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.

加拿大是世界上每年龙卷风发生次数第二多的国家。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，但目前该研究领域发现了一些挑战。首先，当使用传统的数值方法求解基于纳维-斯托克斯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，人们发现商业计算流体动力学（CFD）工具无法对复杂的龙卷风现象进行令人满意的模拟，因为它们有限的界面通常会阻止用户以理想的方式研究龙卷风细节。第三，并行处理被认为是处理大规模计算所必需的，而该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采用。 为了克服这些重大挑战，这项研究工作建立在基于气体动力学的格子玻尔兹曼模型（LBM）之上，因为它具有算法开发固有的并行性以及与其他先进数值工具耦合的显着可扩展性。 OpenLB是一个具有强大并行执行能力的免费开源LBM软件包，将作为基础代码，将开展以下旨在经济且更可靠的龙卷风模拟的重大新研究任务：（1）开发有限体积（ FV）格子玻尔兹曼模型的离散化，从而可以消除LBM环境中的许多约束，从而导致使用非结构化网格和隐式求解方法，这在对高度湍流的龙卷风进行数值计算时基本上是必需的调查了。 （2）开发一种基于龙卷风构建交互模型的创新浸没边界（IB）方法，以避免龙卷风随时间演变时更新计算域外边界的速度。 (3) 开发新的 IB-FV-LBM 自适应网格划分工具，该工具由与 3D 环境中基于 LES 的湍流模型相对应的新型后验误差估计器提供支持，并将其嵌入代码中以提高仿真结果的可靠性。 (4) 开发一个相关的离散元模型，以实现空气与碎片的相互作用，从而对龙卷风动力学进行更现实的研究。 通过 OpenLB 中嵌入的上述功能，由此产生的自适应混合 CFD 工具将能够执行大量龙卷风构建交互测试。将为更好地了解龙卷风动力学和提高建筑设计的抗风能力获得宝贵的见解和指导。通过该计划对 HQP 进行培训也将使加拿大未来的风工程创新受益。