A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements
Joint Authors
Srivastava, Ankur
Meade, Andrew J.
Source
International Journal of Aerospace Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-28
Country of Publication
Egypt
No. of Pages
19
Abstract EN
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.
American Psychological Association (APA)
Srivastava, Ankur& Meade, Andrew J.. 2015. A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements. International Journal of Aerospace Engineering،Vol. 2015, no. 2015, pp.1-19.
https://search.emarefa.net/detail/BIM-1064497
Modern Language Association (MLA)
Srivastava, Ankur& Meade, Andrew J.. A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements. International Journal of Aerospace Engineering No. 2015 (2015), pp.1-19.
https://search.emarefa.net/detail/BIM-1064497
American Medical Association (AMA)
Srivastava, Ankur& Meade, Andrew J.. A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements. International Journal of Aerospace Engineering. 2015. Vol. 2015, no. 2015, pp.1-19.
https://search.emarefa.net/detail/BIM-1064497
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1064497