Add Li/Gaunaa spanwise artificial viscosity for post-stall stabilization#247
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Port the opt-in implicit artificial-viscosity regularization (Li, Gaunaa, Pirrung & Lønbæk, TORQUE 2026) from the Python Vortex-Step-Method to the LOOP solver. Post-stall (negative lift-slope) circulation distributions otherwise develop non-physical sawtooth oscillations and never converge; the implicit scheme (I - diag(mu) L) gamma = F(gamma) stays stable at relaxation factors of order one. Adds the discrete spanwise Laplacian with second-order tip closures (Eq. 15), per-panel lift-slope evaluation, and gating so attached flow is a no-op. The expensive linear solve is gated on any(mu > 0) rather than on precomputed stall angles, since the Julia polar is an interpolation object. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Ports the opt-in implicit artificial-viscosity regularization (Li, Gaunaa, Pirrung & Lønbæk, TORQUE 2026) from the Python Vortex-Step-Method into the Julia LOOP solver.
Why
Post-stall (negative lift-slope) circulation distributions develop non-physical sawtooth oscillations in the fixed-point
gamma_loop!and never converge. The implicit scheme(I - diag(mu) L) gamma = F(gamma)has the same steady solution as the explicit fixed point but stays stable at relaxation factors of order one, whereas the explicit stable step shrinks likeN^-2in post-stall.What
Solver: new opt-in fieldsis_with_artificial_viscosity(defaultfalse) andartificial_viscosity_factor(default0.035), threaded through theVSMSettingsand ForwardDiff dual-copy constructors.build_spanwise_laplacian!: discrete spanwise Laplacian with the second-order tip closures (Eq. 15) enforcinggamma -> 0at the tips.local_lift_slope!: per-paneldCl/dalphavia central difference ofcalculate_cl.gamma_loop!(LOOP): builds the Laplacian and planform area once, then when any panel is post-stall, solves(I - diag(mu) L) gamma = F(gamma)withmu_i = max(0, -k S Cl'_i / dz_i^2)before relaxation.SolverSettings: the two fields exposed for YAML config.Design note (difference from Python)
The Python version gates the expensive linear solve on per-panel stall angles read from raw polar arrays. In Julia the polar is an interpolation object, so the solve is instead gated on
any(mu .> 0)— behaviourally identical (it fires exactly in post-stall) without exposing internal polar storage.The feature is fully opt-in and a no-op in attached flow, so existing results are unchanged.
Tests
Ports both Python tests:
Verified locally: solver tests 14/14, ForwardDiff tests 7/7, and an integration smoke test confirming attached flow is bit-identical to baseline while a high-AoA case converges.
🤖 Generated with Claude Code