Accurate simulation of turbulent combustion is a major open problem requiring petascale computing to resolve highly nonlinear coupling of physical processes over a wide range of length and time scales. The PIs approach to develop new modeling and algorithmic approaches for this problem to tackle effectively High Performance Computing (HPC) for combustion simulation at the Petascale. The PI's approach combines three techniques: automatic algorithm parallelization, multidimensional data analysis for model reduction, and multi-scale modeling with topological analysis to connect models at different scales. The algorithm parallelization is based on an algorithmic analysis that detects dependencies among computing stages, using graph theory to detect and exploit parallelism more effectively than current algorithms. This approach is independent from and complimentary to MPI distributed parallelism and allows achieving the finer grain parallelism necessary to exploit the multi core resources available on each computing node. The PIs also plan a powerful new approach to model multiphysics flows, such as turbulent combustion that leverages direct numerical simulation (DNS) and one-dimensional turbulence (ODT) to provide surrogate 'truth sets'. High-dimensional DNS data sets, containing terabytes of data, can be analyzed to extract lower-dimensional manifolds known to exist. Techniques such as principal component analysis can identify the optimal basis for representing manifolds in this high-dimensional data. Once a basis has been identified and extracted from the data sets generated by ODT, transport equations for the variables forming the basis may be derived and solved in a large-eddy simulation (LES). The LES can then be used to generate new ODT simulations which can feed back to the LES, thereby creating a dynamic modeling approach that uses down-scale, highly resolved statistical information to construct models to be used on larger scales (LES). This modeling approach is a prime candidate for early testing on petascale systems. The researchers in this study have already demonstrated the ability to scale DNS and LES to terascale computing systems, and availability of petascale computing will directly enable these modeling approaches. Application of the algorithmic and modeling advances will be made to oxyfuel combustion of natural gas. Oxyfuel combustion is one technique to facilitate carbon capture and sequestration to mitigate carbon dioxide emissions from power plants burning fossil fuels. While application will be made to natural gas systems, the techniques and algorithms developed here will apply directly to other systems including coal and transportation fuels such as diesel and gasoline. This project will provide unique educational experiences for students, including summer internships at national laboratories. Incorporating in regular classes the lessons learned in this project will help educate the future work force. Additionally, the research will strengthen collaborations between university researchers and national laboratory staff involved in simulation and model development, who will also participate in mentoring students.
