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An adaptive hybrid computational framework for study of tornado dynamics

An adaptive hybrid computational framework for study of tornado dynamics
用于研究龙卷风动力学的自适应混合计算框架
批准号:
RGPIN-2020-05294
负责人:
Cao, Jun
金额:
$1.97万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
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英文摘要
Canada ranks as the second country in the world with the most tornadoes per year. This disastrous weather may inflict colossal property damage and even take numerous human lives. While computer technology advancement promises to aid computational analysis of tornado dynamics, several challenges are currently found in this research area. First, when using conventional numerical methods to solve the Navier-Stokes equations based tornado model, the common drawback lies in the macroscopic nature of this flow model, leading to difficulties in capturing finer flow behaviors necessary for a reliable tornado dynamics study. Second, commercial computational fluid dynamics (CFD) tools were found unable to attain satisfactory simulations of complex tornado phenomena, because their limited interface often prevents the user from investigating tornado details in a desirable manner. Third, parallel processing is considered compulsory in dealing with large-scale computations while this technology has been little employed in available in-house code specifically used for the tornado dynamics study. To overcome these major challenges, this research work is built upon the gas kinetics based lattice Boltzmann model (LBM) owing to its inherent parallelizability for algorithmic development and noticeable extensibility towards its coupling with other advanced numerical tools. OpenLB, a free open-source LBM software package with strong parallel execution capabilities, will serve as the basis code, and the following major new research tasks aimed at economical and more reliable tornado simulations will be conducted: (1) Develop a finite volume (FV) discretization of the lattice Boltzmann model, so that many constraints in the LBM context can be removed, leading to the use of unstructured mesh and implicit solution methods, which are essentially demanded when a highly turbulent tornadic wind is numerically investigated. (2) Develop an innovative immersed boundary (IB) approach based tornado-building interaction model in order to avoid updating the velocity at the outer boundary of computational domain when the tornado evolves with time. (3) Develop new IB-FV-LBM adaptive meshing tools powered by a novel a posteriori error estimator corresponding to the LES based turbulence model in the 3-D context, and embed it in the code to improve the reliability of simulation results. (4) Develop a pertinent discrete element model to enable the air-debris interaction for a more realistic study of tornado dynamics. With the above features embedded in OpenLB, the resulting adaptive hybrid CFD tools will be able to perform a large series of tornado-building interaction tests. Valuable insight and guidance will be gained for better understanding tornado dynamics and improving the wind-resistant capabilities in building design. The training of HQP through this program will also benefit Canada with future wind engineering innovations.
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An adaptive hybrid computational framework for study of tornado dynamics
  • 批准号:
    RGPIN-2020-05294
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.97万
  • 财政年份:
    2021
  • 负责人:
    Cao, Jun
  • 依托单位:
An adaptive hybrid computational framework for study of tornado dynamics
  • 批准号:
    RGPIN-2020-05294
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.97万
  • 财政年份:
    2020
  • 负责人:
    Cao, Jun
  • 依托单位:
Development of Adaptive CFD Tools for Study of Tornadic Wind Field
  • 批准号:
    239167-2012
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.46万
  • 财政年份:
    2016
  • 负责人:
    Cao, Jun
  • 依托单位:
Efficient reduction of energy costs in a data center cooling system
  • 批准号:
    491957-2015
  • 项目类别:
    Engage Grants Program
  • 资助金额:
    $1.82万
  • 财政年份:
    2015
  • 负责人:
    Cao, Jun
  • 依托单位:
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