基于深度学习的压气机叶型气动特性预测

Prediction of aerodynamic characteristics of compressor blade profile based on deep learning

采用了数值模拟与机器学习相结合的方式对压气机双圆弧叶型流场气动参数预测开展了研究。对双圆弧叶型进行参数化批量建模,通过计算流体力学进行数值模拟,将数值模拟的模型数据与气动性能的映射提供给多层神经网络(MLP)和卷积神经网络(CNN)进行学习,分别对预测模型的准确率进行了测试比较。研究发现:通过深度学习的方式可以有效的对压气机内部流场气动参数进行准确预测,该模型预测的压力系数误差率小于0.2%,总压损失系数误差率小于1.2%,并证明CNN在气动参数预测的精度上优于传统全连接神经网络。

A combination of numerical simulation and machine learning was used to investigate the prediction of aerodynamic coefficients in the flow field of a double-circular-arc leaf shape of a compressor.Parametric batch modeling of the double-arc impeller shape was carried out,and numerical simulation was performed by computational fluid dynamics.The mapping of model data from numerical simulation to aerodynamic performance was provided to multilayer neural network(MLP)and convolutional neural network(CNN)for learning,and the accuracy of the prediction models was tested and compared respectively.It was found that the accurate prediction of the impeller mechanical internal flow field aerodynamic coefficients can be effectively performed by deep learning,and the error rate of the model predicted pressure coefficient was less than 0.2%and the error rate of loss coefficient was less than 1.2%,proving that CNN was better than traditional fully connected neural network in the accuracy of aerodynamic coefficient prediction.