A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements
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 anemo...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2015
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| Online Access: | http://hdl.handle.net/1721.1/99151 |
| _version_ | 1811078504103542784 |
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| author | Srivastava, Ankur Meade, Andrew J. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Srivastava, Ankur Meade, Andrew J. |
| author_sort | Srivastava, Ankur |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:01:13Z |
| format | Article |
| id | mit-1721.1/99151 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:01:13Z |
| publishDate | 2015 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | mit-1721.1/991512022-10-01T00:34:04Z A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements Srivastava, Ankur Meade, Andrew J. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Srivastava, Ankur 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. 2015-10-06T12:13:56Z 2015-10-06T12:13:56Z 2015 2015-08 2015-10-03T06:58:42Z Article http://purl.org/eprint/type/JournalArticle 1687-5966 1687-5974 http://hdl.handle.net/1721.1/99151 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. en http://dx.doi.org/10.1155/2015/183712 International Journal of Aerospace Engineering Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 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. application/pdf Hindawi Publishing Corporation Hindawi Publishing Corporation |
| spellingShingle | Srivastava, Ankur Meade, Andrew J. A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title | A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title_full | A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title_fullStr | A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title_full_unstemmed | A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title_short | A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements |
| title_sort | comprehensive probabilistic framework to learn air data from surface pressure measurements |
| url | http://hdl.handle.net/1721.1/99151 |
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