Advancing predictive models for particulate formation in turbulent flames via massively parallel direct numerical simulations
KAUST DepartmentClean Combustion Research Center
Physical Sciences and Engineering (PSE) Division
Mechanical Engineering Program
Reactive Flow Modeling Laboratory (RFML)
Permanent link to this recordhttp://hdl.handle.net/10754/563631
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AbstractCombustion of fossil fuels is likely to continue for the near future due to the growing trends in energy consumption worldwide. The increase in efficiency and the reduction of pollutant emissions from combustion devices are pivotal to achieving meaningful levels of carbon abatement as part of the ongoing climate change efforts. Computational fluid dynamics featuring adequate combustion models will play an increasingly important role in the design of more efficient and cleaner industrial burners, internal combustion engines, and combustors for stationary power generation and aircraft propulsion. Today, turbulent combustion modelling is hindered severely by the lack of data that are accurate and sufficiently complete to assess and remedy model deficiencies effectively. In particular, the formation of pollutants is a complex, nonlinear and multi-scale process characterized by the interaction of molecular and turbulent mixing with a multitude of chemical reactions with disparate time scales. The use of direct numerical simulation (DNS) featuring a state of the art description of the underlying chemistry and physical processes has contributed greatly to combustion model development in recent years. In this paper, the analysis of the intricate evolution of soot formation in turbulent flames demonstrates how DNS databases are used to illuminate relevant physico-chemical mechanisms and to identify modelling needs. © 2014 The Author(s) Published by the Royal Society.
SponsorsResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) through the Competitive Research Grant 1 (CRG-1) program. The authors acknowledge valuable support from KAUST Supercomputing Laboratory (KSL) in the form of computational time on the IBM Blue Gene/P System 'Shaheen'.
PublisherThe Royal Society
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
PubMed Central IDPMC4095900
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