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

Shuang Sun, Zhen Huang, Jinhui Kang, Xiaopeng Sun, Boyu Kuang, Lehan Lu
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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.
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用 RBF-GA 方法研究多因素对高扬程 LPT 边界层的影响
在高空巡航状态下,高扬程低压涡轮机存在边界层分离现象,不同叶片设计所对应的进气条件会直接影响低压涡轮机的工作效率。尤其是进气流中尾流频率的降低和自由流湍流强度的降低会极大地影响边界层的分离和过渡发展。本文通过数值模拟和实验研究了在上游湍流的影响下,不同的入流湍流强度和降低的唤醒频率对大扬程低压涡轮机吸气面边界层发展的影响。由于流入自由流湍流强度和降低唤醒频率的组合,在设计过程中可以选择多种流入条件,而上游唤醒的不稳定影响使边界层流动变得复杂。本文采用 RBF(径向基函数)-GA(遗传算法)机器学习方法,探索与 Pak-B 剖面损失最小相对应的最佳入口条件。自由流湍流强度的搜索区域为 2%-4%,通过改变流量系数来改变唤醒的降低频率,其变化范围为 0.7-1.3。研究发现,RBF-GA 机器学习方法可以在使用相同计算量和工作量的情况下,获得具有较低剖面损失的流入条件。
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