Handle URI:
http://hdl.handle.net/10754/624841
Title:
Chemical model reduction under uncertainty
Authors:
Najm, Habib; Galassi, R. Malpica; Valorani, M.
Abstract:
We outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
5-Jan-2016
Type:
Presentation
Additional Links:
http://mediasite.kaust.edu.sa/Mediasite/Play/cf64569c53d5426d8a37d2f4b9f054331d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
Appears in Collections:
Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

Full metadata record

DC FieldValue Language
dc.contributor.authorNajm, Habiben
dc.contributor.authorGalassi, R. Malpicaen
dc.contributor.authorValorani, M.en
dc.date.accessioned2017-06-08T06:32:29Z-
dc.date.available2017-06-08T06:32:29Z-
dc.date.issued2016-01-05-
dc.identifier.urihttp://hdl.handle.net/10754/624841-
dc.description.abstractWe outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.en
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/cf64569c53d5426d8a37d2f4b9f054331d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52en
dc.titleChemical model reduction under uncertaintyen
dc.typePresentationen
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
dc.contributor.institutionSandia National Laboratories Californiaen
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