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

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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.