首页|Data driven reduced modeling for fluidized bed with immersed tubes based on PCA and Bi-LSTM neural networks

Data driven reduced modeling for fluidized bed with immersed tubes based on PCA and Bi-LSTM neural networks

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The fast and accurate reduced-order modeling of fluidized beds is a challenging task in the field of fluid dynamics,owing to their high dimensionality and nonlinear dynamic behavior.In this study,a nonin-trusive reduced order modeling method,the reduced order model based on principal component analysis and bidirectional long short-term memory networks(PBLSTM ROM),was developed to capture complex spatio-temporal dynamics of fluidized beds.By combining principal component analysis and Bidirectional long-short-term memory networks,the PBLSTM ROM effectively extracted dynamic evo-lution information without any prior knowledge of governing equations,enabling reduced-order modeling of unsteady flow fields.The PBLSTM ROM was validated using the solid volume fraction and gas velocity flow fields of a fluidized bed with immersed tubes,showing superior performance over both the PLSTM and PANN ROMs in accurately capturing temporal changes in the fluidization fields,especially in the region near immersed tubes where severe fluctuations appear.Moreover,the PBLSTM ROM improved the simulation speed by five orders of magnitude compared to traditional computational fluid dynamics simulations.These findings suggest that the PBLSTM ROM presents a promising approach for analyzing the complex fluid flows in engineering practice.

Reduced order modelingFluidized bedDeep learningBi-LSTM

Jiabin Fang、Wenkai Cu、Huang Liu、Huixin Zhang、Hanqing Liu、Jinjia Wei、Xiang Ma、Nan Zheng

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School of Chemical Engineering and Technology,Xi'an Jiaotong University,Xi'an,Shaanxi 710049,China

National Key R&D Program of ChinaKey Research Program of Shaanxi ProvinceNational Key R&D Program of ChinaShaanxi Province Qin Chuangyuan"Scientist+Engineer"TeamTargeted Funding Program of Power Construction Corporation of China

2021YFF05004002022GXLH-01-082018YFB15010032022KXJ-179DJ-PTZX-2021-03

2024

10.1016/j.partic.2023.12.012

颗粒学报(英文版)
中国颗粒学会　中国科学院过程工程研究所

颗粒学报(英文版)

CSTPCD
影响因子：0.632
ISSN：1674-2001
年,卷(期)：2024.91(8)