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dc.contributor.authorKlement, Jan
dc.contributor.authorAnglada, Eva
dc.contributor.authorGarmendia, Iñaki
dc.date.accessioned2016-11-03T10:11:38Z
dc.date.available2016-11-03T10:11:38Z
dc.date.issued2016-07
dc.identifier.citationKLEMENT, Jan; ANGLADA, Eva; GARMENDIA, Iñaki. Advances in automatic thermal model to test correlation in space industry. In: Proceedings of the 46th International Conference on Environmental Systems – ICES 2016, Vienna (Austria). Texas: Texas Tech. Univeristy, 2016. http://hdl.handle.net/2346/67496en
dc.identifier.urihttp://hdl.handle.net/11556/313
dc.description.abstractIn space industry thermal models are an important tool to predict, analyze and understand the thermal behaviour of components, subsystems and whole spacecrafts. Most parameters of these models have a limited accuracy and consequently the models results are uncertain. In order to reduce this uncertainty to a required level the model parameters are adjusted (correlated) by fitting the model to test results obtained during thermo vacuum tests. This is often a difficult long lasting manual process. In order to perform these correlations automatically many different methods have been developed and analyzed. Two of these methods are analyzed regarding their requirements, efficiency and limitations. A genetic algorithm is compared to a method based on non-linear equations solving algorithms of the Broyden class.en
dc.description.sponsorshipNo sponsorsen
dc.language.isoengen
dc.publisherTexas Tech. Univeristy (TTU)en
dc.titleAdvances in automatic thermal model to test correlation in space industryen
dc.typeconferenceObjecten
dc.isiNoen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsThermal modelen
dc.subject.keywordsCorrelationen
dc.subject.keywordsThermo vacuum testen
dc.subject.keywordsFittingen
dc.subject.keywordsGenetic algorithmsen
dc.subject.keywordsBroyden class algorithmen


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