Collaborative Research: Explicit filtering and adaptive mesh refinement for large-eddy simulation

协作研究：大涡模拟的显式滤波和自适应网格细化

• 批准号: 0933642
• 负责人: Fotini Chow
• 金额: $ 25.74万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0933642&HistoricalAwards=false
• 关键词: Collaborative; Research; Explicit; filtering; adaptive

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). 0933642/0932613 Chow/BalarasLarge-eddy simulation (LES) is one of the most promising numerical techniques for modeling complex turbulent flows in a variety of applications ranging from engineering to geophysical flows. Today, one of the main obstacles to the widespread use of LES for such applications is the lack of high-accuracy numerical techniques that efficiently accommodate complex boundary conditions. The advent of structured adaptive mesh refinement (S-AMR) solvers combined with immersed boundary methods offers new opportunities for LES since they can accommodate complex boundary conditions, efficiently distribute computational nodes and at the same time maintain most of the features of structured solvers. A combined S-AMR/LES approach, however, requires the development of filtering and modeling strategies that can deal with the complexity and flexibility inherent to adaptive grids. This study seeks to overcome these challenges and make LES a robust simulation tool for a large range of practical applications, by developing and testing explicit filtering and reconstruction approaches that allow turbulent fluctuations to accurately cross coarse-fine grid interfaces in S-AMR. Previous attempts showed significant improvement over traditional results. These developments will be easily transferrable to existing codes of respective research communities. In addition, these developments will transform LES use in engineering and geophysics by improving fundamental aspects of turbulence modeling and enabling the simulation of flow in complex geometries that were never before possible due to high numerical errors and high computational cost. The particular focus applications in this study will enable the discovery of new physics of rough-wall boundary layers and set new standards for accuracy/efficiency in computations of atmospheric contaminant dispersion in urban environments. In addition to graduate students, this project will support a postdoctoral researcherat UC Berkeley for one year. Career counseling and grant writing training will be provided through interactions with both PIs. Student class projects will help create a public interactive website to upgrade the current pages in Wikipedia on LES and adaptive mesh refinement. The Wiki websites will aid in spreading nderstanding of the limitations but also of the great potential of LES as a universal simulation tool.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。0933642/0932613 Chow/Balaras大涡模拟（LES）是在从工程到地球物理流动的各种应用中模拟复杂湍流的最有前途的数值技术之一。今天，大涡模拟广泛应用的主要障碍之一是缺乏高精度的数值技术，有效地适应复杂的边界条件。 结构化自适应网格加密（S-AMR）求解器的出现结合浸没边界方法提供了新的机会，LES，因为它们可以适应复杂的边界条件，有效地分配计算节点，并在同一时间保持结构化求解器的大部分功能。 然而，S-AMR/LES组合方法需要开发滤波和建模策略，以处理自适应网格固有的复杂性和灵活性。本研究旨在克服这些挑战，并使LES一个强大的模拟工具，为大范围的实际应用，通过开发和测试显式的过滤和重建方法，使湍流波动准确地跨越粗-细网格界面S-AMR。以前的尝试显示出比传统结果有显着改善。这些发展将很容易转移到各自的研究社区的现有代码。此外，这些发展将通过改进湍流建模的基本方面，并使复杂几何形状中的流动模拟成为可能，从而改变LES在工程和物理学中的应用，这些复杂几何形状由于高数值误差和高计算成本而从未实现过。在这项研究中的特别重点应用将使新的物理粗糙壁边界层的发现，并设置在城市环境中的大气污染物扩散计算的准确性/效率的新标准。除了研究生，这个项目将支持一个博士后研究员在加州大学伯克利分校一年。职业咨询和赠款写作培训将通过与两个PI的互动提供。学生的课堂专题将帮助建立一个公共互动网站，以更新维基百科中关于LES和自适应网格细化的当前页面。维基网站将有助于传播理解的局限性，但也有巨大的潜力LES作为一个通用的模拟工具。