Machine learning to assist filtered two‐fluid model development for dense gas–particle flows
Machine learning to assist filtered two‐fluid model development for dense gas–particle flows
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DOI：
10.1002/aic.16973
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发表时间：
2020-06
期刊：
影响因子：
3.7
通讯作者：
Litao Zhu;Jia-Xun Tang;Zheng‐Hong Luo
中科院分区：
文献类型：
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作者：
Litao Zhu;Jia-Xun Tang;Zheng‐Hong Luo
机器学习(ML)正在广泛的领域中经历着令人着迷的复兴。然而，将如此强大的最大似然法应用于构建次网格相间闭合却鲜有报道。为此，我们开发了两种数据驱动的最大似然策略(即人工神经网络和极端梯度增强)，以使用来自高分辨率气固两相流态化模拟的大数据来准确预测过滤后的亚格子网格阻力修正。定量评估了不同的子网格输入标记对训练预测输出的影响，并证明了三标记选择是预测不可见测试集的最佳选择。然后，我们开发了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关于鼓泡和湍流流态化床中基本流体动力学的实验结果吻合得很好。目前的最大似然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。
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.