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Title: Experimental Data-Driven Flow Field Prediction for Compressor Cascade based on Deep Learning and ℓ1 Regularization
题目：基于深度学习和l1正则的压气机叶栅实验数据驱动的流场预测
Authors: LIU Tantao, GAO Limin, LI Ruiyu
作者：刘锬韬，高丽敏，李瑞宇
单位：西北工业大学动力与能源学院，西安交通大学航空航天学院
Journal of Thermal Science, 2024, 33(5): 1867-1882.
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摘要
Abstract: For complex flows in compressors containing flow separations and adverse pressure gradients, the numerical simulation results based on Reynolds-averaged Navier-Stokes (RANS) models often deviate from experimental measurements more or less. To improve the prediction accuracy and reduce the difference between the RANS prediction results and experimental measurements, an experimental data-driven flow field prediction method based on deep learning and ℓ1 regularization is proposed and applied to a compressor cascade flow field. The inlet boundary conditions and turbulence model parameters are calibrated to obtain the high-fidelity flow fields. The Saplart-Allmaras and SST turbulence models are used independently for mutual validation. The contributions of key modified parameters are also analyzed via sensitivity analysis. The results show that the prediction error can be reduced by nearly 70% based on the proposed algorithm. The flow fields predicted by the two calibrated turbulence models are almost the same and nearly independent of the turbulence models. The corrections of the inlet boundary conditions reduce the error in the first half of the chord. The turbulence model calibrations fix the overprediction of flow separation on the suction surface near the tail edge.
摘要：压气机中的流动十分复杂，其中包含流动分离和逆压力梯度，基于雷诺平均湍流模型的数值仿真流场通常会或多或少偏离实验测量结果。为了提高流场预测精度，减少数值仿真与实验测量之间的偏差，提出了一种基于深度神经学习和l1正则的实验数据驱动的压气机流场预测方法，为获得高可信度的流场，校正了进口边界条件和湍流模型参数。独立使用了S-A和SST湍流模型性相互验证预测结果，并使用敏感性分析方法获得关键参数的贡献。结果显示本文提出的算法可以减小约70%的预测偏差，两种湍流模型预测出的流场几乎相同即预测结果基本独立于湍流模型。进口边界条件的修正主要减小了前半弦长的预测偏差，而湍流模型的修正主要减小了吸力面尾缘流动分离的过渡预测。
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引用格式
LIU Tantao, GAO Limin, LI Ruiyu, Experimental Data-Driven Flow Field Prediction for Compressor Cascade based on Deep Learning and ℓ1 Regularization, Journal of Thermal Science, 2024, 33(5): 1867-1882.
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