Verification and Validation: the Path to Predictive Scale-Resolving Simulations of Turbulence

IF 0.5 Q4 ENGINEERING, MECHANICAL
F. Pereira, Fernando Grinstein, Daniel Israel, L. Eça
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引用次数: 2

Abstract

This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRS) of turbulence, i.e., computations capable of resolving a fraction of the turbulent flow scales. Toward this end, we propose a novel but simple V&V strategy based on grid and physical resolution refinement studies that can be used even when the exact initial flow conditions are unknown, or reference data are unavailable. This is particularly relevant for transient and transitional flow problems, as well as for the improvement of turbulence models. We start by presenting a literature survey of results obtained with distinct SRS models for flows past circular cylinders. It confirms the importance of V&V by illustrating a large variability of results, which is independent of the selected mathematical model and Reynolds number. The proposed V&V strategy is then used on three representative problems of practical interest. The results illustrate that it is possible to conduct reliable verification and validation exercises with SRS models, and evidence the importance of V&V to predictive SRS of turbulence. Most notably, the data also confirm the advantages and potential of the proposed V&V strategy: separate assessment of numerical and modeling errors, enhanced flow physics analysis, identification of key flow phenomena, and ability to operate when the exact flow conditions are unknown or reference data are unavailable.
验证与验证:湍流预测尺度解析模拟的路径
这项工作研究了验证和验证(V&V)的重要性,以实现湍流的预测尺度解析模拟(SRS),即能够解析一部分湍流尺度的计算。为此,我们提出了一种基于网格和物理分辨率细化研究的新颖但简单的V&V策略,即使在确切的初始流动条件未知或参考数据不可用的情况下也可以使用。这对于瞬态和过渡流动问题以及湍流模型的改进尤其重要。我们首先介绍了一份文献综述,该综述对通过圆柱体的不同SRS模型获得的结果进行了综述。它通过说明与所选数学模型和雷诺数无关的结果的巨大可变性,证实了V&V的重要性。然后将所提出的V&V策略用于三个具有实际意义的代表性问题。结果表明,可以用SRS模型进行可靠的验证和验证,并证明V&V对湍流SRS预测的重要性。最值得注意的是,这些数据还证实了所提出的V&V策略的优势和潜力:分别评估数值和建模误差,增强流动物理分析,识别关键流动现象,以及在确切流动条件未知或参考数据不可用时进行操作的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.60
自引率
16.70%
发文量
12
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