基于内嵌物理约束神经网络模型的航空发动机数字工程模型

Zhi Fu Lin, Hong Xiao, Zhan Xue Wang, Xiao Bo Zhang

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

A digital model-based prognostics and health management(PHM)system is crucial for digitali-zation,intelligence in aeroengine. Among all digital models,an aeroengine performance digital model is one of the basic modules for PHM system,which is used for condition monitoring and performance prediction on aeroengine. In this work,a strategy for creating a performance digital model to predict aeroengine thrust is given. The strategy is to combine aeroengine domain knowledge and artificial neural networks,which is to create an architecture for tailoring the neural network model with physical information. More,the given model is designed to address feature selection. The application of the given model to aeroengine thrust prediction demonstrates its effectiveness in accuracy with the different testing datasets. Compared with the conventional neural network,the average relative error of the architecture-based model is small,and the max relative error of the architecture-based model is only 1/4 of it under the same model size. With physical constraint,the model is less reliant on training data,and the number of layers and the hyperparameters in the neural networks model are intervened.

投稿的翻译标题An Aeroengine Digital Engineering Model Based on Physics-Embedded Neural Networks
源语言繁体中文
文章编号2210025
期刊Tuijin Jishu/Journal of Propulsion Technology
44
11
DOI
出版状态已出版 - 11月 2023

关键词

  • Aeroengine
  • Digital engineering model
  • Feature processing
  • Performance parameter prediction
  • Physics-embedded neural network

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