CDS&E: Appraisal of Subgrid Scale Closures in Reacting Turbulence via DNS Big Data

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

Design and manufacture of advanced combustion systems for both industrial and government applications is aided by direct numerical simulation (DNS) of turbulent combustion data. Such "big data" sets are so large that the data has to be either aggressively filtered at the source or discarded after a short period of time. The project employs a range of strategies and computational tools for utilizing DNS data to appraise the performance of large eddy simulation (LES) predictions in turbulent combustion. The study will pave the way for LES to become the primary means of predictions for future design and manufacturing of combustion systems, while building a data sharing infrastructure, and providing educational and outreach programs to students at all levels. The proposed research is built around a coordinated 5-element strategy for handling turbulent combustion direct numerical simulation (DNS) data sets of the order of tens to hundreds of terabytes in size. The elements include: (1) Appraisal of current LES strategies using DNS data in various flame regimes; (2) Assessment of confidence intervals of SGS closures in LES; (3) Development of a computational framework for efficient computation of filtered DNS data; (4) Development of infrastructure for broad sharing of DNS data and annotations which can be employed to appraise future SGS closures and LES predictions; and (5) Suggestion for future DNS to be conducted of flames in other (missing) regimes. The DNS big data will be collected from multiple sources and will pertain to both non-premixed and premixed (fully or partially) flames. The LES will be conducted with the aid of subgrid scale (SGS) closures that are applicable for each of the flame configurations considered in DNS. An attempt will be made to cover all of the regimes of turbulent combustion as identified in the literature and contribute further insight as to which LES prediction would work better in the different regimes. Appraisal of the SGS closures via DNS data will be invaluable for assessing the level of trust and confidence that can be placed on the closure. By integrating expertise from a team of engineers, computer scientists, and mathematicians, the study has the potential to make a significant impact in state-of-the-art high-fidelity predictions of turbulent combustion. Success of this research will have a significant impact in combustion, both in the gas-turbine industry and in government (DoD, DOE, NASA). The potential for LES to become the primary predictive tool for future design and manufacturing of combustion systems will be aided by the enhanced infrastructure, which will facilitate incorporation of future SGS closures. The study will also provide research opportunities for both graduate and undergraduate students, K-12 outreach, and recruitment of students from minority and under-represented groups. The project is co-funded by the Computational Data-Enabled Science and Engineering (CDS&E) Program.