Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Abstract

In a high-altitude cruising state, boundary layer separation exists in high-lift low-pressure turbines, and inflow conditions corresponding to different blade designs can directly affect the working efficiency of low-pressure turbines. In particular, the reduced frequency of wake and free-stream turbulence intensity in an inlet flow can greatly influence boundary layer separation and transition development. In this paper, the influence of different inflow turbulence intensities and reduced wake frequencies on the development of suction surface boundary layers in high-lift low-pressure turbines under the influence of upstream wakes is studied by numerical simulations and experiments. Due to the combination of inflow free-stream turbulence intensity and reduced wake frequency, many inflow conditions can be chosen in the design process, and the unsteady influence of upstream wakes complicates the boundary layer flow. In this paper, an RBF (radial basis function)-GA (genetic algorithm) machine learning method is used to explore the optimal inlet conditions corresponding to the minimum profile loss of the Pak-B profile. The search region of the free-stream turbulence intensity is 2%–4%, and the reduced frequency of the wake is changed by changing the flow coefficient, whose variation range is 0.7–1.3. It is found that the RBF-GA machine learning method can attain an inflow condition with a lower profile loss while using the same amount of computation and effort.