中国航空学报（英文版）2025，Vol.38Issue(4)：44-54,11.DOI:10.1016/j.cja.2024.11.034
Stall prediction model based on deep learning network in axial flow compressor
Stall prediction model based on deep learning network in axial flow compressor
Yuyang DENG 1Jichao LI 2Jingyuan LIU 2Feng PENG 2Hongwu ZHANG 2Marco P.SCHOEN3
作者信息
- 1. Advanced Gas Turbine Laboratory,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China||School of Aeronautics and Astronautics,University of Chinese Academy of Sciences,Beijing 100049,China||National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine,Beijing 100190,China||Key Laboratory of Advanced Energy and Power,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China||Jiangsu University Research Center of Fluid Machinery Engineering and Technology,Zhenjiang 212000,China
- 2. Advanced Gas Turbine Laboratory,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China||School of Aeronautics and Astronautics,University of Chinese Academy of Sciences,Beijing 100049,China||National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine,Beijing 100190,China||Key Laboratory of Advanced Energy and Power,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China
- 3. Department of Mechanical Engineering,Idaho State University,Pocatello 83209,USA
- 折叠
摘要
关键词
Compressor/Stall/Deep learning/LSTM/Hyperparameters optimizationKey words
Compressor/Stall/Deep learning/LSTM/Hyperparameters optimization引用本文复制引用
Yuyang DENG,Jichao LI,Jingyuan LIU,Feng PENG,Hongwu ZHANG,Marco P.SCHOEN..Stall prediction model based on deep learning network in axial flow compressor[J].中国航空学报（英文版）,2025,38(4):44-54,11.基金项目
This study was funded by the National Natural Science Foundation of China(No.52376039 and U24A20138),the Beijing Natural Science Foundation of China(No.JQ24017),and the National Science and Technology Major Project of China(Nos.J2019-Ⅱ-0005-0025 and Y2022-Ⅱ-0002-0005).The authors also thank the Special Fund for the Member of Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2018173). （No.52376039 and U24A20138）
