In-house verification run. This is one of Curlscape’s own research studies, run to exercise our agentic CAE/CFD pipeline. It is not a client deliverable. Method and limitations are stated in full.
- NSGA-II
- OpenProp / BEMT
- OpenFOAM RANS
- MRF k-ω SST
- Multi-objective
- Agentic pipeline
PROBLEM
Design a propeller for a planing-hull unmanned surface vessel of roughly 200 kg, at a design speed of 15 m/s (about 29 kn) in seawater, delivering a required thrust of 380 N. The problem is multi-objective for real physical reasons: maximize open-water efficiency η₀ while minimizing shaft torque Q. The two pull in opposite directions and set the motor you can select.
The search has to stay inside the physics that actually constrains a small high-speed prop.
- Cavitation margin enforced with a Keller-criterion check.
- Tip-speed limit to keep the blade tips out of the cavitation regime.
- Motor-torque ceiling so the winning design is one you can actually drive.
- Thrust treated as a hard equality constraint (380 N), not a soft target.
APPROACH
The design vector is expressed as genes for a genetic algorithm: blade count Z ∈ {3, 4, 5}, diameter D from 0.12–0.20 m, shaft speed N from 3000–6000 RPM, and three chord-scale genes (root / mid / tip) applied over a DTMB-4119-like baseline blade (a NACA a = 0.8 meanline with a NACA66 thickness form).
Each candidate is scored fast with OpenProp lifting-line BEMT (the Epps optimizer), which holds thrust as a hard equality constraint. That fast evaluator sits inside a pymoo NSGA-II search (population 64, 40 generations), which produces a Pareto front spanning the motor-selection trade: low-RPM / high-torque / high-η at one end, high-RPM / low-torque / lower-η at the other.
The knee point of that front is lofted to an STL and handed to OpenFOAM v2506 for a blade-resolved RANS verification: snappyHexMesh + simpleFoam, MRF with a k-ω SST turbulence model, run at two operating points (the design advance ratio J and J − 0.15). CFD KT, KQ and η₀ are then compared directly against the BEMT prediction.
The whole sweep → search → verify loop is automated and orchestrated by review agents. Separate agents own geometry review, mesh QA, solver QA, and run monitoring, each with a defined check it has to pass before the pipeline advances.
RESULT
The study produces a verified open-water curve for the winning geometry and a BEMT-vs-CFD agreement table across KT, KQ and η₀ at the two operating points. We report this as a qualitative agreement plus an accounting of where the two methods diverge. We do not publish coefficients we would not defend out of context.
The discrepancies are expected and physical, and naming them is the point:
- Blade-resolved RANS captures viscous and 3-D effects that a lifting-line model approximates.
- Hub drag is resolved in CFD but only lumped in BEMT.
- Tip-vortex resolution depends on the near-tip mesh, which BEMT does not model at all.
LIMITATIONS
- Steady MRF was used (no sliding-mesh URANS), so unsteady blade-passage effects are not resolved.
- No cavitation model in the CFD; cavitation is handled up front as a Keller margin inside the GA constraints.
- Open-water only. No hull or wake effects, and no self-propulsion interaction.
- The comparison is BEMT vs. a single verified geometry; the full Pareto front was not swept in CFD.
Have a study like this to run?
We build agentic CAE/CFD pipelines and surrogate models on your solvers, on your infrastructure, with verification agents gating every stage.