Accurate, Efficient, and Robust Adaptive Solution Methods and Models for Predicting Multi-Scale Physically-Complex Flows

用于预测多尺度物理复杂流的准确、高效、鲁棒的自适应解决方法和模型

• 批准号: RGPIN-2019-06758
• 负责人: Groth, Clinton
• 金额: $ 4.01万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743277
• 关键词: Accurate; Efficient; Robust; Adaptive; Solution

With the significant improvements in numerical methods over the last 15-20 years and correspondoing increases high-performance computing (HPC) resources, computational fluid dynamics (CFD) has become an important enabling technology in science and engineering. However, despite these advances, there remain a variety of multi-scale, physically-complex flows that are still poorly understood and have proven to be very challenging to predict by computational methods. Such flows would include but are not limited to: (i) turbulent, reactive, and multi-phase flows encountered in advanced aerospace propulsion systems; (ii) high-speed flows of gases and conducting fluids and plasmas; and (iii) micro-scale and/or rarefied non-equilibrium flows. In order to enable the more routine solution of such flows in a predictive manner, further and rather significant advances in numerical methods and CFD algorithm design are required, along with improved mathematical models for the relevant physical processes. For the latter, mathematical models that offer significant reductions in the complexity while retaining solution fidelity would be extremely desirable. The proposed research will therefore focus on the development and application of novel, accurate, efficient, and robust adaptive solution methods and models for describing multi-scale physically-complex flows using HPC architectures. Key elements of the research will include: (i) the development of output-based anisotropic adaptive mesh refinement (AMR) techniques for complex geometries and interfaces using multi-block body-fitted and hybrid grids; (ii) the enhancement of high-order finite-volume and related flux-reconstruction spatial discretization methods coupled with complementary high-order temporal discretization schemes for improved solution accuracy; (iii) the development and efficient solution of improved mathematical models based on moment closures for various transport phenomena, including non-equilibrium gaseous and plasma flows, multi-phase atomization and spray formation, the formation, oxidation, and transport of nanoscale solid soot particulates, and radiative heat transfer in participating media; and (iv) the development and exploitation of a combination of parameter estimation, data-driven, and possibly data-assimilation techniques for both assessing and improving physical models and improving simulation predictions. 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 reactive and multi-phase flows, non-equilibrium gaseous flows, as well as high-speed space plasma flows. The latter would include the simulation of space weather phenomena. The proposed research is expected to result in a more that one order of magnitude improvement in computational efficiency compared to existing methods, thereby enabling the simulation of a far wider range of flows.

在过去的15 - 20年中数值方法的显着改善以及相应的增加高性能计算（HPC）资源的相应增加，计算流体动力学（CFD）已成为科学和工程学中重要的技术。然而，尽管有这些进展，但仍然有多种多尺度的，物理复杂的流动，这些流仍然很少理解，并且被证明是通过计算方法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系统中遇到的湍流，反应性和多相流； （ii）气体的高速流以及导体和等离子体； （iii）微尺度和/或稀疏的非平衡流。为了以预测的方式实现此类流的更常规解决方案，需要进一步且相当显着的数值方法和CFD算法设计，以及改进的相关物理过程的数学模型。对于后者而言，在保留解决方案保真度的同时可显着降低复杂性的数学模型将是非常可取的。因此，拟议的研究将着重于使用HPC体系结构来描述多尺度物理复杂流的新型，准确，有效，健壮的自适应解决方案方法和模型的开发和应用。该研究的关键要素将包括：（i）使用多块体构成和混合网格的复杂几何形状和接口的基于输出的各向异性自适应网状精炼（AMR）技术； （ii）增强高阶有限体积和相关的通量重建空间离散方法以及互补的高阶时间离散化方案，以提高解决方案的准确性； （iii）基于各种运输现象的矩闭合（包括非平衡气态和等离子体流动，多相雾化和喷雾形成，形成，氧化，氧化以及纳米级固体烟灰颗粒的氧化以及在参与媒体中的放射传热的传递）的开发和有效解决方案； （iv）参数估计，数据驱动以及可能的数据融合技术的结合，用于评估和改进物理模型并改善模拟预测。将通过应用于预测反应性和多相流，非平衡气态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。后者将包括模拟太空天气现象。与现有方法相比，拟议的研究预计将导致计算效率的一个数量级提高顺序，从而实现了较大范围的流量。