High-fidelity surface flow data-driven aerodynamic solution strategy for non-smooth configurations: Study of compressor cascade with micro riblet surface

Abstract

In this paper, a new aerodynamic solution strategy for non-smooth configurations is proposed based on the wall modification model by machine learning to perform numerical simulations, rather than directly describing the global flow field with massive grids. The aerodynamic effect of non-smooth configurations in the presence of pressure gradients is investigated utilizing the proposed method. Flow features of non-smooth surface are provided by high-fidelity surface flow data acquired through lattice Boltzmann method simulation. The wall modification model is constructed by Fruit fly Optimization Algorithm-Generalized Regression Neural Network (FOA-GRNN) to reproduce the behavior of microflow near the non-smooth surface. Typical flow features, e.g., velocity corrections induced by surface texture as the output of the FOA-GRNN model, are imposed on configuration boundaries, improving computational efficiency and wall resolution. The novel aerodynamic solution strategy is validated by comparing the results of the experiment. In addition, the performance analysis of compressor cascade with micro riblet surface utilizing the above method is conducted. The results indicate that the non-smooth surface structure decreases skin friction and turbulent intensity in the flow channel compared with smooth cascade, thus significantly reducing the total pressure loss. The paper shows a positive prospect of the data-driven strategy in evaluating the aerodynamic performances of non-smooth configurations and provides a reliable solution method for the subsequent design of micro-nano surfaces.