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: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740489
• 关键词: adaptive; hybrid; computational; framework; study

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.

加拿大是世界上龙卷风年发生次数第二多的国家。这种灾害性天气可能造成巨大的财产损失，甚至夺走无数人的生命。虽然计算机技术的进步有望帮助龙卷风动力学的计算分析，但目前在这一研究领域发现了几个挑战。首先，在使用传统的数值方法求解基于Navier-Stokes方程的龙卷风模式时，共同的缺点在于这种流动模式的宏观性质，导致难以捕捉可靠的龙卷风动力学研究所需的更精细的流动行为。其次，商业计算流体动力学(CFD)工具被发现无法获得令人满意的复杂龙卷风现象的模拟，因为它们有限的界面经常阻止用户以理想的方式研究龙卷风的细节。第三，在处理大规模计算时，并行处理被认为是强制性的，而这项技术在专门用于龙卷风动力学研究的可用内部代码中很少被采用。为了克服这些主要挑战，本研究工作建立在基于气体动力学的格子Boltzmann模型(LBM)的基础上，这是因为其固有的算法开发的并行性和与其他先进数值工具的耦合的显著可扩展性。OpenLB是一个具有强大并行执行能力的免费开源LBM软件包，它将作为基础代码，并将针对经济和更可靠的龙卷风模拟进行以下主要新的研究任务：(1)对格子Boltzmann模式进行有限体积(FV)离散，以便消除LBM背景下的许多约束，导致使用非结构网格和隐式求解方法，这是数值研究高湍流龙卷风时的基本要求。(2)提出了一种新的基于浸没边界(IB)方法的龙卷风-建筑相互作用模式，避免了龙卷风随时间演化时计算域外边界速度的更新。(3)开发了新的IB-FV-LBM自适应网格划分工具，该工具由一种新的后验误差估值器提供支持，该估计器对应于基于大涡模拟的三维湍流模型，并将其嵌入到代码中，以提高模拟结果的可靠性。(4)建立适当的离散元模型，使空气-碎片相互作用成为可能，以便更真实地研究龙卷风动力学。将上述功能嵌入到OpenLB中，由此产生的自适应混合CFD工具将能够执行一系列大型龙卷风建造相互作用测试。对于更好地理解龙卷风的动力学和提高建筑设计的抗风能力，将获得有价值的见解和指导。通过该计划对HQP进行培训也将使加拿大未来的风能工程创新受益。