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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759633
• 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.

加拿大是世界上每年龙卷风最多的第二个国家。这种灾难性的天气可能会造成巨大的财产损失，甚至夺去无数人的生命。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，目前在这一研究领域中发现了一些挑战。首先，当使用传统的数值方法来解决基于Navier-Stokes方程的龙卷风模型时，共同的缺点在于该流动模型的宏观性质，导致难以捕获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模拟复杂的龙卷风现象，因为他们有限的界面往往会阻止用户调查龙卷风的细节，在一个理想的方式。第三，并行处理被认为是强制性的，在处理大规模的计算，而这种技术已经很少采用在现有的内部代码专门用于龙卷风动力学研究。 为了克服这些重大挑战，这项研究工作是建立在基于气体动力学的格子玻尔兹曼模型（LBM），由于其固有的并行算法的发展和显着的可扩展性，其耦合与其他先进的数值工具。OpenLB是一个免费的开源LBM软件包，具有强大的并行执行能力，将作为基础代码，并将进行以下主要的新研究任务，旨在经济和更可靠的龙卷风模拟：（1）开发晶格玻尔兹曼模型的有限体积（FV）离散化，使得可以去除LBM上下文中的许多约束，导致使用非结构网格和隐式解方法，这在数值研究高度湍流的龙卷风风时是基本上需要的。 (2)发展一种基于浸没边界（IB）方法的龙卷风-建筑物相互作用模型，以避免龙卷风随时间演化时计算域外边界处的速度更新。(3)开发新的IB-FV-LBM自适应网格工具，该工具由与三维环境中基于LES的湍流模型相对应的新的后验误差估计器提供动力，并将其嵌入到代码中以提高模拟结果的可靠性。 (4)开发一个相关的离散单元模型，以便能够进行空气-碎片相互作用，从而对龙卷风动力学进行更现实的研究。 通过在OpenLB中嵌入上述功能，由此产生的自适应混合CFD工具将能够执行大量的龙卷风建筑交互测试。为更好地理解龙卷风动力学和提高建筑设计的抗风能力提供了宝贵的见解和指导。通过该计划对HQP的培训也将使加拿大受益于未来的风力工程创新。