A hybrid simulation method integrating CFD and deep learning for gas–liquid bubbly flow

被引：0
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作者：
Wen, Kaijie [1 ,2 ]
Guo, Li [1 ,2 ]
Xia, Zhaojie [1 ,2 ]
Cheng, Sibo [3 ,4 ]
Chen, Jianhua [1 ]
机构：
[1] State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing,100190, China
[2] School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing,100049, China
[3] CEREA, École des Ponts Paristech and EDF R&D, Île-de-France, France
[4] Data Science Institute, Department of Computing, Imperial College London, London,SW7 2AZ, United Kingdom
来源：
Chemical Engineering Journal | 2024年 / 495卷
关键词：
This paper develops a hybrid framework that integrates deep learning and CFD simulation for physical field prediction of bubbly flow. The framework utilizes PyTorch for constructing the deep learning model; and OpenFOAM for conducting the CFD simulations. In recursive predictions; i.e. predicting the flow field by the deep learning model successively; the prediction error increases. In contrast; the hybrid simulation mitigates error accumulation and can run for long flow time (T = 300 s) without divergence. Besides; the hybrid simulation effectively predicts the variation in bubble quantity within the calculation domain. The velocity fluctuation at the measurement point can be qualitatively captured; with reasonable fluctuation amplitude close to the prediction of Euler-Lagrange solver DPMFoam. However; there is still some error in the fluctuation period; which may be further reduced by improving the deep learning model or shortening its prediction time span. In the studied case; the hybrid simulation saves a total of 40% of the computation time compared with OpenFOAM simulation. This work provides a feasible way to combine deep learning and CFD for studies of the gas–liquid bubbly flow and more related multiphase flows; and makes contribution to the long-term accelerated simulation. © 2024 Elsevier B.V;
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