Conference
Multi-fidelity Bayesian optimization strategy applied to Overall Drone Design ; Optimisation bayésienne multifidélité appliquée à la conception de drones
| العنوان: | Multi-fidelity Bayesian optimization strategy applied to Overall Drone Design ; Optimisation bayésienne multifidélité appliquée à la conception de drones |
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| المؤلفون: | Charayron, Rémy, Lefebvre, Thierry, Bartoli, Nathalie, Morlier, Joseph |
| المساهمون: | DTIS, ONERA, Université de Toulouse Toulouse, ONERA-PRES Université de Toulouse, Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) |
| المصدر: | AIAA SCITECH 2023 Forum ; https://hal.science/hal-04006425 ; AIAA SCITECH 2023 Forum, Jan 2023, National Harbor, United States. pp.AIAA 2023-2366, ⟨10.2514/6.2023-2366⟩ ; https://arc.aiaa.org/doi/10.2514/6.2023-2366 |
| بيانات النشر: | HAL CCSD American Institute of Aeronautics and Astronautics |
| سنة النشر: | 2023 |
| المجموعة: | Université Toulouse III - Paul Sabatier: HAL-UPS |
| مصطلحات موضوعية: | drone design, bayesian optimization, CONCEPTION DRONE, OPTIMISATION BAYESIENNE, [SPI]Engineering Sciences [physics], [MATH]Mathematics [math], [PHYS]Physics [physics] |
| جغرافية الموضوع: | National Harbor, United States |
| الوصف: | International audience ; Nowadays, drones can be developed for a wide range of use cases, from infrastructure monitoring to sea rescue, urban mobility or military purposes. Which drone design is best suited for a specific mission? To answer this question, we need to solve a constrained optimization problem based on a multidisciplinary design model that takes the mission into account. The model generally being a computationally expensive numerical model whose gradients are not available all the time encourages us to consider a Bayesian optimization approach. Such strategy is well known to achieve a trade-off between exploitation and exploration in order to find interesting minimal area with a reduced number of function evaluations. A multi-fidelity approach can improve even more the computational efficiency of the Bayesian optimization strategy. In this work, we aim at designing a fixed-wing drone (fully electric) for long range surveillance mission. Two fidelity level electric drone models are developed. For a given mission requirement, the final battery state of charge is optimized with respect to drone design variables. Optimizations are performed on several missions using both a mono and a multi-fidelity Bayesian optimization strategy. The interest of using a multi-fidelity method for overall drone design has been assessed. The multi-fidelity super-efficient global optimization algorithm (MFSEGO) appeared to need less budget to reach convergence than the mono-fidelity algorithm and to be more robust to the initial design of experiments. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| DOI: | 10.2514/6.2023-2366 |
| الاتاحة: | https://hal.science/hal-04006425 https://hal.science/hal-04006425v1/document https://hal.science/hal-04006425v1/file/DTIS22187.1677502882_postprint.pdf https://doi.org/10.2514/6.2023-2366 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.20F654A4 |
| قاعدة البيانات: | BASE |
| DOI: | 10.2514/6.2023-2366 |
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