Solution of physically-complex flows using parallel high-order finite-volume methods and hydrid solution-adaptive meshes

使用并行高阶有限体积方法和混合溶液自适应网格求解物理复杂流

• 批准号: 228130-2009
• 负责人: Groth, Clinton
• 金额: $ 2.62万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=524193
• 关键词: Solution; physically; complex; flows; using

Computational fluid dynamics (CFD) has proven to be an important enabling technology in many areas of science and engineering. In spite of the relative maturity and widespread successes of CFD, there remain a variety of physically-complex flows, which are still not well understood and have proven to be very challenging to predict by numerical methods (e.g., turbulent and reactive flows and non-equilibrium micro-scale flows). Along with CFD algorithm development, the rapid increase in high-performance computing systems in the last 10-15 years has lead to terascale and, very recently, petascale parallel systems, ranging in size from a few thousand to hundreds of thousands of cores. These advances in computing hardware are, in turn, creating significant opportunities for CFD of physically-complex flows. Nevertheless, significant advances in numerical methods are required to fully exploit current and future computing platforms and thereby enable the more routine solution of physically-complex flows for practical engineering applications. The proposed research will focus on the development of novel parallel high-order finite-volume and hybrid adaptive mesh refinement (AMR) schemes for predicting physically-complex flows using parallel and new emerging computational architectures. Key elements of the research will include: (i) development of AMR and embedded mesh strategies for treatment of complex geometries and interfaces using hybrid (structure and unstructured) multi-block meshes; (ii) development of anisotropic mesh refinement techniques based on dual-weighted reconstruction and residual error estimates; (iii) design of efficient and scalable, two-level, coarse-fine-grain parallel methods for effective use of multi-core systems and floating-point accelerators; (iv) development of improved parallel implicit time-marching methods; and (v) enhancements of high-order finite-volume spatial discretization procedures for improved solution accuracy. The potential and performance of the proposed methodology will be demonstrated through application to the prediction of turbulent reactive flows, as well as non-equilibrium micro-channel flows.

计算流体动力学（CFD）已被证明是许多科学和工程领域的重要使能技术。 尽管CFD相对成熟并取得了广泛的成功，但仍然存在各种物理复杂的流动，这些流动仍然没有得到很好的理解，并且已经证明通过数值方法预测非常具有挑战性（例如，湍流和反应流以及非平衡微尺度流）。 沿着CFD算法的发展，在过去的10-15年中，高性能计算系统的快速增长已经导致了万亿级并行系统，最近，千万亿级并行系统，其大小从几千到几十万个核。 计算硬件的这些进步反过来又为物理复杂流的CFD创造了重要的机会。 然而，数值方法的显着进步，需要充分利用当前和未来的计算平台，从而使更多的物理复杂的流动的常规解决方案，为实际工程应用。 拟议的研究将集中在开发新的并行高阶有限体积和混合自适应网格细化（AMR）计划预测物理复杂的流动使用并行和新兴的计算架构。 研究的关键要素将包括：（i）开发AMR和嵌入式网格策略，用于使用混合动力技术处理复杂几何形状和界面。（结构和非结构）多块网格;（ii）开发基于双加权重建和残差估计的各向异性网格细化技术;（iii）设计有效和可扩展的两级粗-细粒度并行方法，以有效使用多核系统和浮点加速器;（iv）发展改良的平行隐式时间推进法;及（v）加强高阶有限体积空间离散化程序，以提高解的准确性。 所提出的方法的潜力和性能将通过应用到湍流反应流的预测，以及非平衡微通道流证明。