This study performs data-driven modeling of mesoscale solids stress closures for filtered two-fluid model (fTFM) in gas–particle flows via an artificial neural network (ANN) based machine learning method. The data used for developing the ANN-based predictive data-driven modeling framework is systematically filtered from fine-grid simulations. The loss function optimization result reveals that coupling two loss functions promotes more accurate predictions of the mesoscale solids stresses than using a single loss function. Further comprehensive assessments of closure markers demonstrate a systematic dependence of the mesoscale solids stresses on the filtered particle velocity and its gradient as additional anisotropic markers, instead of using the conventional isotropic filtered rate of solid phase deformation as a closure marker. An optimal three-marker mesoscale closure is thus proposed. Comparative analysis of the conventional filtered model and present three-marker model shows that the data-driven model can substantially enhance the prediction accuracy.

中文翻译：

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气体-颗粒流中过滤二流体模型的中尺度固体应力闭合的数据驱动建模

本研究通过基于人工神经网络 (ANN) 的机器学习方法对气体-颗粒流中过滤双流体模型 (fTFM) 的中尺度固体应力闭合进行数据驱动建模。用于开发基于 ANN 的预测数据驱动建模框架的数据是从细网格模拟中系统地过滤出来的。损失函数优化结果表明，与使用单个损失函数相比，耦合两个损失函数可以更准确地预测中尺度固体应力。闭合标记的进一步综合评估表明，中尺度固体应力对过滤粒子速度及其梯度的系统依赖性作为附加的各向异性标记，而不是使用固相变形的传统各向同性过滤速率作为闭合标记。因此提出了最佳的三标记中尺度闭合。传统滤波模型与现有三标记模型的对比分析表明，数据驱动的模型可以显着提高预测精度。