Machine‐learning‐based surface tension model for multiphase flow simulation using particle method,International Journal for Numerical Methods in Fluids

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Machine‐learning‐based surface tension model for multiphase flow simulation using particle method
International Journal for Numerical Methods in Fluids ( IF 1.7 ) Pub Date : 2020-06-22 , DOI: 10.1002/fld.4886
Xiaoxing Liu ₁ , Koji Morita ₂ , Shuai Zhang ₃

Affiliation

Particle methods have shown their potential for simulating multiphase flows due to the convenience in capturing interfaces. However, when it comes to estimate the surface tension, calculation of the curvature of the interface remains challenging. Traditional methods are based on derivative models to estimate the curvature analytically from the particle number density or color function that marks different phases. It is difficult to estimate the curvature accurately in traditional derivative models. In this study, background cells are built up and are used to predict the curvature through machine learning. By training on a data set generated using circles of varying sizes, a relation function is found to predict the curvature from the particle distribution near the interface. Together with the enhanced schemes developed in our previous study, multiphase flows with surface tension are studied within the framework of the moving particle semi‐implicit method.

中文翻译：

基于机器学习的表面张力模型，用于使用粒子法进行多相流模拟

由于捕获接口的便利性，粒子方法显示了其模拟多相流的潜力。然而，当要估计表面张力时，界面曲率的计算仍然具有挑战性。传统方法基于导数模型，以根据标记不同相的粒子数密度或颜色函数来分析估计曲率。在传统的导数模型中很难准确估计曲率。在这项研究中，背景细胞被建立起来，并被用来通过机器学习来预测曲率。通过训练使用大小可变的圆生成的数据集，可以找到一个关系函数，根据界面附近的粒子分布预测曲率。加上我们先前研究中开发的增强方案，

更新日期：2020-06-22

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