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Improving Statistical Convergence in Direct Numerical Simulations by Exploring Large-Scale Structures Organization and Symmetry

通过探索大规模结构组织和对称性来提高直接数值模拟中的统计收敛性

基本信息

批准号：
1707075
负责人：
Yulia Peet
金额：
$ 33.12万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707075&HistoricalAwards=false
关键词：
Improving Statistical Convergence Direct Numerical

项目摘要

Computational Fluid Dynamics (CFD) is used to describe, explain, and predict turbulent fluid flow externally around airplanes, rockets, and ground vehicles and internally in engines and building ventilation systems. Such computations can be expensive because of the unsteady structures in the turbulence. This project will focus on developing simulation techniques to make such computations more practical. Outreach activities through ASU community-connect infrastructure will focus on educating high-school students about turbulence and its connection to real-life applications through short tutorials and demonstrations involving advanced visualization of turbulent flows and large-scale organized motions. The goal of this project is to develop the strategies that mitigate the problem associated with the persistence of slow moving coherent structures in certain portions of the flow domain, or locking, during the finite time of numerical simulations. In these situations, the collected flow statistics can be significantly biased by the localized and temporary effect of these structures and cannot reliably represent a long-term system dynamics. The current proposal is devoted to designing simple but efficient strategies that will yield significantly improved convergence of statistics in numerical simulations over a much reduced computational time. These strategies include efficient sampling over multiple realizations and multiple states of the flow during the finite time of the simulations. This will be achieved by designing specific mathematical transformations of the computed flow field at a runtime that can be thought of as disturbances that trigger the state transitions in nature. The symmetries of the system will be explored to design the transformations that (i) amenable to promoting quick transitions into desired unsampled flow states, (ii) mathematically consistent and preserve the governing fluid dynamics equations. In addition to improving statistical convergence, the project seeks to answer the following scientific questions: (i) whether the observation of very long-lived asymmetric flow states in certain symmetric systems, such as jets in crossflow and bluff-body wakes, is an artifact of a similar phenomenon of coherent structure locking and whether the current techniques will yield symmetric average fields, (ii) whether addition of the samples from the newly reconstructed states improve a long-term prediction of the system dynamics by low-order models, such as Proper Orthogonal Decomposition. The research is coupled to the educational plan that includes outreach activities at local Phoenix district high schools, involvement of undergraduates in research, and improving graduate curriculum.

计算流体动力学（CFD）用于描述，解释和预测飞机，火箭和地面车辆外部以及发动机和建筑物通风系统内部的湍流流体流动。由于紊流中的非定常结构，这种计算可能是昂贵的。该项目将侧重于开发模拟技术，使此类计算更加实用。通过ASU社区连接基础设施开展的外联活动将侧重于教育高中学生了解湍流及其与现实生活应用的联系，方法是通过简短的教程和演示，包括湍流和大规模有组织运动的高级可视化。该项目的目标是制定策略，以缓解在有限的数值模拟时间内与流域某些部分中缓慢移动的相干结构的持续存在或锁定相关的问题。在这些情况下，收集的流量统计数据可能会受到这些结构的局部和暂时影响的严重影响，并且无法可靠地表示长期的系统动态。目前的建议是致力于设计简单，但有效的策略，将产生显着改善收敛的统计数值模拟在一个大大减少计算时间。这些策略包括在模拟的有限时间内对多个实现和流的多个状态进行有效采样。这将通过在运行时设计计算的流场的特定数学变换来实现，所述数学变换可以被认为是触发自然状态转换的扰动。系统的对称性将被探索，以设计的转换，（i）适合促进快速过渡到所需的未采样的流状态，（ii）数学上一致的，并保持控制流体动力学方程。除了改善统计趋同外，该项目还寻求回答以下科学问题：（i）在某些对称系统中观察到非常长寿命的非对称流态，例如横流和钝体尾流中的射流，是否是相干结构锁定的类似现象的人为结果，以及目前的技术是否会产生对称的平均场，（ii）来自新重构状态的样本的添加是否通过低阶模型（诸如本征正交分解）改善了系统动态的长期预测。这项研究与教育计划相结合，该计划包括在当地凤凰城地区高中开展外联活动，让本科生参与研究，并改善研究生课程。