基于一种贝叶斯优化框架的高空螺旋桨气动优化设计

To obtain optimal aerodynamic configurations of high-altitude propellers efficiently, we propose a method for the aerodynamic shape design of high-altitude propellers within the Bayesian optimization framework. This method parameterizes propellers' shapes by eight variables using the quadratic functions. Initial samples come from the Latin hypercube sampling. The corresponding aerodynamic performance is obtained by Computational Fluid Dynamics (CFD) simulations, which have been validated by ground tests. A Gaussian process is established between the shape parameters and the aerodynamic performance. New samples are obtained by the sub-optimization composed of the genetic algorithm and the infill sampling criterion. The infill sampling criterion enables generating new samples near the locally and globally optimal solutions to improve the approximation accuracy near the optimal solution without considering the prediction accuracy in the whole design space; the parallel strategy can enhance its efficiency. Samples and the Gaussian process are adaptively updated. We apply this method to optimize the propeller of a high-altitude long-endurance solar-powered UAV based on six low-Reynolds-number airfoils developed by our group. This method shows great potential in optimizing high-altitude propellers and, hopefully, other applications since the thrust and efficiency of the optimized propeller increase by roughly 10%.