Title:
Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations
Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations
| dc.contributor.advisor | Menon, Suresh | |
| dc.contributor.author | Sen, Baris Ali | en_US |
| dc.contributor.committeeMember | Lieuwen, Timothy C. | |
| dc.contributor.committeeMember | Sankar, Lakshmi N. | |
| dc.contributor.committeeMember | Stoesser, Thorsten | |
| dc.contributor.committeeMember | Syed, Saadat | |
| dc.contributor.committeeMember | Walker, Mitchell L. | |
| dc.contributor.department | Aerospace Engineering | en_US |
| dc.date.accessioned | 2010-01-29T19:47:35Z | |
| dc.date.available | 2010-01-29T19:47:35Z | |
| dc.date.issued | 2009-08-17 | en_US |
| dc.description.abstract | Two new models to calculate the species instantaneous and filtered reaction rates for multi-step, multi-species chemical kinetics mechanisms are developed based on the artificial neural networks (ANN) approach. The proposed methodologies depend on training the ANNs off-line on a thermo-chemical database representative of the actual composition and turbulence level of interest. The thermo-chemical database is constructed by stand-alone linear eddy mixing (LEM) model simulations under both premixed and non-premixed conditions, where the unsteady interaction of turbulence with chemical kinetics is included as a part of the training database. In this approach, the information regarding the actual geometry of interest is not needed within the LEM computations. The developed models are validated extensively on the large eddy simulations (LES) of (i) premixed laminar-flame-vortex-turbulence interaction, (ii) temporally mixing non-premixed flame with extinction-reignition characteristics, and (iii) stagnation point reverse flow combustor, which utilizes exhaust gas re-circulation technique. Results in general are satisfactory, and it is shown that the ANN provides considerable amount of memory saving and speed-up with reasonable and reliable accuracy. The speed-up is strongly affected by the stiffness of the reduced mechanism used for the computations, whereas the memory saving is considerable regardless. | en_US |
| dc.description.degree | Ph.D. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/31757 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Tabulation | en_US |
| dc.subject | Linear eddy mixing | en_US |
| dc.subject | Turbulent combustion modeling | en_US |
| dc.subject | Chemical kinetics | en_US |
| dc.subject | Large eddy simulation | en_US |
| dc.subject.lcsh | Turbulence | |
| dc.subject.lcsh | Eddies | |
| dc.subject.lcsh | Combustion | |
| dc.title | Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations | en_US |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Menon, Suresh | |
| local.contributor.corporatename | College of Engineering | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.relation.ispartofseries | Doctor of Philosophy with a Major in Aerospace Engineering | |
| relation.isAdvisorOfPublication | 67d13e49-1e1d-4ce9-ac87-8f1a49266904 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isSeriesOfPublication | f6a932db-1cde-43b5-bcab-bf573da55ed6 |
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