Volume 38 Issue 3
Feb 2022
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JOURNAL OF MECHANICAL ENGINEERING  > 2022  >  38(3): 321187.
L. Zhu, X. Sun, Y. Liu, and W. Zhang,One neural network approach for the surrogate turbulence model in transonic flows. Acta Mech. Sin., 2022, 38, http://www.w3.org/1999/xlink' xlink:href='https://doi.org/10.1007/s10409-021-09057-z'>https://doi.org/10.1007/s10409-021-09057-z
Citation: L. Zhu, X. Sun, Y. Liu, and W. Zhang,One neural network approach for the surrogate turbulence model in transonic flows. Acta Mech. Sin., 2022, 38, http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.1007/s10409-021-09057-z">https://doi.org/10.1007/s10409-021-09057-z
shu

One neural network approach for the surrogate turbulence model in transonic flows

doi: 10.1007/s10409-021-09057-z
  • 1.

    School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China

  • 2.

    State Key Laboratory of Aerodynamics, China Aerodynamics R&D Center, Mianyang 621000, China

  • 3.

    Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027, China

Funds:

the National Numerical Wind Tunnel Project Grant

and the National Natural Science Foundation of China Grant

More Information
  • Corresponding author: Zhang Weiwei, E-mail address: aeroelastic@nwpu.edu.cn (Weiwei Zhang)
  • Accepted Date: 18 Sep 2021
    • Available Online: 01 Aug 2022
  • Publish Date: 16 Feb 2022
  • Issue Publish Date: 01 Mar 2022
    Abstract
  • With the rapid development of artificial intelligence techniques such as neural networks, data-driven machine learning methods are popular in improving and constructing turbulence models. For high Reynolds number turbulence in aerodynamics, our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions. The results calculated by the proposed model are encouraging. In this work, we aim to model the turbulence of transonic wing flows with fully connected deep neural networks, where there is less research at present. The proposed model is driven by two flow cases of the ONERA (Office National d’Etudes et de Recherches Aérospatiales) wing and coupled with the Navier-Stokes equation solver. Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance. The iteration process is stable, and final convergence is achieved. The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model. Compared with the data calculated by the Spallart-Allmaras model, the results show that the proposed model can be well generalized to the test cases. The mean relative error of the drag coefficient at different sections is below 4% for each case. This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.

     

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