Self-Adaptive, Unstructured Mesh, NURBS Enhanced, Polyhedral Schemes, with Hybrid Multicore CPU and Manycore GPU Solution Algorithms, for Nuclear Reac

适用于核反应堆的自适应、非结构化网格、NURBS 增强型、多面体方案，具有混合多核 CPU 和众核 GPU 解决方案算法

• 批准号: 2738301
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2738301
• 关键词: Self; Adaptive; Unstructured; Mesh; NURBS

(a) Development of computationally efficient, numerically accurate, spatially self-adaptive NURBS-enhanced VE methods in 2D and 3D. This will build upon prior R&D work performed by the nuclear engineering group at Imperial College London.(b) Development of mathematically rigorous, dual-weighted-residual, goal-based or goal-oriented error measures using the primal (forward) and adjoint solutions of the neutron transport equation with an associated prescribed functional. This will be applied to both eigenvalue (Keff of nuclear criticality) problems as well as fixed (or prescribed) neutron source problems. In this case typical "goals" will be global quantities of interest, such as the degree of criticality (or Keff), the local fission/capture reaction rate within prescribed regions of the computational domain, or local quantities such as scalar/angular neutron flux, neutron/gamma-ray photon fluence, radiation dose or radiation damage (displacement per atom or DPA) at prescribed points within the computational domain.(c) Development of computationally efficient, massively parallel, hybrid multicore CPU/manycore GPU discrete ordinate (SN) sweep based neutron transport solvers on NURBS-enhanced unstructured polygonal/polyhedral meshes. This will take advantage of hybrid OpenMP (shared), MPI (distributed) and CUDA/OpenCL (GPU) parallel software libraries. A key aspect will be investigating what specific aspect of the discrete ordinate (SN) sweep based neutron transport solver algorithm will be more optimal in terms of the use of the multicore CPU versus use of the manycore GPU and how data can be effectively passed between these two different types of compute units as efficiently as possible for sweep algorithms.(d) A final aspect of the project will be applying the methods developed to standard multidimensional and multiscale nuclear reactor physics and reactor shielding benchmark verification test cases. These benchmark verification test cases will be used to investigate the computational efficiency, scalability and numerical accuracy of the self-adaptive, massively parallel, NURBS-enhanced virtual element methods using hybrid multicore CPU and manycore GPU workstation and HPC systems. These will include the OECD/NEA 3D C5G7 UOX/MOX colour-set nuclear reactor physics benchmark verification test case and the extended quarter-core OECD/NEA KAIST Light Water Reactor (LWR) nuclear reactor physics benchmark test case. For reactor shielding the OECD/NEA SINBAD coupled neutron and gamma-ray photon transport ASPIS and Crank-duct benchmark verification test cases will be investigated.

(A)开发计算高效、数值精确、空间自适应的NURBS增强的2D和3D VE方法。这将建立在伦敦帝国理工学院核工程小组以前进行的研发工作的基础上。(B)利用具有相关规定泛函的中子传输方程的原始(正演)和伴随解，开发数学上严格的、双重加权残差、基于目标或面向目标的误差测量。这将既适用于本征值问题(核临界系数)问题，也适用于固定(或规定)中子源问题。在这种情况下，典型的“目标”将是感兴趣的全局量，例如临界度(或Kef)，计算区域内指定区域内的局部裂变/俘获反应率，或局部量，例如标量/角中子通量、中子/伽马射线光子通量、辐射剂量或计算区域内指定点处的辐射损伤(每原子位移或DPA)。(C)在NURBS增强的非结构化多边形/多面体网格上开发计算高效、大规模并行、混合多核CPU/多核GPU离散坐标(SN)的中子输运解算器。这将利用混合OpenMP(共享)、MPI(分布式)和CUDA/OpenCL(GPU)并行软件库。一个关键方面将是研究基于离散纵坐标(SN)扫描的中子传输解算器算法的哪个特定方面在使用多核CPU与使用多核GPU方面将更加优化，以及如何在这两种不同类型的计算单元之间尽可能高效地有效地传递数据以用于扫描算法。(D)该项目的最后一个方面将是应用为标准多维和多尺度核反应堆物理和反应堆屏蔽基准验证测试案例开发的方法。这些基准验证测试案例将被用来考察使用混合多核CPU和多核GPU工作站和HPC系统的自适应、大规模并行、NURBS增强的虚拟单元方法的计算效率、可扩展性和数值精度。这些将包括OECD/NEA 3D C5G7 UOX/MOX彩色设置核反应堆物理基准验证测试案例和扩展的四分之一芯OECD/NEA KAIST轻水反应堆(LWR)核反应堆物理基准测试案例。对于反应堆屏蔽，将调查OECD/NEA辛巴德耦合中子和伽马射线光子传输ASPIS和曲柄管道基准验证测试案例。