On constructing efficient UAV aerodynamic surrogate models for digital twins
| dc.contributor.author | Aláez Gómez, Daniel | |
| dc.contributor.author | Prieto Míguez, Manuel | |
| dc.contributor.author | Villadangos Alonso, Jesús | |
| dc.contributor.author | Astrain Escola, José Javier | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua | |
| dc.date.accessioned | 2024-11-14T12:10:14Z | |
| dc.date.available | 2024-11-14T12:10:14Z | |
| dc.date.issued | 2024-07-31 | |
| dc.date.updated | 2024-11-14T11:55:00Z | |
| dc.description.abstract | Aerodynamic modeling and optimization for unmanned aerial vehicles (UAVs) are complex and computationally intensive tasks. Surrogate models have emerged as a powerful tool for increasing efficiency in the aircraft design and optimization process. We review and evaluate some modeling techniques, such as artificial neural networks and support vector regression, showing that Gaussian process regression generally provides a well-performing solution to this type of problem. We propose an active learning algorithm based on the relevance factor, that combines bias estimated from nearest-neighbor Euclidean distance and variance, to achieve higher accuracy with fewer compuational fluid dynamics (CFD) simulations. The obtained performance is evaluated using four 2-D test functions and an experimental CFD case, indicating that the proposed active learning approach outperforms classical random sampling techniques. Thanks to this architecture, the development process of a new commercial UAV can be significantly streamlined by expediting the testing phase through the use of DTs modeled more efficiently. | en |
| dc.description.sponsorship | This work was supported in part by European Union NextGenerationEU/PRTRMCIN/AEI/10.13039/501100011033, Holistic power lines predictive maintenance system under Grant PLEC2021-007997, in part by the CONDOR-Connected under Grant TED2021-131716B-C21 SARA and Grant PID2021-127409OB-C31, and in part by the Government of Navarre (Departamento de Desarrollo Economico) under the research Grant PC109-110 NAITEST. Paper no. TII-24-2116. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Aláez, D., Prieto, M., Villadangos, J., Astrain, J. J. (2024). On constructing efficient UAV aerodynamic surrogate models for digital twins. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2024.3431106. | |
| dc.identifier.doi | 10.1109/TII.2024.3431106 | |
| dc.identifier.issn | 1551-3203 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/52507 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | IEEE Transactions on Industrial Informatics (2024), vol. 20, núm. 11 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PLEC2021-007997/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement///TED2021-131716B-C21/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127409OB-C31/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//PC109-110/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//TII-24-2116/ | |
| dc.relation.publisherversion | https://doi.org/10.1109/TII.2024.3431106 | |
| dc.rights | © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Active learning | en |
| dc.subject | CFD | en |
| dc.subject | Gaussian process regression (GPR) | en |
| dc.subject | Surrogate model | en |
| dc.subject | Unmanned aerial vehicles (UAV) | en |
| dc.title | On constructing efficient UAV aerodynamic surrogate models for digital twins | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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