Multiphase Turbulence Modeling Using Sparse Regression and Gene Expression Programming
Multiphase Turbulence Modeling Using Sparse Regression and Gene Expression Programming
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DOI：
10.1080/00295450.2023.2178251
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发表时间：
2021-06
影响因子：
1.5
通讯作者：
S. Beetham;Jesse Capecelatro
中科院分区：
文献类型：
--
作者：
S. Beetham;Jesse Capecelatro
摘要 两相流中的湍流驱动着许多重要的自然和工程过程，从地球物理流到核发电。载液和分散相之间的强相间耦合阻碍了为单相流开发的经典湍流模型的使用。近年来，用于湍流闭合建模的机器学习技术呈爆炸式增长，尽管许多技术依赖于增强现有模型。在这项工作中，我们提出了一种将稀疏回归和基因表达编程（GEP）相结合的方法，从模拟数据生成封闭形式的代数模型。稀疏回归用于确定捕获物理现象所需的最小功能组集，GEP 用于自动制定系数和对操作条件的依赖性。该框架在均匀湍流气体粒子流上进行了演示，其中双向耦合产生并维持载相湍流。
Abstract Turbulence in two-phase flows drives many important natural and engineering processes, from geophysical flows to nuclear power generation. Strong interphase coupling between the carrier fluid and disperse phase precludes the use of classical turbulence models developed for single-phase flows. In recent years, there has been an explosion of machine learning techniques for turbulence closure modeling, though many rely on augmenting existing models. In this work, we propose an approach that blends sparse regression and gene expression programming (GEP) to generate closed-form algebraic models from simulation data. Sparse regression is used to determine a minimum set of functional groups required to capture the physics, and GEP is used to automate the formulation of the coefficients and dependencies on operating conditions. The framework is demonstrated on homogeneous turbulent gas-particle flows in which two-way coupling generates and sustains carrier-phase turbulence.