Collaborative Research: Discovery in High Reynolds Number Turbulence via Advanced Tools for Petascale Simulation and Analysis

协作研究:通过用于千万亿次模拟和分析的高级工具发现高雷诺数湍流

基本信息

  • 批准号:
    0749235
  • 负责人:
  • 金额:
    $ 38.4万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-10-01 至 2013-09-30
  • 项目状态:
    已结题

项目摘要

PROPOSAL NO.: OCI - 0749223/0749209/0749235/0749286 PRINCIPAL INVESTIGATOR: P-K YEUNG /MoserINSTITUTION: Georgia Institute of Technology COLLABORATIVE RESEARCH: ENABLING DISCOVERY IN HIGH REYNOLDS NUMBER TURBULENCE VIA ADVANCED TOOLS FOR PETASCALE SIMULATION AND ANALYSIS This research will advance the science of turbulent fluid flow at high Reynolds number, by taking full advantage of emerging Petascale computing capabilities to address a number of important research questions, while setting a new standard for open-source code development in CFD. The science emphasis is on simulations at the finest grid resolution and highest Reynolds number possible, for homogeneous turbulence and inhomogeneous turbulence with one direction of spatial inhomogeneity. Elements of advanced computing will include domain decomposition techniques that scale to future Petascale systems with on million processors or more, high node-level performance making use of advanced hardware features, and enhanced capacity for storage and analysis of very large datasets. Open access to both codes and data will be provided for the research community. Turbulence is characterized by disorderly fluctuations over a wide range of scales in time and space, and is a problem of great complexity and societal and technological importance. Direct numerical simulations (DNS), in which fluctuations are computed according to exact conservation equations is an ideal application for Petascale computation, since computations of this complexity are needed to resolve the wide range of spatial and temporal scales, and because the high reliability of DNS data makes such a resource investment worthwhile. To enable PetaScale DNS (PSDNS), a powerful, flexible and extensible open-source suite of software analyzing the resulting data, for flows with no more than one direction of spatial inhomogeneity will be developed. The PSDNS suite, based on highly scalable components developed by the PIs, will be further developed for extreme parallelism. New software will perform many high Reynolds number DNS to answer pressing questions in turbulence research. These simulations and analyses will yield critical discoveries in diverse areas of turbulence research, including intermittency in turbulent dispersion, the high Reynolds number overlap layer in wall-turbulence, and local extinction and reignition in turbulent reacting flows. This research will have broad societal and economic impact through advances in turbulence research and computational science. DNS at unprecedented Reynolds numbers will impact science, engineering, society and competitiveness in such areas as mixing and dispersal of pollutants, design and drag of transportation vehicles, and efficiency and pollution in combustion processes. This activity will also impact education in high performance computing through development of materials based on these Petascale software developments. It will impact education in fluid mechanics and turbulence through materials developed from the simulations. Finally, all of this will be performed while encouraging participation at all levels by under-represented groups.
提案编号:OCI - 0749223/0749209/0749235/0749286主要研究者:P-K Yeung /Moser研究机构:格鲁吉亚理工学院合作研究:通过先进的千倍尺度模拟和分析工具实现高雷诺数湍流的发现这项研究将推进高雷诺数下湍流流体流动的科学,通过充分利用新兴的Petascale计算能力来解决一些重要的研究问题,同时为CFD中的开源代码开发设定了新的标准。科学的重点是在最好的网格分辨率和最高的雷诺数可能的模拟,均匀湍流和非均匀湍流与一个方向的空间不均匀性。高级计算的元素将包括域分解技术,这些技术可扩展到未来的Petascale系统,具有百万处理器或更多,利用高级硬件功能实现高节点级性能,以及增强存储和分析超大数据集的能力。将向研究界开放代码和数据。湍流的特点是在时间和空间的大范围尺度上的无序波动,是一个非常复杂的社会和技术重要性的问题。直接数值模拟(DNS),其中波动计算根据精确的守恒方程是一个理想的应用Petascale计算,因为这种复杂性的计算需要解决广泛的空间和时间尺度,因为DNS数据的高可靠性使得这样的资源投资是值得的。为了启用PetaScale DNS(PSDNS),将开发一个功能强大、灵活和可扩展的开源软件套件,分析所产生的数据,用于空间不均匀性不超过一个方向的流动。PSDNS套件以PI开发的高度可扩展组件为基础,将进一步开发以实现极端并行。新的软件将执行许多高雷诺数DNS,以回答湍流研究中的紧迫问题。这些模拟和分析将在湍流研究的不同领域产生重要的发现,包括湍流分散中的不稳定性,壁湍流中的高雷诺数重叠层,以及湍流反应流中的局部熄灭和重燃。这项研究将通过湍流研究和计算科学的进步产生广泛的社会和经济影响。DNS在前所未有的雷诺数将影响科学,工程,社会和竞争力等领域的污染物的混合和分散,运输车辆的设计和阻力,以及燃烧过程中的效率和污染。这项活动还将通过开发基于这些Petascale软件开发的材料来影响高性能计算的教育。它将通过模拟开发的材料影响流体力学和湍流的教育。最后,所有这些都将在鼓励代表性不足的群体参与各级活动的同时进行。

项目成果

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Amitava Majumdar其他文献

Cyberinfrastructure Usage Modalities on the TeraGrid
TeraGrid 上的网络基础设施使用方式
Thermal stability, dielectric and conductivity characteristics of 9,10-anthracene-diol-anhydride polycondensates
  • DOI:
    10.1007/bf00395581
  • 发表时间:
    1990-11-01
  • 期刊:
  • 影响因子:
    4.000
  • 作者:
    Amitava Majumdar;Mukul Biswas
  • 通讯作者:
    Mukul Biswas
Ground bounce considerations in DC parametric test generation using boundary scan
使用边界扫描生成直流参数测试时的地弹注意事项
A parallel Monte Carlo code for planar and SPECT imaging: implementation, verification and applications in /sup 131/I SPECT
用于平面和 SPECT 成像的并行蒙特卡罗代码:/sup 131/I SPECT 中的实现、验证和应用
Neuromorphic computing at scale
大规模神经形态计算
  • DOI:
    10.1038/s41586-024-08253-8
  • 发表时间:
    2025-01-22
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Dhireesha Kudithipudi;Catherine Schuman;Craig M. Vineyard;Tej Pandit;Cory Merkel;Rajkumar Kubendran;James B. Aimone;Garrick Orchard;Christian Mayr;Ryad Benosman;Joe Hays;Cliff Young;Chiara Bartolozzi;Amitava Majumdar;Suma George Cardwell;Melika Payvand;Sonia Buckley;Shruti Kulkarni;Hector A. Gonzalez;Gert Cauwenberghs;Chetan Singh Thakur;Anand Subramoney;Steve Furber
  • 通讯作者:
    Steve Furber

Amitava Majumdar的其他文献

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{{ truncateString('Amitava Majumdar', 18)}}的其他基金

Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
  • 批准号:
    2411297
  • 财政年份:
    2024
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
Category II: Exploring Neural Network Processors for AI in Science and Engineering
第二类:探索科学与工程中人工智能的神经网络处理器
  • 批准号:
    2005369
  • 财政年份:
    2020
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Cooperative Agreement
Collaborative Research: CIBR: Building Capacity for Data-driven Neuroscience Research
合作研究:CIBR:数据驱动神经科学研究能力建设
  • 批准号:
    1935749
  • 财政年份:
    2020
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
Collaborative Research: Frameworks: Designing Next-Generation MPI Libraries for Emerging Dense GPU Systems
协作研究:框架:为新兴密集 GPU 系统设计下一代 MPI 库
  • 批准号:
    1931450
  • 财政年份:
    2019
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
Promoting International Collaboration on Developing Scalable, Portable & Efficient HPC Software for Modern HPC Platforms
促进开发可扩展、便携的国际合作
  • 批准号:
    1849519
  • 财政年份:
    2018
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
SHF: Large: Collaborative Research: Next Generation Communication Mechanisms exploiting Heterogeneity, Hierarchy and Concurrency for Emerging HPC Systems
SHF:大型:协作研究:利用新兴 HPC 系统的异构性、层次结构和并发性的下一代通信机制
  • 批准号:
    1565336
  • 财政年份:
    2016
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
Bilateral BBSRC-NSF/BIO: Collaborative Research: ABI Development: Seamless Integration of Neuroscience Models and Tools with HPC - Easy Path to Supercomputing for Neuroscience
双边 BBSRC-NSF/BIO:合作研究:ABI 开发:神经科学模型和工具与 HPC 的无缝集成 - 神经科学超级计算的简单途径
  • 批准号:
    1458840
  • 财政年份:
    2015
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
BIGDATA: F: DKM: Collaborative Research: Scalable Middleware for Managing and Processing Big Data on Next Generation HPC Systems
BIGDATA:F:DKM:协作研究:用于在下一代 HPC 系统上管理和处理大数据的可扩展中间件
  • 批准号:
    1447861
  • 财政年份:
    2014
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
SHF: Large: Collaborative Research: Unified Runtime for Supporting Hybrid Programming Models on Heterogeneous Architecture.
SHF:大型:协作研究:支持异构架构上的混合编程模型的统一运行时。
  • 批准号:
    1213056
  • 财政年份:
    2012
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant
Collaborative Research: SI2-SSI: A Comprehensive Performance Tuning Framework for the MPI Stack
合作研究:SI2-SSI:MPI 堆栈的综合性能调优框架
  • 批准号:
    1147926
  • 财政年份:
    2012
  • 资助金额:
    $ 38.4万
  • 项目类别:
    Standard Grant

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