Parallel High-Order Adaptive Mesh Refinement Finite-Volume Schemes for Multi-Scale Physically-Complex Flows

多尺度物理复杂流的并行高阶自适应网格细化有限体积方案

• 批准号: RGPIN-2014-04583
• 负责人: Groth, Clinton
• 金额: $ 3.64万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566612
• 关键词: Parallel; Order; Adaptive; Mesh; Refinement

Computational fluid dynamics (CFD) has proven to be an important enabling technology in many areas of science and engineering. Despite the numerous advances, there is still a wide variety of multi-scale, physically-complex flows that remain both poorly understood and which have proven to be very challenging to predict by computational means. Such flows would include but are not limited to: (i) turbulent and reactive flows encountered in advanced aerospace propulsion, more general transportation, as well as stationary power generation systems; (ii) high-speed compressible flows of gases and conducting fluids and plasmas; and (iii) micro-scale and/or rarefied non-equilibrium flows among others. As with all multi-scale processes, the small-scale physics directly impacts the observed large-scale behaviour. In order to enable the more routine solution of multi-scale, physically-complex flows for practical engineering applications, further and rather significant advances in numerical methods and CFD algorithm design are required. The proposed research will therefore focus on the further development of a novel class or family of highly-scalable, parallel, adaptive mesh refinement (AMR), high-order, finite-volume schemes for the prediction of multi-scale, physically-complex flows on multi-block, body-fitted, unstructured, and hybrid computational meshes using new and emerging HPC architectures. The applicant's recent advances in high-order spatial discreatization procedures, anisotropic and hybrid AMR meshing strategies with local solution-dependent refinement, and efficient parallel algorithm design in the last 4-6 year period will provide the basis for the research moving forward. Key elements of the research will include: (i) the further development of isotropic and anisotropic AMR techniques for the treatment of complex geometries and interfaces using hybrid (structured and unstructured) multi-block grids where the mesh refinement is directed by adjoint-based estimates of the solution error; (ii) the enhancement and extension of high-order finite-volume spatial coupled with high-order temporal discretization schemes for improved solution accuracy on both anisotropic and hybrid AMR meshes; (iii) the development of improved parallel implicit time-marching methods using multi-level preconditioning techniques; and (iv) the design of efficient and scalable parallel methods for effective use of heterogeneous multi-core systems with floating-point accelerators. The potential, capabilities, and performance of the proposed computational tools for multi-scale, physically-complex problems will be assessed through application to the prediction of laminar and turbulent reactive flows, non-equilibrium micro-channel flows, as well as high-speed space plasma flows. It is anticipated that the results arising from the research will lead to a more than one order of magnitude improvement in efficiency when compared to CFD algorithms in current use, both in terms of computational performance and resolution capabilities. This will enable the more routine prediction of a far wider range of physically complex flows for many more practical problems. For aerospace propulsion and other transportation system applications, improved prediction of turbulent combusting flows in gas-turbine combustors would lead to improved aircraft engines with lower emissions, reduced noise output, lower fuel consumption, and less environmental impact. In particular, the proposed research will greatly enhance and find application in the applicant's on-going research partnerships and collaborations with two leading manufacturers of gas turbine engines: Pratt & Whitney Canada and Rolls-Royce Canada.

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。尽管取得了许多进展，但仍然存在各种各样的多尺度，物理复杂的流动，这些流动仍然知之甚少，并且已经证明通过计算手段进行预测非常具有挑战性。这种流动将包括但不限于：（i）在先进的航空航天推进、更一般的运输以及固定发电系统中遇到的湍流和反应流;（ii）气体和导电流体和等离子体的高速可压缩流;以及（iii）微尺度和/或稀薄非平衡流等。与所有多尺度过程一样，小尺度物理直接影响观察到的大尺度行为。为了使更多的常规解决方案的多尺度，物理复杂的流动的实际工程应用中，进一步和相当显着的进展，数值方法和CFD算法设计是必需的。因此，拟议的研究将集中于进一步开发一类或一族新型的高度可扩展、并行、自适应网格细化（AMR）、高阶、有限体积方案，用于预测多块、贴体、非结构化和混合计算网格上的多尺度、物理复杂流，使用新的和新兴的HPC架构。申请人最近在高阶空间离散化程序，各向异性和混合AMR网格化策略与局部解决方案相关的细化，以及高效的并行算法设计在过去的4-6年期间的最新进展将为研究向前发展提供基础。研究的主要内容将包括：（i）进一步发展各向同性和各向异性AMR技术，用于使用混合方法处理复杂几何形状和界面（结构化和非结构化）多块网格，其中网格细化由基于伴随的解误差估计指导;（ii）高阶有限体积空间耦合的增强和扩展，顺序时间离散计划，以提高解决方案的精度各向异性和混合AMR网格：（iii）改进的并行隐式时间推进方法，使用多级预处理技术的发展;以及（iv）设计高效和可扩展的并行方法，以有效使用具有浮点加速器的异构多核系统。将通过应用于预测层流和湍流反应流、非平衡微通道流以及高速空间等离子体流来评估所提出的用于多尺度、物理复杂问题的计算工具的潜力、能力和性能。预计从研究中产生的结果将导致一个以上的数量级的提高效率相比，目前使用的CFD算法，无论是在计算性能和分辨率能力。这将使更多的常规预测范围更广的物理复杂的流动，为许多更实际的问题。对于航空航天推进和其他运输系统应用，改进燃气涡轮燃烧室中湍流燃烧流的预测将导致改进的航空发动机具有更低的排放、更低的噪声输出、更低的燃料消耗和更小的环境影响。特别是，拟议的研究将大大加强和应用于申请人正在进行的研究伙伴关系，并与两家领先的燃气涡轮机发动机制造商合作：加拿大普惠公司和加拿大罗尔斯罗伊斯公司。