High-resolution fluid–particle interactions: a machine learning approach
High-resolution fluid–particle interactions: a machine learning approach
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DOI：
10.1017/jfm.2022.174
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发表时间：
2022-03
影响因子：
3.7
通讯作者：
Tsimur Davydzenka;P. Tahmasebi
中科院分区：
文献类型：
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作者：
Tsimur Davydzenka;P. Tahmasebi
摘要流体-颗粒相互作用模型是许多科学和工程领域的主要研究领域。有几种技术可以对这种相互作用进行建模，其中计算流体动力学(CFD)和离散元方法(DEM)的耦合是最方便的解决方案之一，因为它在精度和计算成本之间取得了平衡。然而，这种方法的精度在很大程度上取决于网格大小，而获得逼真的结果总是需要使用小网格，从而增加计算强度。为了弥补在这种建模中使用大网格的不准确性，并仍然利用快速计算的优势，我们扩展了经典建模，将其与机器学习模型相结合。我们已经进行了七次模拟，其中第一个是一个具有很高计算时间和精度的细网格(即地面真实)的数值模型，后三个模型是在精度和计算负担相当低的粗网格上构建的，最后三个模型是通过机器学习来辅助的，在观察细尺度特征方面我们可以获得很大的改进，但仍然基于粗网格。这项研究的结果表明，通过在合理的时间内为大规模系统生成高精度的模型，机器学习在改进经典的流体-颗粒建模方法方面有很大的机会。
Abstract Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.