Advancing predictive models for particulate formation in turbulent flames via massively parallel direct numerical simulations

Handle URI:
http://hdl.handle.net/10754/563631
Title:
Advancing predictive models for particulate formation in turbulent flames via massively parallel direct numerical simulations
Authors:
Bisetti, Fabrizio ( 0000-0001-5162-7805 ) ; Attili, Antonio; Pitsch, Heinz G.
Abstract:
Combustion 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.
KAUST Department:
Clean Combustion Research Center; Physical Sciences and Engineering (PSE) Division; Mechanical Engineering Program; Reactive Flow Modeling Laboratory (RFML)
Publisher:
The Royal Society
Journal:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue Date:
14-Jul-2014
DOI:
10.1098/rsta.2013.0324
PubMed ID:
25024412
PubMed Central ID:
PMC4095900
Type:
Article
ISSN:
1364503X
Sponsors:
Research 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'.
Additional Links:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4095900
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorBisetti, Fabrizioen
dc.contributor.authorAttili, Antonioen
dc.contributor.authorPitsch, Heinz G.en
dc.date.accessioned2015-08-03T12:04:58Zen
dc.date.available2015-08-03T12:04:58Zen
dc.date.issued2014-07-14en
dc.identifier.issn1364503Xen
dc.identifier.pmid25024412en
dc.identifier.doi10.1098/rsta.2013.0324en
dc.identifier.urihttp://hdl.handle.net/10754/563631en
dc.description.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.en
dc.description.sponsorshipResearch 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'.en
dc.publisherThe Royal Societyen
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4095900en
dc.subjectDirect numerical simulationen
dc.subjectIntermittencyen
dc.subjectSooten
dc.subjectTurbulent combustionen
dc.titleAdvancing predictive models for particulate formation in turbulent flames via massively parallel direct numerical simulationsen
dc.typeArticleen
dc.contributor.departmentClean Combustion Research Centeren
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentMechanical Engineering Programen
dc.contributor.departmentReactive Flow Modeling Laboratory (RFML)en
dc.identifier.journalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciencesen
dc.identifier.pmcidPMC4095900en
dc.contributor.institutionInstitute for Combustion Technology, RWTH Aachen University, Aachen 52056, Germanyen
kaust.authorBisetti, Fabrizioen
kaust.authorAttili, Antonioen

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