物理约束数据驱动方法在湍流模型不确定性量化中的评估
Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification
AI总结：
本文评估物理约束数据驱动方法在湍流模型不确定性量化中的应用，通过机器学习策略自动检测流动区域并估计预测置信度，以减少用户输入并提高不确定性估计的准确性。
摘要：
为了实现涡轮机械的虚拟认证过程和稳健设计，必须了解计算流体力学的不确定性边界。湍流闭合模型的公式化是雷诺平均纳维-斯托克斯模拟总体不确定性的主要来源。我们讨论了应用雷诺应力张量的物理约束特征空间微扰以解释湍流模型形式不确定性的常见做法。由于基本方法通常导致过于宽松的不确定性估计，我们扩展了最近添加机器学习策略的方法。应用数据驱动方法的动机是致力于检测容易缺乏湍流模型预测精度的流动区域。这样，任何与选择不确定性程度相关的用户输入都将变得过时。本研究特别调查了一种方法，当没有准确数据来判断预测时，该方法试图确定预测置信度的先验估计。近失速条件下NACA 4412翼型周围的流动展示了数据驱动特征空间微扰框架的成功应用。此外，我们特别强调了底层方法的目标和局限性。
英文摘要：
In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the overall uncertainty of Reynolds-averaged Navier-Stokes simulations. We discuss the common practice of applying a physics constrained eigenspace perturbation of the Reynolds stress tensor in order to account for the model form uncertainty of turbulence models. Since the basic methodology often leads to overly generous uncertainty estimates, we extend a recent approach of adding a machine learning strategy. The application of a data-driven method is motivated by striving for the detection of flow regions, which are prone to suffer from a lack of turbulence model prediction accuracy. In this way any user input related to choosing the degree of uncertainty is supposed to become obsolete. This work especially investigates an approach, which tries to determine an a priori estimation of prediction confidence, when there is no accurate data available to judge the prediction. The flow around the NACA 4412 airfoil at near-stall conditions demonstrates the successful application of the data-driven eigenspace perturbation framework. Furthermore, we especially highlight the objectives and limitations of the underlying methodology.
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