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Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach
Nuclear Science and Engineering ( IF 1.2 ) Pub Date : 2020-02-06 , DOI: 10.1080/00295639.2020.1712928
Chih-Wei Chang 1 , Jun Fang 2 , Nam T. Dinh 3
Affiliation  

Abstract Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-ε, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. The presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

中文翻译:

使用具有局部流动特征的深度学习进行雷诺平均湍流建模:一种经验方法

摘要 当相应的实验由于几何复杂性、预算有限或知识有限而难以实现时,雷诺平均纳维-斯托克 (RANS) 模型提供了一种预测流动特性的替代途径。RANS 模型需要亚网格尺度物理学知识来求解质量、能量和动量的守恒方程。机械湍流模型,例如 k-ε,通常针对具有不同程度不确定性的特定流动条件进行评估和校准。由于模型形式的限制,这些模型吸收大量数据的能力有限。同时,深度学习 (DL) 已被证明是具有吸收可用、相关和充分评估数据的潜力的通用逼近器。而且,深度神经网络 (DNN) 可以在不知道函数形式的情况下创建代理模型。这种数据驱动的方法可用于基于观察更新流体模型,而不是具有预校准相关性的硬接线模型。本文介绍了使用两个机器学习 (ML) 框架将 DNN 应用于雷诺应力建模的进展。提出了一种新的流特征覆盖映射来量化基于 DL 的闭包的物理覆盖。它可用于检查数据驱动湍流模型的训练数据和输入流特征的充分性。向后步骤的情况被公式化以证明 DNN 不仅可以发现流体数据背后的潜在相关性,而且还可以在 RANS 中实现它们以预测流动特性,而不会出现数值稳定性问题。
更新日期:2020-02-06
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