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dc.contributor.authorSrivastava, Ankur
Meade, Andrew J.
dc.date.accessioned 2016-03-24T18:24:34Z
dc.date.available 2016-03-24T18:24:34Z
dc.date.issued 2014
dc.identifier.citation Srivastava, Ankur and Meade, Andrew J.. "Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements." International Journal of Aerospace Engineering, 2014, (2014) Hindawi: http://dx.doi.org/10.1155/2014/218710.
dc.identifier.urihttps://hdl.handle.net/1911/88641
dc.description.abstract Wind 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.
dc.language.iso eng
dc.publisher Hindawi
dc.rights This 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.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.title Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements
dc.type Journal article
dc.contributor.funder National Aeronautics and Space Administration
dc.citation.journalTitle International Journal of Aerospace Engineering
dc.citation.volumeNumber 2014
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1155/2014/218710
dc.identifier.grantID NCC-2-8077 (National Aeronautics and Space Administration)
dc.identifier.grantID NCC-1-02038 (National Aeronautics and Space Administration)
dc.type.publication publisher version


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