Spatiotemporal prediction method for the transient multiphase flow field via graph neural network

被引：0

作者：

Hao, Yichen ^{[1
]}

Xie, Xinyu ^{[1
]}

Ding, Jiaqi ^{[1
]}

Xie, Rong ^{[1
]}

Wang, Xiaofang ^{[1
]}

Liu, Haitao ^{[1
]}

机构：

[1] College of Energy and Power, Dalian University of Technology, Dalian

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2024年 / 45卷 / 09期

关键词：

circulating fluidized bed; deep learning; graph neural network; multiphase flow; multiscale feature; spatiotemporal prediction; transient flow field; unstructured mesh;

D O I：

10.11990/jheu.202207004

中图分类号：

学科分类号：

摘要：

This study aims to rapidly build a spatiotemporal model and predict transient multiphase flow fields within large-energy and chemical equipment (e. g., circulating fluidized beds). Herein, a deep learning model based on the graph neural network was developed. It established a spatiotemporal predictor for discrete phase volume fractions with numerically simulated unstructured time-varying data of a circulating fluidized bed. The model successfully captured the multiscale spatiotemporal features of the fluidized bed, achieving high-efficiency dynamic prediction of the spatiotemporal coupling of the multiphase flow field. The result showed that, compared with traditional numerical simulation, the data-driven model ran significantly faster, with a speedup ratio close to 500. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.

引用

页码：1761 / 1769

页数：8

共 20 条

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