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A hybrid simulation method integrating CFD and deep learning for gas–liquid bubbly flow
被引：0
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
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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