空气动力学
人工神经网络
转子(电动)
计算机科学
涡轮机械
刀(考古)
卷积神经网络
领域(数学)
集合(抽象数据类型)
涡轮机
性能预测
人工智能
工程类
机械工程
模拟
数学
结构工程
航空航天工程
纯数学
程序设计语言
作者
Qiuwan Du,Lili Li,Like Yang,Tianyuan Liu,Di Zhang,Tianyuan Liu
出处
期刊:Energy
[Elsevier]
日期:2022-09-01
卷期号:254: 124351-124351
被引量:22
标识
DOI:10.1016/j.energy.2022.124351
摘要
Aerodynamic design optimization of the blade profile is a critical approach to improve performance of turbomachinery. This paper aims to achieve the performance prediction with deep learning method and realize fast design optimization of a turbine blade. Two parameterization methods based on geometric relationships (PGR) and neural network (PNN) are proposed, which can generate smooth and complete blade profiles. A dual convolutional neural network (DCNN) is constructed to predict the physical fields and aerodynamic performance. The implementations of DCNN are accomplished based on the datasets generated by the two parameterization methods respectively, which are called PGR-DCNN and PNN-DCNN model. Results show that the prediction accuracy increases and then keeps stable as train size increases. The two models can offer the detailed physical field distribution within 3 ms and accurately predict the aerodynamic performance. The prediction errors of performance parameters for 99% samples in validation set are less than 0.5% with PGR-DCNN model, which are significantly better than conventional machine learning methods. Finally, based on the accurate predictive models, the gradient-based design optimization for rotor blade profile is completed in 38 s. The efficiency of the two optimal blades reaches 89.29% and 88.92% respectively, which verifies the feasibility of our method. • Two parameterization methods based on geometric relationships and neural network are proposed. • The DCNN model is constructed to reconstruct physical fields and predict performance. • High prediction accuracy and fast calculation speed are achieved by DCNN model. • The gradient descent method is adopted to conduct the optimization of the turbine blade profile.
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