Dynamic Machine Learning Global Optimization Algorithm and Its Application to Aerodynamics

Abstract

A dynamic extreme gradient boosting (XGBoost) and MaxLIPO trust region parallel global optimization algorithm is proposed in this paper, and it is applied to the turbomachinery blade aerodynamic optimization coupled with an in-house graphics processing unit (GPU) heterogeneous accelerated compressible flow solver, AeroWhale. The algorithm combines an accurate machine learning regression model, an efficient nongradient optimization method with no hyperparameters, a dynamic update regression strategy, and double convergence criteria to achieve high optimization accuracy and efficiency. The optimization results on the test function indicate that the number of objective function calls is less than 2% of that required by a traditional genetic algorithm, which greatly reduces the optimization time. The dynamic XGBoost model ensures that the regression model accuracy near the optimum is relatively high, which is attributed to the update strategy. The error between the optimal value identified by the proposed algorithm and the theoretical value is only 0.52% after several objective function calls. Finally, the aerodynamic optimization algorithm is applied to the LS89 high-pressure turbine, and the total pressure loss is reduced by 13.16%. The sensitivity of each optimization feature to the objective function is determined, showing that the blade suction surface control point near the trailing edge has the greatest impact on aerodynamic performance.