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 frac-tions with numerically simulated unstructured time-varying data of a circulating fluidized bed.The model success-fully captured the multiscale spatiotemporal features of the fluidized bed,achieving high-efficiency dynamic predic-tion 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.
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