The Role of Supercomputing in Industrial Combustion Modeling

Abstract

Currently, numerical simulation using automated parameter studies is already a key tool in discovering functional optima in complex systems such as biochemical drug design and car crash analysis. In the future, such studies of complex systems will be extremely important for the purpose of steering simulations. One such example is the optimum design and steering of computation equipment for power plants. The performance of today’s high performance computers enables simulation studies with results that are comparable to those obtained from physical experimentation. Recently, Grid technology has supported this development by providing uniform and secure access to computing resources over wide area networks (WANs), making it possible for industries to investigate large numbers of parameter sets using sophisticated optimization simulations. However, the large scale of such studies requires organized support for the submission, monitoring, and termination of jobs, as well as mechanisms for the collection of results, and the dynamic generation of new parameter sets in order to intelligently approach an optimum. In this paper, we describe a solution to these problems which we call Science Experimental Grid Laboratory (SEGL). The system defines complex workflows which can be executed in the Grid environment, and supports the dynamic generation of parameter sets.