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.

在许多科学和工程领域，计算流体动力学（CFD）已被证明是一项重要的促成技术。尽管取得了很多进步，但仍然有多种多尺度的，身体上的复杂性流动，这些流仍然鲜为人知，并且被证明是通过计算手段预测的非常具有挑战性的。这样的流将包括但不限于：（i）在高级航空航天推进，更通用的运输以及固定发电系统中遇到的动荡和反应性流动； （ii）气体的高速压缩流以及导体和等离子体； （iii）微尺度和/或稀疏的非平衡流。 与所有多尺度过程一样，小规模的物理学直接影响观察到的大规模行为。 为了实现用于实用工程应用的多尺度，物理复杂流的更常规解决方案，需要在数值方法和CFD算法设计方面进一步且相当重大的进步。 因此，拟议的研究将着重于进一步发展高度阶级，平行，适应性网状精炼（AMR），高阶，有限体积方案，以预测多尺度，物理复杂的多层次，具有多块，身体构造，无结构，无结构，杂交，计算和氢晶体的架构的多尺度，物理复杂的流量。 申请人在高阶空间裁员程序，各向异性和混合AMR网格划分策略方面的最新进展，具有依赖局部解决方案的细化，以及在过去的4 - 6年期间有效的平行算法设计，将为研究的前进提供基础。 该研究的关键要素将包括：（i）使用混合（结构化和非结构化的）多块网格对复杂几何形状和接口进行处理的各向同性和各向异性AMR技术的进一步开发，其中基于基于基于求解解决方案误差的相互作用的估计值将网格细化指向网格细化； （ii）高阶 - 有限体积空间的增强和扩展以及高阶时间离散方案，以提高各向异性和混合AMR网格的溶液精度； （iii）使用多级预处理技术的改进的平行隐式时间建设方法的发展； （iv）设计有效使用具有浮点加速器的异质多核系统的高效和可扩展的平行方法的设计。将通过应用层状和湍流反应流，非平衡微量通道流以及高速空间等离子体流量来评估针对多尺度，物理复杂问题的拟议计算工具的潜在，能力和性能。 可以预计，与当前使用中的CFD算法相比，研究结果与CFD算法相比，在计算性能和分辨率功能方面，效率的效率和分辨率能力都将导致效率的数量级不止。 这将使更广泛的物理上复杂流动的更广泛的实用问题实现更广泛的预测。 对于航空航天的推进和其他运输系统的应用，改进的燃气燃烧器中湍流燃烧流的预测将导致改进的飞机发动机，发动机较低，噪声输出减少，燃油消耗降低以及环境影响较小。 尤其是，拟议的研究将在申请人正在进行的研究合作伙伴关系以及与两个领先的燃气轮机发动机制造商的合作中大大提高并找到应用：Pratt＆Whitney Canada和Canada Rolls-Royce。