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

NSF-ITR GrantPI‘s：Stephen B.Pope，康奈尔大学Peyman Givi，匹兹堡大学这个合作的ITR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话题，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在一起。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存储-检索算法(3)在大规模并行系统上的高效算法实现，以及(4)湍流燃烧模拟的性能。在燃烧(和其他应用)中，如果能够降低问题的维度，则计算成本可以大大降低。目前正在探索和开发两种新的降维方法。这些都是基于前像曲线和迭代的泰勒级数。存储-检索算法已被证明在湍流燃烧计算中非常有效，还有许多其他应用程序已经成熟可以使用。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，僵硬的颂歌控制化学反应的解决方案)。在模拟早期生成的数据在稍后的模拟中被有效地重复使用。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在全球范围内产生的数据。为了实现准确和高效的湍流燃烧模拟，结合了几种先进的方法：采用大涡模拟(LES)来处理流动，以显式地表示大尺度、非定常的三维运动；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)来表示，其演化方程用拉格朗日粒子方法求解；真实的燃烧化学采用降维和存储-恢复相结合的方法。这方面工作的目标是开发在大规模并行系统上有效执行的这些方法的全面实施。最后，作为正在进行的国际合作讲习班的一部分，对存在高质量实验数据的几个“目标火焰”进行了模拟。除了测试和演示所开发的方法外，这些模拟还用于调查物理子模型的性能，并阐明所涉及的过程的物理和化学。现在和未来几十年，湍流燃烧是一个对社会和几个主要行业都具有巨大意义的话题。能源使用(在电力生产、运输、流程工业和其他领域)主要是通过在湍流中燃烧燃料来实现的。虽然目前人们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，燃烧技术在未来几十年仍将占据主导地位。有令人信服的理由寻求在环境和经济方面改进燃烧设备，该行业越来越多地将计算机模拟作为实现改进设计的一种手段。更高的燃烧效率直接导致二氧化碳排放量的减少(对于给定的产量)；同时，人们一直在寻求更低的污染物排放，如NO和颗粒物。计算机模拟已经是设计过程中不可或缺的一部分，随着计算机能力的不断增强和模拟逼真度的提高，计算机模拟的重要性将不可避免地增加。在这个项目中，正在开发计算机算法，以显著提高我们模拟燃烧过程的能力，从而影响改进的燃烧装置的设计。虽然该项目的重点是湍流燃烧模拟，但所开发的算法(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。

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.