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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (4): 524779-524779.doi: 10.7527/S1000-6893.2020.24779

• Review • Previous Articles     Next Articles

Progress of deep learning modeling technology for fluid mechanics

WANG Yixing1,2,3, HAN Renkun2, LIU Ziyang1, ZHANG Yang1,2, CHEN Gang1,2   

  1. 1. State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China;
    2. Shaanxi Key Laboratory for Environment and Control of Flight Vehicle, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
    3. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China
  • Received:2020-09-22 Revised:2020-10-09 Published:2020-11-13
  • Supported by:
    National Natural Science Foundation of China (11872293); National Defense Science and Technology Foundation of Key Laboratory (6142004190307)

Abstract: Deep learning technology has brought subversive changes in many fields, such as image processing, language translation, disease diagnosis, and game competition. Due to the characteristics of high dimensionality, strong nonlinearity and large amount of data, fluid mechanics is an important area where deep learning is good at and could bring out innovation in research paradigm. At present, the deep learning technology has been initially applied in the field of fluid mechanics, and its application potential has been gradually confirmed. Based on the deep learning technology for fluid mechanics and the recent research results of our group, this paper discusses the deep learning modeling technology for fluid mechanics and its latest progress. First, the basic theory of the deep learning technology is introduced, and the mathematics behind the deep learning methods commonly used in fluid mechanics modeling are explained. Then, the progress of the deep learning technology involved in several typical application scenarios of artificial intelligence of fluid mechanics, such as basic control equation, flow field reconstruction, and feature modeling and application, are introduced. Finally, the challenges and future development trend of the deep learning modeling technology of fluid mechanics are discussed.

Key words: deep learning, fluid mechanics, reduced order method, flow field reconstruction, extraction of geometry information, modeling of nonlinear system

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