Fast simulations of turbulent flows based on spatiotemporal statistical information

基于时空统计信息的湍流快速模拟

• 批准号: 1337000
• 负责人: Prakash Vedula
• 金额: $ 33万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1337000&HistoricalAwards=false
• 关键词: Fast; simulations; turbulent; flows; based

1337000 VedulaThe overall goal of this project is to significantly enhance the speed, accuracy, and spatiotemporal prediction capabilities of large eddy simulations of turbulent flows using a new modeling framework. Such enhancements in prediction of large-scale eddy interactions across multiple points in space at multiple time instances are of importance in several applications including (i) statistical prediction of the path of a destructive tornado and (ii) prediction and control of jet-noise intensities at various locations away from the source. While the development of such models for turbulent flows requires a fundamental understanding of the statistical nature of spatiotemporal interactions in turbulence, sufficient knowledge in this context (e.g., regarding multi-point space-time correlations at high Reynolds numbers) is currently lacking. To address this knowledge gap for isotropic and wall-bounded turbulent flows and to develop the proposed framework based on space-time structure, the PIs will focus on achieving the following objectives: (1) conduct a comprehensive study of statistical properties of spatiotemporal fluctuations in (1D) Burgers turbulence, (3D) isotropic turbulence and turbulent channel flow configurations at high Reynolds numbers using traditional and novel computational approaches, (2) develop new, optimal turbulence models for Burgers and isotropic turbulence with highly improved prediction capabilities, based on space-time structure of turbulence, and (3) generalize the proposed framework for turbulent channel flows. The proposed framework would significantly advance the state of the art in turbulence modeling by addressing a major drawback of existing models arising from their inability to accurately capture the large-scale spatiotemporal structure of turbulent flows. The hypothesis is that the speed and accuracy of large eddy simulations can be significantly improved if the relevant subgrid-scale stress models are constructed based on information that is consistent with the underlying spatiotemporal statistics of the turbulent flow. In accordance with this hypothesis, the effects of unresolved spatial and temporal scales on the resolved scales are carefully considered in the filtered (or coarse-grained) governing equations via new subgrid scale models, based on the optimal prediction formalism, principles of error minimization, stochastic estimation, and relevant information on spatiotemporal correlations. The resulting subgrid scale models will not only attempt to preserve the spatiotemporal structure of the turbulent flow but will also enable faster simulations of turbulent flows by allowing for larger time steps in numerical simulations due to inclusion of coarse-grained temporal information contained in the space-time correlations. The proposed framework appears to be promising for fast and reliable turbulent flow simulations, as preliminary studies by the PIs demonstrated that the method was successful when applied to a broad range of other canonical nonlinear dynamical systems. Through the proposed test problems and objectives, the PIs also plan to demonstrate the utility of the proposed framework for turbulent flows and gain insights for applications for more complex flows. In terms of the broader impacts, this project is not only expected to make important contributions to the fundamental understanding of turbulence, but is also expected to have an impact on fast and reliable turbulence simulations in various scientific and engineering applications and product innovations relevant to jet noise acoustics, fluid-structure interaction, turbulence control, gas turbines, pollutant dispersion, and weather phenomena. In order to disseminate knowledge about the underlying basic concepts and research results to students from different disciplines, the PIs plan to offer a new graduate level multi-disciplinary course on turbulence simulation and reduced order modeling, with lecture notes to be made available to the general public through a website. For further impact, the PIs also propose to organize a minisymposium on recent advances in turbulence modeling at the US National Congress on Computational Mechanics to communicate the framework and results to other researchers in the field. This project will support the training of researchers including Ph.D. and undergraduate students, who will be actively recruited from underrepresented groups through Diversity Enrichment Programs at U. Oklahoma.

1337000 Vedula该项目的总体目标是使用新的建模框架显著提高湍流大涡模拟的速度、精度和时空预测能力。这种在多个时间实例处跨空间中的多个点的大尺度涡流相互作用的预测的增强在若干应用中是重要的，包括（i）破坏性龙卷风的路径的统计预测和（ii）远离源的各个位置处的喷射噪声强度的预测和控制。虽然湍流模型的开发需要对湍流中时空相互作用的统计性质有基本的理解，但在这方面的足够知识（例如，关于高雷诺数下的多点时空相关性）。为了解决各向同性和有壁湍流的知识缺口，并开发基于时空结构的拟议框架，PI将重点实现以下目标：（1）对（1D）Burgers湍流时空涨落的统计特性进行了全面的研究，（2）开发新的，基于湍流的时空结构，建立了Burgers和各向同性湍流的最优湍流模型，并大大提高了模型的预测能力;（3）推广了所提出的湍流槽道流动的框架。 所提出的框架将显着推进最先进的湍流建模解决现有模型的主要缺点所产生的无法准确捕捉大规模的时空结构的湍流。假设是，大涡模拟的速度和精度可以显着提高，如果相关的亚网格尺度应力模型的基础上构建的信息是一致的湍流的基本时空统计。根据这一假设，未解决的空间和时间尺度上的分辨率尺度的影响被仔细考虑在过滤（或粗粒度）的控制方程通过新的亚网格尺度模型，基于最佳预测形式主义，误差最小化原则，随机估计，和时空相关性的相关信息。由此产生的亚网格尺度模型将不仅试图保持湍流的时空结构，但也将使更快的湍流模拟，允许更大的时间步长，由于包含在时空相关性的粗粒度的时间信息的数值模拟。所提出的框架似乎对于快速可靠的湍流模拟很有希望，因为PI的初步研究表明，该方法在应用于广泛的其他典型非线性动力系统时是成功的。通过提出的测试问题和目标，PI还计划展示所提出的湍流框架的实用性，并获得对更复杂流动应用的见解。 就更广泛的影响而言，该项目不仅有望对湍流的基本理解做出重要贡献，而且还有望对各种科学和工程应用中的快速可靠的湍流模拟以及与射流噪声声学，流体-结构相互作用，湍流控制，燃气轮机，污染物扩散和天气现象相关的产品创新产生影响。为了向来自不同学科的学生传播有关基本概念和研究成果的知识，PI计划提供一个新的研究生水平的湍流模拟和降阶建模多学科课程，并通过网站向公众提供讲义。为了进一步发挥影响，PI还建议在美国全国计算力学大会上组织一次关于湍流建模最新进展的小型对称会议，以便将框架和结果传达给该领域的其他研究人员。该项目将支持包括博士在内的研究人员的培训。以及本科生，他们将通过美国大学的多样性丰富项目从代表性不足的群体中积极招募。俄克拉荷马州。