No AccessExperimental Defect Detection in a Swirl-Burner Array Through Exhaust Jet AnalysisUlrich Hartmann, Henrik von der Haar, Friedrich Dinkelacker and Jörg R. SeumeUlrich HartmannLeibniz UniversitySearch for more papers by this author, Henrik von der HaarLeibniz UniversitySearch for more papers by this author, Friedrich DinkelackerLeibniz UniversitySearch for more papers by this author and Jörg R. SeumeLeibniz UniversitySearch for more papers by this authorAIAA 2018-0303Session: Measurement Applications and CharacterizationPublished Online:7 Jan 2018https://doi.org/10.2514/6.2018-0303SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookXLinked InRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetailsSee PDF for referencesCited byExhaust Jet Analysis12 September 2024Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields13 July 2021 | Journal of the Global Power and Propulsion Society, Vol. 13, No. MayExperimental and Numerical Based Defect Detection in a Model Combustion Chamber through Machine Learning1 Jan 2021 | International Journal of Gas Turbine, Propulsion and Power Systems, Vol. 12, No. 4 What's Popular 2018 AIAA Aerospace Sciences Meeting 8–12 January 2018Kissimmee, Floridahttps://doi.org/10.2514/6.2018-0303 CrossmarkInformationCopyright © 2018 by Leibniz Universitaet Hannover. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. TopicsAircraft EnginesCombustion ChambersCombustorsHeating, Ventilating, and Air ConditioningJet EnginesMass TransferPropulsion and PowerThermophysics and Heat Transfer KeywordsBackground Oriented SchlierenCombustorsCombustion ChambersJet EnginesInfrared SpectroscopyRefractionAircraft EnginesCartesian CoordinatesParticle Image VelocimetryMass FlowDigital Topics Ground Testing