计算流体动力学验证实验策略

IF 0.5 Q4 ENGINEERING, MECHANICAL
Aldo Gargiulo, Julie E Duetsch-Patel, Aurelien Borgoltz, William Devenport, Christopher J Roy, K. Todd Lowe
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引用次数: 1

摘要

摘要/ Abstract摘要:RANS/LES研究基准验证实验(BeVERLI)旨在生成具有不同分离水平的三维非平衡湍流边界层实验数据集,该数据集首次满足了计算流体动力学验证的最严格要求。在过去的二十年中,模拟和建模在高后果工程环境中的应用变得越来越突出,大大提高了模型验证的标准和要求,并迫使相应验证实验的设计发生了重大的范式转变。在本文中,基于BeVERLI项目的经验,我们提出了设计和执行验证实验的策略,希望能够轻松过渡到这个流体动力学实验的新时代,并帮助即将到来的验证实验取得成功。我们讨论了验证流程的选择、模拟和实验的协同使用、跨机构合作和工具,如模型扫描、时间相关测量以及重复和冗余测量。所提出的策略被证明可以成功地降低风险,并能够有条不紊地识别、测量、不确定度量化和描述关键流动特征、边界条件和相应的灵敏度,从而提高Oberkampf和Smith的模型验证实验的最高水平。此外,这些策略适用于估计不同测量系统的关键和难以获得的偏差误差不确定性,例如,由于空间滤波效应,粒子图像测速速度场数据的高阶统计矩的预估不足,以及系统地评估不确定性估计的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategies for Computational Fluid Dynamics Validation Experiments
Abstract The Benchmark Validation Experiment for RANS/LES Investigations (BeVERLI) aims to produce an experimental dataset of three-dimensional non-equilibrium turbulent boundary layers with various levels of separation that, for the first time, meets the most exacting requirements of computational fluid dynamics validation. The application of simulations and modeling in high-consequence engineering environments has become increasingly prominent in the past two decades, considerably raising the standards and demands of model validation and forcing a significant paradigm shift in the design of corresponding validation experiments. In this paper, based on the experiences of project BeVERLI, we present strategies for designing and executing validation experiments, hoping to ease the transition into this new era of fluid dynamics experimentation and help upcoming validation experiments succeed. We discuss the selection of a flow for validation, the synergistic use of simulations and experiments, cross-institutional collaborations, and tools, such as model scans, time-dependent measurements, and repeated and redundant measurements. The proposed strategies are shown to successfully mitigate risks and enable the methodical identification, measurement, uncertainty quantification, and characterization of critical flow features, boundary conditions, and corresponding sensitivities, promoting the highest levels of model validation experiment completeness per Oberkampf and Smith. Furthermore, the applicability of these strategies to estimating critical and difficult-to-obtain bias error uncertainties of different measurement systems, e.g., the underprediction of high-order statistical moments from particle image velocimetry velocity field data due to spatial filtering effects, and to systematically assessing the quality of uncertainty estimates is shown.
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来源期刊
CiteScore
1.60
自引率
16.70%
发文量
12
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