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A deep learning approach using temporal-spatial data of computational fluid dynamics for fast property prediction of gas-solid fluidized bed

文献类型：期刊论文

作者Qin, Pengfei1,2; Xia, Zhaojie1,2; Guo, Li1,2,3
刊名KOREAN JOURNAL OF CHEMICAL ENGINEERING
出版日期2023
卷号40期号:1页码:57-66
关键词Gas-solid Fluidized Bed CFD Surrogate Model Deep Learning Multi-scale Convolutional Neural Network Voidage Distribution Prediction
ISSN号0256-1115
DOI10.1007/s11814-022-1340-8
英文摘要To deal with the critical issue of long computational time in practical application of computational fluid dynamics (CFD), this paper presents a new approach of deep learning for voidage prediction (DeepVP) that couples short time CFD simulations (limited CFD iterations) with the deep learning method to accelerate the 2D voidage distribution prediction for a gas-solid fluidized bed at steady state. Short time CFD simulations are first performed to obtain a sequence of voidage distribution images containing the temporal-spatial property of a gas-solid fluidized bed of the early period. A deep learning model is built to predict the voidage distribution at steady state, which is achieved by implementing multi-scale convolutional neural networks based on the sequence of voidage images. The case study results for a bubbling bed show that the voidage distribution at steady state for the bubbling bed can be predicted with comparable accuracy of conventional CFD simulations at about 1/30th computational cost. Moreover, the DeepVP method exhibits better extrapolation capability than the deep learning approach merely based on CFD condition parameters.
WOS关键词CFD ; VALIDATION ; RELEASE
资助项目National Natural Science Foundation of China[62050226] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA21030700] ; Innovation Academy for Green Manufacture, Chinese Academy of Sciences[IAGM-2019-A03]
WOS研究方向Chemistry ; Engineering
语种英语
WOS记录号WOS:000908933500005
出版者KOREAN INSTITUTE CHEMICAL ENGINEERS
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Innovation Academy for Green Manufacture, Chinese Academy of Sciences
源URL[http://ir.ipe.ac.cn/handle/122111/56914]  
通讯作者Guo, Li
作者单位1.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Chem Engn, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Innovat Acad Green Manufacture, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Qin, Pengfei,Xia, Zhaojie,Guo, Li. A deep learning approach using temporal-spatial data of computational fluid dynamics for fast property prediction of gas-solid fluidized bed[J]. KOREAN JOURNAL OF CHEMICAL ENGINEERING,2023,40(1):57-66.
APA Qin, Pengfei,Xia, Zhaojie,&Guo, Li.(2023).A deep learning approach using temporal-spatial data of computational fluid dynamics for fast property prediction of gas-solid fluidized bed.KOREAN JOURNAL OF CHEMICAL ENGINEERING,40(1),57-66.
MLA Qin, Pengfei,et al."A deep learning approach using temporal-spatial data of computational fluid dynamics for fast property prediction of gas-solid fluidized bed".KOREAN JOURNAL OF CHEMICAL ENGINEERING 40.1(2023):57-66.

入库方式： OAI收割

来源：过程工程研究所

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