Abstract Particles under fluidization conditions tend to clog and aggregate, and form meso–scale structures that significantly affect gas-solid transport phenomena. In the last decade, resolution of multi–scale particle structures has been attained by using advanced sub-grid models, such as the Energy Minimization Multi-Scale (EMMS) scheme. The current work aims to develop an ANN (Artificial Neural Network) to better resolve the effect of such structures. The ANN is developed, trained and validated using data generated by a custom-built FORTRAN code that solves the EMMS equations for a wide variety of gas-particle mixture properties (1

中文翻译：

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介绍一种用于流化颗粒的人工神经网络能量最小化多尺度拖动方案

摘要 流化条件下的颗粒容易发生堵塞和聚集，形成细观结构，显着影响气固输运现象。在过去十年中，通过使用先进的子网格模型，例如能量最小化多尺度 (EMMS) 方案，已经获得了多尺度粒子结构的分辨率。当前的工作旨在开发一种 ANN（人工神经网络）以更好地解决此类结构的影响。人工神经网络是使用定制的 FORTRAN 代码生成的数据开发、训练和验证的，该代码解决了各种气体-颗粒混合物属性的 EMMS 方程 (1