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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638785
• 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支持技术。尽管取得了许多进展，但仍然存在各种各样的多尺度、物理复杂的流体，人们对这些流体的理解仍然很差，而且通过计算手段预测这些流体非常具有挑战性。这些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无功流动；（ii）气体、导电流体和等离子体的高速可压缩流动；（iii）微尺度和/或稀薄的非平衡流等。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。为了使多尺度、物理复杂的流动能够在实际工程应用中得到更常规的求解，需要在数值方法和CFD算法设计方面取得进一步的重大进展。因此，拟议的研究将集中于进一步开发一种新型或系列的高可扩展，并行，自适应网格细化（AMR），高阶，有限体积方案，用于预测多块，体合，非结构化和混合计算网格上的多尺度，物理复杂的流动，使用新的和新兴的HPC架构。申请人近4 ~ 6年在高阶空间离散化过程、局部解相关细化的各向异性和混合AMR网格策略、高效并行算法设计等方面的最新进展，将为今后的研究提供基础。研究的关键要素将包括：(i)进一步发展各向同性和各向异性AMR技术，用于使用混合（结构化和非结构化）多块网格处理复杂的几何形状和界面，其中网格细化由基于随结点的解误差估计指导；（ii）在各向异性和混合AMR网格上增强和扩展高阶有限体积空间耦合高阶时间离散化方案，以提高求解精度；（iii）利用多级预处理技术开发改进的并行隐式时间推进方法；（iv）设计高效和可扩展的并行方法，以有效地使用具有浮点加速器的异构多核系统。所提出的计算工具在多尺度、物理复杂问题上的潜力、能力和性能将通过应用于层流和湍流反应流动、非平衡微通道流动以及高速空间等离子体流动的预测来评估。预计与目前使用的CFD算法相比，研究结果将导致效率提高一个数量级以上，无论是在计算性能还是分辨率方面。这将使更常规的预测更广泛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，改进燃气轮机燃烧室湍流燃烧流的预测将导致改进的飞机发动机具有更低的排放、更低的噪音输出、更低的燃料消耗和更少的环境影响。特别值得一提的是，拟议的研究将大大加强申请人与两家领先的燃气涡轮发动机制造商：加拿大普惠公司和加拿大罗尔斯·罗伊斯公司正在进行的研究伙伴关系和合作，并在其中找到应用。