A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements
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IJAE.2015.183712.pdf
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Author(s)
Srivastava, Ankur
Meade, Andrew J.
Date Issued
2015
Journal
International Journal of Aerospace Engineering
Publisher
Hindawi Publishing Corporation
Citation
Ankur Srivastava and Andrew J. Meade, “A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements,” International Journal of Aerospace Engineering, vol. 2015, Article ID 183712, 19 pages, 2015.
Version
Final published version
Abstract
Use 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1155/2015/183712
