Aerodynamic optimization of a transonic fan rotor by blade sweeping using adaptive Gaussian process

Abstract

Due to its easy implementation and comprehensive applicability, surrogate model has been widely used in the aerodynamic design optimization (ADO) of turbomachinery blades. Machine learning method, rapidly developed and applied in many disciplines, is beneficial to model training. The paper works on ADO of a transonic fan rotor by blade sweeping using a supervised learning model method. First, verifications of numerical simulation and specifications of blade design optimization are given. Then the principles and implementations of an adaptive Gaussian Process (GP) are introduced. The acquisition function for adaptive sampling firstly increases and then decreases and ultimately converges in the process of model update. Meanwhile, performance predictions of the swept blades are achieved using GP model. From probability estimation and Sobol sensitivity analysis, some priori design knowledge revealing the impacts of blade sweeping on aerodynamic performance is preliminarily discovered. The results demonstrate that the concerned performance parameters are sensitive to the design parameters on the outer spans of the rotor blade. Finally, multi-objective optimization maximizing the total pressure ratio and adiabatic efficiency with constrained mass flow rate is carried out and the Pareto front is determined. The results of the original and three swept blades are compared in detail to illustrate the effects of blade sweeping to flow variations. For the transonic rotor, forward sweeping favors the increase of total pressure ratio, while backward sweeping on the outer spans favors the increase of adiabatic efficiency.