Overview
Extended ML-simulation fusion methods for propeller optimization, combining learned surrogate models with numerical CFD simulation to achieve optimized propeller designs for autonomous underwater vehicles.
Approach
The method fuses ML predictions with CFD simulation results, using the ML model for rapid initial screening and CFD for high-fidelity validation of promising candidates. This hybrid approach balances computational efficiency with design accuracy.