%0 Conference Paper %F Oral %T Hydrofoil Optimization via Automated Multi-Fidelity Surrogate Models %+ Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA) %+ Institute of Marine Engineering - CNR (CNR-INM) %+ École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne) %+ Institut de Recherche Dupuy de Lôme (IRDL) %+ Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) %+ Institut de Recherche de l'Ecole Navale (IRENAV) %A Pehlivan Solak, Hayriye %A Wackers, Jeroen %A Pellegrini, Riccardo %A Serani, Andrea %A Diez, Matteo %A Perali, Paolo %A Sacher, Matthieu %A Leroux, Jean-Baptiste %A Augier, Benoit %A Hauville, Frédéric %A Bot, Patrick %Z Institut Carnot MERS (projets ORUP et OptiFoil) et ARTS (projet OptiFoil). %< avec comité de lecture %B 10th International Conference on Computational Methods in Marine Engineering (MARINE 2023) %C Madrid, Spain %8 2023-06-27 %D 2023 %K SDDO %K multi-fidelity %K RANS %K potential solver %K kitefoil %Z Engineering Sciences [physics]Conference papers %X Lifting hydrofoils are gaining importance, since they drastically reduce the wetted surface area of a ship hull, thus decreasing resistance. To attain efficient hydrofoils, the geometries can be obtained from an automated optimization process, based on simulations. However, hydrofoil high-fidelity simulations are computationally demanding, since fine meshes are needed to accurately capture the pressure field and the boundary layer on the hydrofoil. Simulation-based optimization can therefore be very expensive. Automated surrogate models, trained by a limited number of simulations, can reduce the required computational demand for the optimization by performing simulations where these are more informative. Furthermore, if an efficient low-fidelity hydrofoil performance prediction tool (with a low computational cost) is available, using surrogates in a multi-fidelity framework can provide a further reduction in required simulations, by combining the accuracy of a few high-fidelity with an exploration process based on a larger number of low-fidelity computations. In this study, we propose a hydrofoil optimization procedure based on two simulation codes, a dedicated hydrofoil potential flow solver for low-fidelity simulations and a RANS solver for both medium-and high-fidelity simulations. The RANS solver uses adaptive grid refinement to attain high accuracy with a limited computational budget. Two different multi-fidelity frameworks are compared for a parameterized geometric model of a realistic hydrofoil: only RANS based and potential-RANS based. The effect of different combinations of fidelity levels on the efficiency of the optimization and the performance of the kitefoil-type hydrofoil is investigated and discussed. %G English %2 https://hal.science/hal-04089604/document %2 https://hal.science/hal-04089604/file/MARINE_2023_Pehlivan_etal.pdf %L hal-04089604 %U https://hal.science/hal-04089604 %~ UNIV-BREST %~ ENSTA-BRETAGNE %~ CNRS %~ UNIV-UBS %~ EC-NANTES %~ ENSAM %~ IFREMER %~ UNAM %~ UBS %~ ENIB %~ IRDL %~ I2M-BX %~ INRAE %~ ENSTA-BRETAGNE-MECA %~ NANTES-UNIVERSITE %~ NANTES-UNIV %~ HESAM %~ HESAM-ENSAM %~ RESEAU-EAU %~ IRENAV %~ LAMPA %~ LCPI %~ LABOMAP %~ LISPEN %~ MSMP