Quantifying uncertainties in direct numerical simulations of a turbulent channel flow
Quantifying uncertainties in direct numerical simulations of a turbulent channel flow
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DOI：
10.1016/j.compfluid.2023.106108
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发表时间：
2023-11-11
影响因子：
2.8
通讯作者：
Coveney，Peter V.
中科院分区：
文献类型：
--
作者：
O'Connor，Joseph;Laizet，Sylvain;Coveney，Peter V.
直接数值模拟（DNS）为模拟湍流提供了无与伦比的细节和精度。然而，像所有的数值方法，DNS是受数值方案和输入参数（如网格分辨率）所产生的不确定性。虽然不确定性量化（UQ）技术越来越多地被用于为低保真度模型提供系统的不确定性分析，但它们在DNS中的应用仍然相对较少。有鉴于此，本文的目的是将UQ和灵敏度分析应用于低雷诺数（Re τ= 180）下典型壁面湍流通道流的DNS。为了计算DNS，Incompact 3d-一个高度可扩展的开源框架，基于高阶紧致有限差分和频谱泊松求解器-被用作黑盒求解器。随机配置用于通过Incompact 3d将输入不确定性传播到感兴趣的输出量（QOI）。为了促进非侵入式前向UQ分析，开源EasyVVUQ包用于提供计算活动的采样、预处理、执行、后处理和分析的集成功能。开展了三次独立的UQ活动。前两种方法分别考察了区域大小和数值参数（如网格分辨率、时间步长、采样时间）的影响，并采用通过张量积结合的高斯求积规则对多维输入空间进行采样。最后，第三个战役研究了维度自适应采样策略的性能，与全张量积方法相比，该策略显着降低了计算成本。该分析侧重于QOI的跨渠道统计矩，以及局部和全局敏感性分析，以评估每个QOI相对于每个单独输入的敏感性。这使得DNS的鲁棒性和灵敏度的用户定义的数值参数的壁有界湍流的评估，并提供了一个合适的范围内定义这些参数的值的指示。
Direct numerical simulation (DNS) provides unrivalled levels of detail and accuracy for simulating turbulent flows. However, like all numerical methods, DNS is subject to uncertainties arising from the numerical scheme and input parameters (eg mesh resolution). While uncertainty quantification (UQ) techniques are being employed more and more to provide a systematic analysis of uncertainty for lower-fidelity models, their application to DNS is still relatively rare. In light of this, the aim of this work is to apply UQ and sensitivity analysis to the DNS of a canonical wall-bounded turbulent channel flow at low Reynolds number (R e τ= 180). To compute the DNS, Incompact3d–a highly scalable open-source framework based on high-order compact finite differences and a spectral Poisson solver–is used as a black-box solver. Stochastic collocation is used to propagate the input uncertainties through Incompact3d to the output quantities of interest (QOIs). To facilitate the non-intrusive forward UQ analysis, the open-source EasyVVUQ package is used to provide integrated capability for sampling, pre-processing, execution, post-processing, and analysis of the computational campaign. Three separate UQ campaigns are conducted. The first two examine the effect of domain size and the numerical parameters (eg mesh resolution, time step, sample time), respectively, and adopt Gaussian quadrature rules combined via tensor products to sample the multi-dimensional input space. Finally, the third campaign investigates the performance of a dimension-adaptive sampling strategy that significantly reduces the computational cost compared to the full tensor product approach. The analysis focuses on the cross-channel statistical moments of the QOIs, as well as local and global sensitivity analyses to assess the sensitivity of each QOI with respect to each individual input. This enables an assessment of the robustness and sensitivity of DNS to the user-defined numerical parameters for wall-bounded turbulent flows, and provides an indication of suitable ranges for defining the values of these parameters.