Machine learning (ML) is experiencing an immensely fascinating resurgence in a wide variety of fields. However, applying such powerful ML to construct subgrid interphase closures has been rarely reported. To this end, we develop two data‐driven ML strategies (i.e., artificial neural networks and eXtreme gradient boosting) to accurately predict filtered subgrid drag corrections using big data from highly resolved simulations of gas‐particle fluidization. Quantitative assessments of effects of various subgrid input markers on training prediction outputs are performed and three‐marker choice is demonstrated to be the optimal one for predicting the unseen test set. We then develop a parallel data loader to integrate this predictive ML model into a computational fluid dynamic (CFD) framework. Subsequent coarse‐grid simulations agree fairly well with experiments regarding the underlying hydrodynamics in bubbling and turbulent fluidized beds. The present ML approach provides easily extended ways to facilitate the development of predictive models for multiphase flows.

中文翻译：

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机器学习可帮助为稠密的气体-颗粒流建立过滤的双流体模型

机器学习（ML）在众多领域中正经历着令人着迷的复兴。但是，很少有报道将这种强大的ML应用于构造亚网格相间闭合。为此，我们开发了两种数据驱动的ML策略（即，人工神经网络和eXtreme梯度增强），可使用来自高度解析的气态颗粒流化模拟的大数据来准确预测过滤后的亚网格阻力校正。进行了各种子网格输入标记对训练预测输出的影响的定量评估，并证明了三标记选择是预测看不见的测试集的最佳选择。然后，我们开发一个并行数据加载器以将该预测ML模型集成到计算流体动力学（CFD）框架中。随后的粗网格模拟与有关鼓泡和湍流床中水动力的实验非常吻合。当前的ML方法提供了易于扩展的方法，以促进多相流预测模型的开发。