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Dual-convolutional neural network based aerodynamic prediction and multi-objective optimization of a compact turbine rotor

机译：基于双卷积神经网络的基于致密涡轮机转子的多目标优化

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摘要

With the development of neural network technology, surrogate models and dimensionality reduction strategies based on machine learning have become the research hotspots of aerodynamic shape optimization recently. In order to further improve the accuracy and interpretability of the traditional surrogate models, this research establishes a deep learning model, named Dual Convolutional Neural Network (Dual-CNN) for the aero-engine turbines. The aerodynamic performances are predicted and the pressure, temperature fields are reconstructed for multiple rotor profile conditions. The prediction of efficiency is compared with the accuracy of Gaussian Process Regression (GPR) and Artificial Neural Network (ANN) models. The results show that the proposed Dual-CNN model can accurately reconstruct the fields, thus interpreting the mechanism for the change of aerodynamic performance. Dual-CNN is more accurate than GPR and ANN in predicting efficiency and torque, whose error is within an acceptable range of optimization. Then, efficiency and torque are selected as the objective functions to perform a gradient-based multi-objective optimization by the automatic differentiation method and a Pareto solution is obtained. The trained Dual-CNN provides rapid and accurate prediction of performance without CFD calculation in the optimization. Finally, the sensitivity to train size is analyzed for the Dual CNN model, which indicates that the sampling of 1500 cases for eight design variables in this dataset enables Dual-CNN to achieve favorable effect of field reconstruction and performance prediction. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：随着神经网络技术的发展，基于机器学习的代理模型和维度减少策略已成为最近的空气动力学优化的研究热点。为了进一步提高传统代理模型的准确性和可解释性，这项研究建立了一个用于航空发动机涡轮机的双卷积神经网络（双CNN）的深层学习模型。预测空气动力学性能以及压力，温度场被重建用于多个转子轮廓条件。将效率预测与高斯过程回归（GPR）和人工神经网络（ANN）模型的准确性进行了比较。结果表明，所提出的双CNN模型可以准确地重建场，从而解释气动性能变化的机制。双CNN比GPR和ANN更精确，以预测效率和扭矩，其误差在可接受的优化范围内。然后，选择效率和扭矩作为通过自动分化法执行基于梯度的多目标优化的目标函数，并且获得帕累托溶液。训练有素的双CNN在不需要CFD计算的情况下提供了对性能的快速准确的性能预测。最后，对双CNN模型分析了对列车规模的敏感性，这表明该数据集中的八个设计变量的1500例采样使双CNN能够实现现场重建和性能预测的良好影响。 （c）2021 Elsevier Masson SAS。版权所有。

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  • 来源
    《Aerospace science and technology》 |2021年第9期|106869.1-106869.13|共13页
  • 作者

    Wang Yuqi; Liu Tianyuan; Zhang Di; Xie Yonghui;

  • 作者单位

    Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian Peoples R China;

    Xi An Jiao Tong Univ Sch Energy & Power Engn Shaanxi Engn Lab Turbomachinery & Power Equipment Xian Peoples R China;

    Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian Peoples R China;

    Xi An Jiao Tong Univ Sch Energy & Power Engn Shaanxi Engn Lab Turbomachinery & Power Equipment Xian Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Aerodynamic prediction; Multi-objective optimization; Turbine; Deep learning; Convolution neural network;

    机译：空气动力学预测;多目标优化;涡轮机;深度学习;卷积神经网络;

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