A novel multi-fidelity optimization framework for high-altitude propellers

A novel optimization methodology is presented for optimizing high-altitude propellers operating in the lower stratosphere. The proposed methodology combines a Bayesian optimization approach for most design variables and Vortex Theory for the optimization of twist distribution and rotational velocity. For the Bayesian optimization part of the problem, a multi-fidelity approach is employed with three levels of modeling fidelity. The considered fidelity levels, from lower to higher, are: Vortex Theory, and 3D Reynolds-Averaged Navier-Stokes (RANS) with the use of γ−Re_θ transition model, converged with first-order upwind, and second-order upwind for the momentum equations. Additionally, the optimization problem involves two objectives: maximizing the cruise efficiency and minimizing the total volume of the propeller. To enhance the performance of multi-fidelity, multi-objective (MFMO) acquisition functions, a novel algorithm is proposed. The proposed optimization methodology demonstrates its effectiveness in generating several high-performing propeller designs and proves to be more efficient than an equivalent problem formulation relying solely on Bayesian optimization. Post-processing of optimization data reveals new design variable bounds that correspond to more efficient blade shapes. These findings provide insights into some important geometric features of high-altitude propellers and establish guidelines for future optimization efforts within the specified thrust and power consumption ranges. Finally, simulations of the best-performing designs across a range of advance ratios and altitudes confirm their high performance in various operating conditions.