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| Title: | Intelligent strain sensing on a smart composite wing using extrinsic Fabry-Perot interferometric sensors and neural networks | |
| Author (s): | Dua, R. Eller, V. Isaac, Kakkattukuzhy M. Watkins, Steve E. Wunsch, Donald C. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Applied Optics Laboratory Electrical and Computer Engineering Mechanical & Aerospace Engineering | |
| Keywords: | Fabry-Perot interferometers aerodynamic condition aerodynamics aerospace computing aerospace materials air speeds angle of attack backpropagation backpropagation training algorithm beams (structures) closed circuit wind tunnel extrinsic Fabry-Perot interferometric sensors feedforward neural nets feedforward neural networks fibre optic sensors glass fibre reinforced plastics glass/epoxy composite beam intelligent sensors intelligent strain sensing intelligent structures smart composite wing stall condition strain measurement strain prediction strain sensors | |
| Issue Date: | 2003 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Dua, R.; Eller, V.; Isaac, K.M.; Watkins, S.E.; Wunsch, D.C., "Intelligent strain sensing on a smart composite wing using extrinsic Fabry-Perot interferometric sensors and neural networks," Proceedings of the International Joint Conference on Neural Networks, vol.4, pp. 2667- 2672, 20-24 July 2003 | |
| Abstract: | Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Intelligent strain sensing on a smart composite wing using extrinsic Fabry-Perot interferometric sensors and neural networks | |
| contributor.author | Dua, R. | |
| contributor.author | Eller, V. | |
| contributor.author | Isaac, Kakkattukuzhy M. | |
| contributor.author | Watkins, Steve E. | |
| contributor.author | Wunsch, Donald C. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Applied Optics Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Mechanical & Aerospace Engineering | |
| subject | Fabry-Perot interferometers | |
| subject | aerodynamic condition | |
| subject | aerodynamics | |
| subject | aerospace computing | |
| subject | aerospace materials | |
| subject | air speeds | |
| subject | angle of attack | |
| subject | backpropagation | |
| subject | backpropagation training algorithm | |
| subject | beams (structures) | |
| subject | closed circuit wind tunnel | |
| subject | extrinsic Fabry-Perot interferometric sensors | |
| subject | feedforward neural nets | |
| subject | feedforward neural networks | |
| subject | fibre optic sensors | |
| subject | glass fibre reinforced plastics | |
| subject | glass/epoxy composite beam | |
| subject | intelligent sensors | |
| subject | intelligent strain sensing | |
| subject | intelligent structures | |
| subject | smart composite wing | |
| subject | stall condition | |
| subject | strain measurement | |
| subject | strain prediction | |
| subject | strain sensors | |
| date.issued | 2003 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Dua, R.; Eller, V.; Isaac, K.M.; Watkins, S.E.; Wunsch, D.C., "Intelligent strain sensing on a smart composite wing using extrinsic Fabry-Perot interferometric sensors and neural networks," Proceedings of the International Joint Conference on Neural Networks, vol.4, pp. 2667- 2672, 20-24 July 2003 | |
| identifier.issn | 1098-7576 | |
| identifier.pub.URI | ||
| description.abstract | Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:17:19Z | |
| date.available | 2007-04-05T14:17:19Z | |
| identifier.persist.URI | ||
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