CDS&E: Data Management and Visualization in Petascale Turbulent Combustion Simulation

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

CBET-1250171 PI: Peyman Givi, Univ. of Pittsburgh This award provides funding for developments of (1) a robust computational method for petascale simulation of turbulent combustion, (2) a scalable data management system for such simulations and (3) a systematic visual analysis of the generated data. The computational method will be based on the filtered density function (FDF) methodology for large eddy simulation (LES) of complex turbulent flames. The simulation will be based on a new algorithm, termed ?irregularly portioned Lagrangian Monte Carlo-Finite Difference? (IPLMCFD) which facilitate FDF simulations on massively parallel, up to petascale platforms. Data management will be provided by addressing research challenges in management of annotations, management of workflows and data archiving. Effective data visualization and analysis will be conducted through a machine learning ?feature-extraction? approach. This work crosses the disciplines of engineering and computer science and expands the state-of-the-art in high fidelity predictions of turbulent reacting flows. At the conclusion of the work, an open source LES code will be provided for use by the public. If successful, the results of this research will have a significant impact in combustion, both in gas-turbine industry and in government. It is firmly believed that LES will constitute the primary means of predictions for future design and manufacturing of combustion systems. Having it coupled with robust and versatile data management and visualization capabilities will be useful for both basic and applied research purposes. Some of the other broader impacts are through involvement of undergraduate students in research and attracting them to graduate school, K-12 outreach, involvement of high school students in research, and recruitment of students from minority and under-represented groups.