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

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dc.citation.journalTitleInternational Journal of Aerospace Engineering
dc.citation.volumeNumber2015
dc.contributor.authorSrivastava, Ankur
dc.contributor.authorMeade, Andrew J.
dc.date.accessioned2015-10-05T16:24:55Z
dc.date.available2015-10-05T16:24:55Z
dc.date.issued2015
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.
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.
dc.identifier.doihttp://dx.doi.org/10.1155/2015/183712
dc.identifier.urihttps://hdl.handle.net/1911/81872
dc.language.isoeng
dc.publisherHindawi
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.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.titleA Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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