| 项目编号: | 1707075
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| 项目名称: | Improving Statistical Convergence in Direct Numerical Simulations by Exploring Large-Scale Structures Organization and Symmetry |
| 作者: | Yulia Peet
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| 承担单位: | Arizona State University
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| 批准年: | 2017
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| 开始日期: | 2017-09-01
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| 结束日期: | 2020-08-31
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| 资助金额: | 331155
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | numerical simulation
; coherent structure
; asu community-connect infrastructure
; unsteady structure
; simulation technique
; project
; statistical convergence
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| 英文摘要: | 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89127
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Yulia Peet. Improving Statistical Convergence in Direct Numerical Simulations by Exploring Large-Scale Structures Organization and Symmetry. 2017-01-01.
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