A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements

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dc.citation.journalTitleInternational Journal of Aerospace Engineeringen_US
dc.citation.volumeNumber2015en_US
dc.contributor.authorSrivastava, Ankuren_US
dc.contributor.authorMeade, Andrew J.en_US
dc.date.accessioned2015-10-05T16:24:55Zen_US
dc.date.available2015-10-05T16:24:55Zen_US
dc.date.issued2015en_US
dc.description.abstractUse of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.en_US
dc.identifier.citationSrivastava, Ankur and Meade, Andrew J.. "A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements." <i>International Journal of Aerospace Engineering,</i> 2015, (2015) Hindawi: http://dx.doi.org/10.1155/2015/183712.en_US
dc.identifier.doihttp://dx.doi.org/10.1155/2015/183712en_US
dc.identifier.urihttps://hdl.handle.net/1911/81872en_US
dc.language.isoengen_US
dc.publisherHindawien_US
dc.rightsThis is an open access article distributed under theCreative 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.titleA Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurementsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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