Parameter optimization design of an aero-engine bearing chamber based on active learning Kriging

The air-oil two-phase flow in the aero-engine bearing chamber is highly complex and unsteady. To obtain the mapping relationship between the oil volume fraction in the chamber and the oil inlet flow rate and rotational speed for further design optimization, the air-oil two-phase flows are simulated using the level set method in COMSOL Multiphysics software. To avoid frequent calls to the simulation model during the optimization process, which consumes huge computational costs, the Kriging model is applied to approximate the true response of the computational fluid dynamics (CFD) simulations. The point addition process is assisted by introducing the active learning function EI to enhance the efficiency of constructing the Kriging model. Particle swarm optimization (PSO) is applied to optimize the Kriging-based optimization objective function. With the optimal parameters’ combination, the performance of the oil return in the aero-engine bearing chamber is improved. This study has guiding significance for the lubrication design of the aero-engine bearing chamber.