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: 0426787
• 负责人: Stephen Pope
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426787&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.

湍流燃烧大尺度模拟的算法。Pope，康奈尔大学Peyman Givi，汉堡大学这个合作ITR项目的重点是开发和使用创新的计算算法来模拟湍流燃烧。这是一个极端的智力挑战的主题，因为它结合了高度复杂和非线性燃烧化学与湍流的多尺度和随机方面。 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）包括使用广泛分布的数据库的检索算法（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）的计算成本可以显着降低，如果问题的维数可以减少。 两种新的降维方法正在探索和发展。 这些都是基于原像曲线和迭代泰勒级数。湍流反演算法在湍流燃烧计算中已被证明是非常有效的，并且还有许多其他成熟的应用。 这些算法的基础是重新使用直接计算成本高的数据（例如，严格常微分方程控制化学反应的解）。 在模拟早期生成的数据在模拟后期可以有效地重复使用。 这一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产生的全球数据。为了实现湍流燃烧的精确和高效模拟，采用了几种先进的模拟方法：流动采用大涡模拟（LES），以显式地表示大尺度、非定常的三维运动，亚网格尺度组分的统计分布采用联合概率密度函数（PDF），其演化方程采用拉格朗日粒子法求解;并且使用降维和存储-检索的组合来结合真实的燃烧化学。 这方面的工作的目标是制定一个全面的实施这些方法，有效地执行大规模并行系统。最后，作为一个正在进行的国际合作研讨会的一部分，模拟几个“目标火焰”，存在高质量的实验数据进行。 除了测试和演示所开发的方法外，这些模拟还用于研究物理子模型的性能，并揭示所涉及过程的物理和化学。现在，以及未来几十年，湍流燃烧是一个对社会和几个主要行业具有重大意义的话题。能源使用（在电力生产、运输、加工工业和其他地方）主要通过湍流中的燃料燃烧发生。虽然目前对燃料电池和可能重新出现的核能有很大的兴趣，但现实是燃烧技术将在几十年内保持主导地位。 有令人信服的理由来寻求改进燃烧装置，环境和经济，并且工业越来越多地将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减少的CO2排放（对于给定的输出）;同时，不断寻求降低污染物（如NO和颗粒物）的排放。随着计算机能力的不断提高和模拟逼真度的不断提高，计算机模拟已经成为设计过程的一个组成部分，其重要性将不可避免地增长。在这个项目中，正在开发计算机算法，以大大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。