Consistent models for large eddy simulation on anisotropic grids

各向异性网格上大涡模拟的一致模型

基本信息

  • 批准号:
    1804825
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-06-01 至 2022-05-31
  • 项目状态:
    已结题

项目摘要

Turbulence is an important phenomenon in many natural and man-made systems. Our ability to make accurate predictions of turbulent flows is important in a wide range of scientific and engineering disciplines, including atmospheric flows, aerospace, naval, automotive, wind and nuclear energy, etc. In all these fields, we rely on supercomputers to 'simulate' turbulence in order to predict tomorrow's weather, the lift and drag of a future airplane design, and so on. These types of turbulence simulations solve a set of mathematical equations on a grid of smaller cells. In the vast majority of applications, these cells are anisotropic, i.e., they have different lengths in different directions. In contrast, the vast majority of the underlying mathematical and physical theory upon which these turbulence simulations are based was developed for the assumption of isotropic (i.e., perfectly cubic) cells. This project is aimed at reconciling this gap, which is expected to lead to more accurate and trustworthy turbulence simulations across the wide range of disciplines in which they are used.The goal of this project is to develop models for turbulence-resolving simulations (mainly, large eddy simulations) that behave consistently and accurately on anisotropic grids. Consistency is defined here as having the right behavior in the limits of increasing anisotropies, while accuracy is defined as making effective and efficient use of the given grid resolution. Models of both eddy-viscosity and tensorial forms will be considered. A key component of the project is to perform a comprehensive and conclusive assessment of how different grid anisotropies affect the accuracy, and to communicate this to developers and practitioners in the field. This should raise awareness and hopefully stimulate further work throughout the field, beyond the planned model developments in the present project. The assessment will be performed on multiple canonical flows, including channel flow, a temporally developing mixing layer, and a transitional boundary layer. Crucially, the assessment will be performed using three different numerical codes, to ensure the robustness of the findings and avoid code-specific conclusions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
湍流是许多自然和人为系统中的重要现象。我们对湍流做出准确预测的能力在广泛的科学和工程学科中非常重要,包括大气流动、航空航天、海军、汽车、风能和核能等。在所有这些领域,我们都依靠超级计算机来“模拟”湍流,以预测明天的天气、未来飞机设计的升力和阻力等等。这些类型的湍流模拟解决了一组小单元网格上的数学方程。在绝大多数应用中,这些细胞是各向异性的,即它们在不同方向上具有不同的长度。相比之下,这些湍流模拟所依据的绝大多数基础数学和物理理论都是基于各向同性(即完全立方)的假设而发展起来的。该项目旨在弥补这一差距,预计将在广泛的学科中产生更准确、更可靠的湍流模拟。本项目的目标是开发在各向异性网格上表现一致和准确的湍流解析模拟(主要是大涡模拟)模型。一致性在这里被定义为在增加各向异性的限制下具有正确的行为,而准确性被定义为有效和高效地使用给定的网格分辨率。涡旋黏度和张量两种形式的模型都将被考虑。该项目的一个关键组成部分是对不同网格各向异性如何影响准确性进行全面和结论性的评估,并将其传达给该领域的开发人员和实践者。这将提高人们的认识,并有望刺激整个领域的进一步工作,超出本项目计划的模式发展。评估将在多个典型流上进行,包括通道流、暂时发展的混合层和过渡边界层。至关重要的是,评估将使用三种不同的数字代码进行,以确保结果的稳健性并避免特定代码的结论。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Assessment of Grid Anisotropy Effects on Large-Eddy-Simulation Models with Different Length Scales
不同长度尺度大涡模拟模型的网格各向异性效应评估
  • DOI:
    10.2514/1.j059576
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Schumann, Jan-Erik;Toosi, Siavash;Larsson, Johan
  • 通讯作者:
    Larsson, Johan
Towards systematic grid selection in LES: Identifying the optimal spatial resolution by minimizing the solution sensitivity
LES 中的系统网格选择:通过最小化解灵敏度来确定最佳空间分辨率
  • DOI:
    10.1016/j.compfluid.2020.104488
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Toosi, Siavash;Larsson, Johan
  • 通讯作者:
    Larsson, Johan
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Johan Larsson其他文献

Area-Proportional Euler and Venn Diagrams with Ellipses [R package eulerr version 6.1.0]
带椭圆的面积比例欧拉图和维恩图 [R 包 eulerr 版本 6.1.0]
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Johan Larsson
  • 通讯作者:
    Johan Larsson
HLPW-4/GMGW-3: Wall-Modeled LES and Lattice-Boltzmann Technology Focus Group Workshop Summary
HLPW-4/GMGW-3:墙建模 LES 和格子玻尔兹曼技术焦点小组研讨会总结
  • DOI:
    10.2514/6.2022-3294
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    C. Kiris;A. Ghate;O. M. Browne;J. Slotnick;Johan Larsson
  • 通讯作者:
    Johan Larsson
The Strong Screening Rule for SLOPE
SLOPE 的强筛选规则
CaLES: A GPU-accelerated solver for large-eddy simulation of wall-bounded flows
CaLES:一种用于壁面受限流动大涡模拟的GPU加速求解器
  • DOI:
    10.1016/j.cpc.2025.109546
  • 发表时间:
    2025-05-01
  • 期刊:
  • 影响因子:
    3.400
  • 作者:
    Maochao Xiao;Alessandro Ceci;Pedro Costa;Johan Larsson;Sergio Pirozzoli
  • 通讯作者:
    Sergio Pirozzoli
Enabling large eddy simulations of realistic turbulent flows
实现真实湍流的大涡流模拟
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Johan Larsson
  • 通讯作者:
    Johan Larsson

Johan Larsson的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Johan Larsson', 18)}}的其他基金

Conference: 2024 Summer Research School on Fluid Dynamics: Topics in Turbulence
会议:2024 年夏季流体动力学研究学校:湍流主题
  • 批准号:
    2330793
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
2018 Summer Research School on Fluid Dynamics: Topics in Turbulence
2018 暑期流体动力学研究学校:湍流主题
  • 批准号:
    1802138
  • 财政年份:
    2018
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CAREER: Non-equilibrium wall-bounded turbulent flows at high Reynolds numbers
职业:高雷诺数下的非平衡壁面湍流
  • 批准号:
    1453633
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

相似国自然基金

Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    合作创新研究团队
河北南部地区灰霾的来源和形成机制研究
  • 批准号:
    41105105
  • 批准年份:
    2011
  • 资助金额:
    25.0 万元
  • 项目类别:
    青年科学基金项目
保险风险模型、投资组合及相关课题研究
  • 批准号:
    10971157
  • 批准年份:
    2009
  • 资助金额:
    24.0 万元
  • 项目类别:
    面上项目
RKTG对ERK信号通路的调控和肿瘤生成的影响
  • 批准号:
    30830037
  • 批准年份:
    2008
  • 资助金额:
    190.0 万元
  • 项目类别:
    重点项目
新型手性NAD(P)H Models合成及生化模拟
  • 批准号:
    20472090
  • 批准年份:
    2004
  • 资助金额:
    23.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: Conference: Large Language Models for Biological Discoveries (LLMs4Bio)
合作研究:会议:生物发现的大型语言模型 (LLMs4Bio)
  • 批准号:
    2411529
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: Conference: Large Language Models for Biological Discoveries (LLMs4Bio)
合作研究:会议:生物发现的大型语言模型 (LLMs4Bio)
  • 批准号:
    2411530
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Investigating the potential for developing self-regulation in foreign language learners through the use of computer-based large language models and machine learning
通过使用基于计算机的大语言模型和机器学习来调查外语学习者自我调节的潜力
  • 批准号:
    24K04111
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Multi-agent Self-improving of Large Language Models (LLMs)
大型语言模型 (LLM) 的多智能体自我改进
  • 批准号:
    2903811
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Studentship
Integrating Large Language Models for Long Horizon Task Planning in Multi-robot Scenarios
集成大型语言模型以实现多机器人场景中的长期任务规划
  • 批准号:
    24K07399
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Tuning Large language models to read biological literature
调整大型语言模型以阅读生物文献
  • 批准号:
    BB/Y514032/1
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Research Grant
CAREER: Theoretical foundations for deep learning and large-scale AI models
职业:深度学习和大规模人工智能模型的理论基础
  • 批准号:
    2339904
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
CAREER: Regularizing Large Language Models for Safe and Reliable Program Generation
职业:规范大型语言模型以安全可靠地生成程序
  • 批准号:
    2340408
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
Conference: New horizons in language science: large language models, language structure, and the neural basis of language
会议:语言科学的新视野:大语言模型、语言结构和语言的神经基础
  • 批准号:
    2418125
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Sediment connectivity in large landslides based on quality-maximized digital elevation models derived from historical aerial photography and UAV imagery
基于源自历史航空摄影和无人机图像的质量最大化数字高程模型的大型滑坡中的沉积物连通性
  • 批准号:
    24K04397
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了