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...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/183712 |
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| _version_ | 1841524638579949568 |
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| author | Ankur Srivastava Andrew J. Meade |
| author_facet | Ankur Srivastava Andrew J. Meade |
| author_sort | Ankur Srivastava |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-7d06e8e7ae504e4ba532a27718c3479b |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-7d06e8e7ae504e4ba532a27718c3479b2025-02-03T05:47:43ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742015-01-01201510.1155/2015/183712183712A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure MeasurementsAnkur Srivastava0Andrew J. Meade1Massachusetts Institute of Technology, Cambridge, MA 02139, USARice University, Houston, TX 77005, USAUse 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.http://dx.doi.org/10.1155/2015/183712 |
| spellingShingle | Ankur Srivastava Andrew J. Meade A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements International Journal of Aerospace Engineering |
| 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://dx.doi.org/10.1155/2015/183712 |
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