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International Journal of Aerospace Engineering
Volume 2015, Article ID 183712, 19 pages
Research Article

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

1Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2Rice University, Houston, TX 77005, USA

Received 11 May 2015; Revised 13 August 2015; Accepted 23 August 2015

Academic Editor: Christopher J. Damaren

Copyright © 2015 Ankur Srivastava and Andrew J. Meade. 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.


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.