Purpose

Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects.

Design/methodology/approach

A precursor to this methodology has already been tested in Radhakrishnan et al. (2021) for equilibrium flows using LES of channel flow data. In the current methodology, the high-fidelity data chosen for training includes direct numerical simulation of a double diffuser that has strong non-equilibrium flow regions, and LES of a channel flow. The ultimate purpose of the model is to distinguish between equilibrium and non-equilibrium regions, and to provide the appropriate wall shear stress. The ML system used for this study is gradient-boosted regression trees.

Findings

The authors show that the model can be trained to make accurate predictions for both equilibrium and non-equilibrium boundary layers. In example, the authors find that the model is very effective for corner flows and flows that involve relaminarization, while performing rather ineffectively at recirculation regions.

Originality/value

Data from relaminarization regions help the model to better understand such phenomenon and to provide an appropriate boundary condition based on that. This motivates the authors to continue the research in this direction by adding more non-equilibrium phenomena to the training data to capture recirculation as well.

中文翻译：

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涉及非平衡边界层效应的 LES 数据驱动壁建模

 目的

壁面建模大涡模拟 (LES) 是一种实用工具，通过避免近壁区域的显式解析，以较少的计算成本求解壁面边界流动。然而，它的使用仅限于具有高度非平衡效应（如分离或过渡）的流动。本研究旨在提出一种使用高保真数据和机器学习 (ML) 技术来捕获这些非平衡效应的新颖方法。

设计/方法论/途径

Radhakrishnan等人已经对这种方法的前身进行了测试。 (2021) 使用渠道流量数据的 LES 计算平衡流量。在当前的方法中，选择用于训练的高保真数据包括具有强非平衡流动区域的双扩散器的直接数值模拟以及通道流动的 LES。该模型的最终目的是区分平衡区域和非平衡区域，并提供适当的壁面剪应力。本研究使用的机器学习系统是梯度增强回归树。

 发现

作者表明，该模型可以通过训练来对平衡和非平衡边界层做出准确的预测。例如，作者发现该模型对于角流和涉及再层化的流非常有效，而在再循环区域则表现相当低效。

 原创性/价值

来自再层化区域的数据有助于模型更好地理解这种现象，并据此提供适当的边界条件。这激励作者通过在训练数据中添加更多非平衡现象来捕获再循环，继续朝这个方向进行研究。