_canopy_shapes():
+    from VSM.core.BodyAerodynamics import BodyAerodynamics
+    from VSM.plotly.surfplan_runner import ensure_surfplan_processed
+    from VSM.plotly.tube_geometry import build_tube_data, load_strut_span_fractions
+    from VSM.plotly.canopy_geometry import build_canopy_grid, load_contour_table
+
+    n_panels = 36
+    body = BodyAerodynamics.instantiate(
+        n_panels=n_panels,
+        file_path=DRAWN_YAML,
+        spanwise_panel_distribution="uniform",
+    )
+    panels = body.panels
+    processed = ensure_surfplan_processed(SURFPLAN_DIR)
+
+    tube = build_tube_data(panels, processed)
+    n_struts = len(load_strut_span_fractions(processed))
+    # LE tube has one ring per panel edge, plus the wrap-around tip extensions.
+    assert len(tube["le"]) >= n_panels + 1
+    assert len(tube["struts"]) == n_struts
+
+    x, y, z = build_canopy_grid(panels, load_contour_table(processed))
+    assert x.shape[0] == n_panels + 1
+    assert x.shape == y.shape == z.shape
+
+    # Tubes track the drawn wing span (within a tube radius at the tips).
+    wing_le = np.array([p.LE_point_1 for p in panels] + [p.LE_point_2 for p in panels])
+    tube_pts = np.vstack(list(tube["le"]))
+    assert abs(tube_pts[:, 1].max() - wing_le[:, 1].max()) < 0.2
+
+
+@requires_surfplan
+def test_interactive_plot_fancy_traces():
+    import plotly.graph_objects as go
+    from VSM.core.BodyAerodynamics import BodyAerodynamics
+    from VSM.plot_geometry_plotly import interactive_plot
+    from VSM.plotly.tube_geometry import load_strut_span_fractions
+    from VSM.plotly.surfplan_runner import ensure_surfplan_processed
+
+    body = BodyAerodynamics.instantiate(
+        n_panels=36,
+        file_path=DRAWN_YAML,
+        spanwise_panel_distribution="uniform",
+    )
+    n_struts = len(load_strut_span_fractions(ensure_surfplan_processed(SURFPLAN_DIR)))
+
+    # Fancy plot with canopy (panels default off): surfaces = canopy + LE + struts.
+    fig = interactive_plot(body, surfplan_dir=SURFPLAN_DIR, is_show=False)
+    surfaces = [t for t in fig.data if isinstance(t, go.Surface)]
+    flat_panels = [
+        t for t in fig.data if isinstance(t, go.Mesh3d) and t.name == "Panel Surface"
+    ]
+    le_outline = [t for t in fig.data if getattr(t, "name", None) == "Leading Edge"]
+    assert len(surfaces) == 1 + 1 + n_struts
+    assert len(flat_panels) == 0
+    assert len(le_outline) == 0  # panel outlines hidden by default in fancy mode
+
+    # Tubes only with panels explicitly on: flat panels kept, no canopy surface.
+    fig2 = interactive_plot(
+        body,
+        surfplan_dir=SURFPLAN_DIR,
+        is_with_canopy=False,
+        is_with_panels=True,
+        is_show=False,
+    )
+    surfaces2 = [t for t in fig2.data if isinstance(t, go.Surface)]
+    flat_panels2 = [
+        t for t in fig2.data if isinstance(t, go.Mesh3d) and t.name == "Panel Surface"
+    ]
+    assert len(surfaces2) == 1 + n_struts
+    assert len(flat_panels2) == len(body.panels)
+
+    # Plain plot is unchanged: panels shown, no surfaces at all.
+    fig3 = interactive_plot(body, is_show=False)
+    assert not [t for t in fig3.data if isinstance(t, go.Surface)]
+    assert [t for t in fig3.data if getattr(t, "name", None) == "Leading Edge"]
+
+
+# --------------------------------------------------------------------------- #
+# Cp distribution database
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.skipif(not CP_DIR.is_dir(), reason="Cp distributions unavailable")
+def test_load_upper_surface_cp_ordering():
+    from VSM.plotly.cp_distributions import load_upper_surface_cp
+
+    x, cp = load_upper_surface_cp(CP_DIR / "cp_AOA_8.dat")
+    assert np.all(np.diff(x) >= 0)  # ascending x/c
+    assert x.min() >= 0.0 and x.max() <= 1.01  # normalised chord (small CFD overshoot)
+    assert cp.min() < 0  # suction side has negative Cp
+
+
+@pytest.mark.skipif(not CP_DIR.is_dir(), reason="Cp distributions unavailable")
+def test_build_cp_magnitude_fn_closest_aoa():
+    from VSM.plotly.cp_distributions import build_cp_magnitude_fn
+
+    # Exact and nearest matches over the available {2, 6, 8} degrees.
+    assert build_cp_magnitude_fn(CP_DIR, 8)[1] == 8
+    assert build_cp_magnitude_fn(CP_DIR, 5)[1] == 6
+    assert build_cp_magnitude_fn(CP_DIR, 3)[1] == 2
+    assert build_cp_magnitude_fn(CP_DIR, 100)[1] == 8
+
+    magnitude, _ = build_cp_magnitude_fn(CP_DIR, 8)
+    values = magnitude(np.array([0.05, 0.5, 0.95]))
+    assert np.all(values >= 0)
+    # Loading is front-heavy: near-LE |Cp| exceeds near-TE |Cp|.
+    assert values[0] > values[-1]
+
+
+@pytest.mark.skipif(
+    not (_has_surfplan() and CP_DIR.is_dir()),
+    reason="SurfplanAdapter or data unavailable",
+)
+def test_interactive_plot_cp_scaled_vectors_run():
+    from VSM.core.BodyAerodynamics import BodyAerodynamics
+    from VSM.plot_geometry_plotly import interactive_plot
+
+    body = BodyAerodynamics.instantiate(
+        n_panels=36,
+        file_path=DRAWN_YAML,
+        spanwise_panel_distribution="uniform",
+    )
+    fig = interactive_plot(
+        body,
+        angle_of_attack=8,
+        surfplan_dir=SURFPLAN_DIR,
+        cp_distributions_dir=CP_DIR,
+        is_show=False,
+    )
+    # The vector grid is still drawn (stems + arrowheads present).
+    assert [t for t in fig.data if getattr(t, "name", None) == "Aerodynamic Vectors"]