Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements

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dc.citation.journalTitleInternational Journal of Aerospace Engineeringen_US
dc.citation.volumeNumber2014en_US
dc.contributor.authorSrivastava, Ankuren_US
dc.contributor.authorMeade, Andrew J.en_US
dc.date.accessioned2016-03-24T18:24:34Zen_US
dc.date.available2016-03-24T18:24:34Zen_US
dc.date.issued2014en_US
dc.description.abstractWind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US). We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS) strategy.en_US
dc.identifier.citationSrivastava, Ankur and Meade, Andrew J.. "Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements." <i>International Journal of Aerospace Engineering,</i> 2014, (2014) Hindawi: http://dx.doi.org/10.1155/2014/218710.en_US
dc.identifier.doihttp://dx.doi.org/10.1155/2014/218710en_US
dc.identifier.urihttps://hdl.handle.net/1911/88641en_US
dc.language.isoengen_US
dc.publisherHindawien_US
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.titleUse of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurementsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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