Title:
Probabilistic Matching of Turbofan Engine Performance Models to Test Data

dc.contributor.author Roth, Bryce Alexander
dc.contributor.author Doel, David L.
dc.contributor.author Cissell, Jeffrey J.
dc.date.accessioned 2006-06-15T19:51:32Z
dc.date.available 2006-06-15T19:51:32Z
dc.date.issued 2005-06-06
dc.description Proceedings of ASME Turbo Expo 2005: Land Sea & Air, June 6-9, 2005, Reno-Tahoe. en
dc.description.abstract This paper describes the development of an improved method for reliable, repeatable, and accurate matching of engine performance models to test data. The centerpiece of this approach is a minimum variance estimator algorithm with a priori estimates which addresses both deterministic and probabilistic aspects of the problem. Specific probabilistic aspects include uncertainty in the measurements, prior expectations on model matching parameters, and noise in the power setting parameters. The algorithm is able to produce optimal results using any number of measurements and model matching parameters and can therefore take advantage of all measured data to produce the best possible match. This improves on current matching algorithms which require that the number of measured parameters be equal to the number of model matching parameters. This algorithm has been implemented in the Numerical Propulsion System Simulation (NPSS) and tested on a generic high-bypass turbofan model typical of those used in commercial service. The baseline engine model and simulated test data are described in detail. Several exercises are discussed to illustrate results available from this algorithm including the matching of a typical power calibration data set and matching of a typical production engine data set. en
dc.format.extent 269348 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/10610
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries ASDL;GT-2005-68201 en
dc.subject Matching engine performance models to test data en
dc.subject Minimum variance estimator algorithm en
dc.subject Matching algorithms en
dc.subject Numerical Propulsion System Simulation (NPSS) en
dc.subject Power calibration data sets en
dc.subject Production engine data sets en
dc.title Probabilistic Matching of Turbofan Engine Performance Models to Test Data en
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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