Petascale Computational Fluid Dynamics

Petascale 计算流体动力学

• 批准号: 0749152
• 负责人: Mark Shephard
• 金额: $ 100万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0749152&HistoricalAwards=false
• 关键词: Petascale; Computational; Fluid; Dynamics

This proposed research will develop an adaptive computational fluid dynamics solver that will achieve sustained petaflop performance. A mature finite element method will be paired with anisotropic adaptive meshing procedures to provide a powerful tool for attacking fluid flow problems where boundary and shear layers develop highly anisotropic solutions that can only be located and resolved through adaptivity. These flow problems can involve complicated geometries and complex physics such as fluid turbulence and multiphase interactions, resulting in discretizations so large that only petascale simulation offers the resources required for a complete solution. Two applications will be used to develop the computational methodology. These are simulation of trailing edge noise (compressible-explicit), ii) two-phase annular flow, and iii) cardiovascular simulation (both incompressible implicit); each yielding fundamental scientific insight into currently, poorly understood flow physics.Intellectual Merit.Algorithms to extend an adaptive CFD solver to petaflop performance will be developed to maintain scaling in the response to emerging architectures (multicore, CELL, clockrate, bus, cache) and networks (bandwidth, latency, communication layout/mapping, asynchronous communication). This solver and others will be enabled by developments in parallel, anisotropic adaptive meshing that affords a dramatic reduction in the elements required to accurately represent anisotropic physics. Both developments will be guided by a petascale emulator that does not perturb the heap or stack memory, fits within an MPI layer and predicts performance accurately in the presence of system faults. The proposed applications will lead to a better understanding of the complex physics of turbulent flow, noise produced by turbulent flows, multiphase flows and the interaction of mechanics and biology in the human cardiovascular system. This fundamental scientific insight will be gained from high fidelity simulations that will be the first of their kind, and will be made possible by petascale resources and algorithms.Broader Impacts.A petascale adaptive CFD solver will demonstrate that robust, open source software, applicable to a very broad range of flow physics, can sustain petaflop performance, providing a path for all continuum-based PDE solvers to follow, greatly extending our nations modeling and simulation capacity. Additionally, the petascale emulator will be extended to other classes of parallel codes. There will be an abundance of opportunities for students from diverse sub-fields to work together on the challenges proposed. The proposed petascale applications will impact critical areas of national need including energy, environment and improved patient care.

这项拟议的研究将开发一个自适应计算流体动力学求解器，将实现持续的petaflop性能。一个成熟的有限元方法将与各向异性自适应网格程序配对，以提供一个强大的工具，用于解决边界层和剪切层发展高度各向异性的解决方案，只能通过自适应定位和解决的流体流动问题。这些流动问题可能涉及复杂的几何形状和复杂的物理特性，如流体湍流和多相相互作用，导致离散化如此之大，以至于只有千万亿次模拟提供完整解决方案所需的资源。两个应用程序将被用来开发的计算方法。这些是后缘噪声的模拟（可压缩-显式），ii）两相环形流，以及iii）心血管模拟（均为不可压缩隐式）;每一个都能对目前尚不清楚的流动物理学产生基本的科学见解。智力上的优点。将开发将自适应计算流体动力学求解器扩展到千万亿次性能的算法，以保持对新兴体系结构的响应的扩展性（多核、CELL、时钟速率、总线、高速缓存）和网络（带宽、延迟、通信布局/映射、异步通信）。该求解器和其他求解器将通过并行各向异性自适应网格的开发来实现，该网格提供了精确表示各向异性物理所需的元素的显着减少。这两项开发都将由一个千万亿次模拟器指导，该模拟器不会干扰堆或堆栈内存，适合MPI层，并在系统故障存在时准确预测性能。拟议的应用程序将导致更好地了解湍流，湍流产生的噪声，多相流和人体心血管系统中的力学和生物学的相互作用的复杂物理。这一基本的科学见解将从高保真模拟中获得，这将是第一次，并将通过千万亿次的资源和算法成为可能。更广泛的影响。千万亿次自适应CFD求解器将证明，适用于非常广泛的流动物理范围的强大的开源软件可以维持千万亿次的性能，为所有基于连续介质的PDE求解器提供一条可遵循的路径，大大扩展了我们国家的建模和模拟能力。此外，千万亿次模拟器将扩展到其他类的并行代码。来自不同子领域的学生将有大量的机会共同应对所提出的挑战。拟议的千万亿次应用将影响国家需求的关键领域，包括能源、环境和改善患者护理。