A Data-Driven Reduced Order Model for Rotor Optimization

Abstract. For rotor design applications, such as wind turbine rotor or Urban Air Mobility (UAM) rotorcraft and flying car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex turbulent flow fields. One potential path for deriving high-fidelity but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD) based reduced order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed pressure predictions on rotor blades subjected to topology change due to variations in twist and taper ratio. Rotor twist, θ, was varied between 0°, 10°, 20°, and 30° while taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases; a high pitch or a high thrust rotor in hover, a low pitch or a low thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed highly accurate distributed load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 hours on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotor in forward flight cost was reduced from 20 hours on 440 cores to less than a second on a single core. The POD ROM was used to undergo a design optimization of the rotor such that figure of merit was maximized for hovering rotor cases and the lift to drag effective ratio was maximized in forward flight.