Correlations adaptation for optimal emissions prediction

Tsalavoutas, A; Kelaidis, M; Thoma, N; Mathioudakis, K

dc.contributor.author	Tsalavoutas, A	en
dc.contributor.author	Kelaidis, M	en
dc.contributor.author	Thoma, N	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:51:03Z
dc.date.available	2014-03-01T02:51:03Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35332
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-34548771365&partnerID=40&md5=b29db57de115ec57b2f14369c2ed6584	en
dc.subject.other	Correlation methods	en
dc.subject.other	Data acquisition	en
dc.subject.other	Gas turbines	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Toxic materials	en
dc.subject.other	Turbojet engines	en
dc.subject.other	Industrial turbines	en
dc.subject.other	Multivariate analysis	en
dc.subject.other	Pollutants	en
dc.subject.other	Emission control	en
dc.title	Correlations adaptation for optimal emissions prediction	en
heal.type	conferenceItem	en
heal.publicationDate	2007	en
heal.abstract	An approach for estimating the pollutants emitted from a gas turbine using semi-empirical correlations is described. An extensive literature review has been carried out, in order to obtain information already available in the public domain, on the subject of pollutants emitted from turbine engines and on the effect of different parameters on them. It is shown that application of correlations in their original form does not provide a reliable estimation of emissions. Such estimation requires adaptation to the particular case studies. The possibility of adapting the considered semi-empirical correlations to available emissions measurements, through the use of optimization method is further studied. Multivariate analysis, for the establishment of generic correlations had been also applied. Results are presented and compared to the test data that derive from the dry performance of an industrial turbine and a turbojet military engine. It is demonstrated that a good predictive ability can be established. Copyright © 2007 by ASME.	en
heal.journalName	Proceedings of the ASME Turbo Expo	en
dc.identifier.volume	3	en
dc.identifier.spage	545	en
dc.identifier.epage	555	en