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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652520
• 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，而且已经证明通过计算手段预测非常具有挑战性。这种流动将包括但不限于：(1)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；(2)气体、导电流体和等离子体的高速可压缩流动；(3)微尺度和/或稀薄的非平衡流动等。与所有多尺度过程一样，小尺度物理直接影响观测到的大尺度行为。为了能够对实际工程应用中的多尺度、物理复杂的流动进行更常规的求解，需要在数值方法和CFD算法设计方面取得更大的进步。因此，建议的研究将集中在利用新的和新兴的HPC体系结构来进一步开发一类或一族高度可扩展的、并行的、自适应的网格加密(AMR)、高阶、有限体积格式，用于预测多块、贴体、非结构化和混合计算网格上的多尺度、物理复杂的流动。申请人在过去4-6年期间在高阶空间离散化过程、各向异性和混合AMR网格策略以及依赖于局部解的细化的各向异性和混合AMR网格策略以及高效的并行算法设计方面的最新进展将为研究的推进提供基础。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，使用混合(结构和非结构)多块网格处理复杂几何和界面，其中网格细化由基于伴随的解误差估计来指导；(Ii)增强和扩展高阶有限体积空间耦合高阶时间离散格式，以提高各向异性和混合AMR网格的解精度；(Iii)使用多层预适应技术开发改进的并行隐式时间推进方法；以及(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质多核系统。将通过应用于层流和湍流反应流、非平衡微通道流以及高速空间等离子体流的预测来评估所提议的用于多尺度、物理复杂问题的计算工具的潜力、能力和性能。预计，与目前使用的CFD算法相比，研究结果将使效率在计算性能和分辨率方面提高一个数量级以上。这将使对更多实际问题的更广泛的物理复杂流动的更常规预测成为可能。对于航空航天推进和其他运输系统的应用，改进对燃气轮机燃烧室内湍流燃烧流动的预测将导致改进的飞机发动机具有更低的排放、更低的噪声输出、更低的燃油消耗和更小的环境影响。特别是，拟议的研究将极大地增强申请者正在进行的研究伙伴关系，并在与两家领先的燃气轮机发动机制造商：普惠加拿大公司和罗尔斯-罗伊斯加拿大公司的合作中找到应用。