Collaborative Research: ITR: (ASE)-(sim+dmc): Algorithms for Large-Scale Simulations of Turbulent Combustion

合作研究：ITR：(ASE)-(sim dmc)：湍流燃烧大规模模拟算法

• 批准号: 0426857
• 负责人: Peyman Givi
• 金额: $ 50.66万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426857&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; ASE; sim+dmc

ABSTRACTAlgorithms for Large-Scale Simulation of Turbulent CombustionNSF-ITR GrantPI's: Stephen B. Pope, Cornell UniversityPeyman Givi, University of PittsburghThe focus of this collaborative ITR project is the development and use of innovative computational algorithms for the simulation of turbulent combustion. This is a topic of extreme intellectual challenge as it combines highly complex and non-linear combustion chemistry with the multi-scale and stochastic aspects of turbulence. Addressing this challenge, the four components of the project are (1) Dimension Reduction Algorithms suitable for combustion chemistry (2) Storage-Retrieval Algorithms including the use of widely-distributed databases (3) Algorithm Implementation for efficient performance on large-scale parallel systems, and (4) performance of Turbulent Combustion Simulations. In combustion (and other applications) the computational cost can be dramatically decreased if the dimensionality of the problem can be reduced. Two new approaches to dimension reduction are being explored and developed. These are based on pre-image curves and iterated Taylor series. Storage-retrieval algorithms have proved extremely effective in turbulent combustion calculations, and there are many other applications ripe for their use. The basis of these algorithms is to re-use data that are costly to compute directly (e.g., the solutions to the stiff ODE's governing chemical reactions). Data generated early in a simulation are efficiently re-used later in the simulation. This idea is extended to widely distributed computing and databases, so that data generated worldwide in all previous simulations can be used. To achieve accurate and efficient simulations of turbulent combustion, several advanced methodologies are combined: the flow is treated by large-eddy simulation (LES) so that the large-scale, unsteady, 3D motions are explicitly represented; the statistical distribution of the subgrid scale compositions is fully represented by its joint probability density function (PDF) whose evolution equation is solved by a Lagrangian particle method; and realistic combustion chemistry is incorporated using the combination of dimension reduction and storage-retrieval. The objective of this aspect of the work is to develop a comprehensive implementation of these methodologies that performs efficiently on large-scale parallel systems. Finally, as part of an ongoing international collaborative workshop, simulations are performed for several "target flames" for which there exist high-quality experimental data. In addition to testing and demonstrating the methodology developed, these simulations serve to investigate the performance of the physical sub-models, and to shed light on the physics and chemistry of the processes involved. Now, and for many decades to come, turbulent combustion is a topic of tremendous significance to society and to several major industries. Energy usage (in power production, transportation, process industry and elsewhere) occurs predominantly through the combustion of fuels in turbulent flows. While there is, appropriately, great current interest in fuel cells and the possible re-emergence of nuclear power, the reality is that combustion technologies will remain dominant for many decades. There are compelling reasons to seek improvements in combustion devices, environmental and economic, and the industry is looking increasingly to computer simulations as a means of achieving improved designs. Higher combustion efficiencies lead directly to reduced CO2 emissions (for given output); at the same time, lower emissions of pollutants such as NO and particulates are continually being sought. It is inevitable that computer simulation, already an integral part of the design process, will grow in importance, as computers continually increase in power and the fidelity of the simulations improves. In this project, computer algorithms are being developed to increase substantially our abilities to simulate combustion processes and hence to impact the design of improved combustion devices. While the focus of the project is on turbulent combustion simulations, the algorithms developed (especially for dimension reduction and storage-retrieval) have broad applicability in computational science and engineering in general.

摘要 湍流燃烧大规模模拟算法 NSF-ITR Grant 负责人：Stephen B. Pope，康奈尔大学 Peyman Givi，匹兹堡大学 该 ITR 合作项目的重点是开发和使用用于模拟湍流燃烧的创新计算算法。这是一个极具智力挑战的主题，因为它将高度复杂和非线性的燃烧化学与湍流的多尺度和随机方面结合起来。 为了应对这一挑战，该项目的四个组成部分是（1）适用于燃烧化学的降维算法（2）存储检索算法，包括使用广泛分布的数据库（3）在大规模并行系统上实现高效性能的算法实现，以及（4）湍流燃烧模拟的性能。在燃烧（和其他应用）中，如果可以降低问题的维度，则可以显着降低计算成本。 正在探索和开发两种新的降维方法。 这些基于原像曲线和迭代泰勒级数。事实证明，存储检索算法在湍流燃烧计算中极其有效，而且还有许多其他成熟的应用程序可供使用。 这些算法的基础是重复使用直接计算成本高昂的数据（例如，控制化学反应的刚性 ODE 的解）。 模拟早期生成的数据可在模拟后期有效地重复使用。 这个想法被扩展到广泛分布的计算和数据库，以便可以使用以前所有模拟中在全球范围内生成的数据。为了实现准确高效的湍流燃烧模拟，结合了几种先进的方法：通过大涡模拟（LES）处理流动，从而明确地表示大范围的、不稳定的3D运动；亚网格尺度成分的统计分布完全由其联合概率密度函数（PDF）表示，其演化方程由拉格朗日粒子法求解；结合降维和存储检索，结合现实的燃烧化学。 这方面工作的目标是开发这些方法的全面实现，以便在大规模并行系统上高效执行。最后，作为正在进行的国际合作研讨会的一部分，对存在高质量实验数据的几种“目标火焰”进行了模拟。 除了测试和演示所开发的方法之外，这些模拟还用于研究物理子模型的性能，并阐明所涉及过程的物理和化学原理。现在以及未来几十年，湍流燃烧对于社会和几个主要行业来说都是一个具有巨大意义的话题。能源的使用（在电力生产、运输、加工业和其他领域）主要通过燃料在湍流中的燃烧进行。尽管目前人们对燃料电池和核电可能重新出现的兴趣很大，但现实是燃烧技术将在未来几十年内保持主导地位。 寻求燃烧装置、环境和经济方面的改进有令人信服的理由，并且该行业越来越多地寻求计算机模拟作为实现改进设计的手段。更高的燃烧效率直接导致二氧化碳排放量减少（对于给定的输出）；与此同时，人们不断寻求降低二氧化氮和颗粒物等污染物的排放。随着计算机功能的不断增强和模拟保真度的提高，计算机模拟已经不可避免地成为设计过程中不可或缺的一部分，其重要性将日益增加。在这个项目中，正在开发计算机算法，以大幅提高我们模拟燃烧过程的能力，从而影响改进燃烧装置的设计。 虽然该项目的重点是湍流燃烧模拟，但开发的算法（特别是降维和存储检索）在计算科学和工程中具有广泛的适用性。