Efficient optimization procedure in non-linear fluid-structure interaction problem: Application to mainsail trimming in upwind conditions

Abstract : This paper investigates the use of Gaussian processes to solve sail trimming optimization problems. The Gaussian process, used to model the dependence of the performance with the trimming parameters, is constructed from a limited number of performance estimations at carefully selected trimming points, potentially enabling the optimization of complex sail systems with multiple trimming parameters. The proposed approach is tested on a two-parameter trimming for a scaled IMOCA mainsail in upwind sailing conditions. We focus on the robustness of the proposed approach and study especially the sensitivity of the results to noise and model error in the point estimations of the performance. In particular, we contrast the optimization performed on a real physical model set in a wind tunnel with a fully non-linear numerical fluid structure interaction model of the same experiments. For this problem with a limited number of trimming parameters , the numerical optimization was affordable and found to require a comparable amount of performance estimation as for the experimental case. The results reveal a satisfactory agreement for the numerical and experimental optimal trimming parameters, considering the inherent sources of errors and uncertainties in both numerical and experimental approaches. Sensitivity analyses have been eventually performed in the numerical optimization problem to determine the dominant source of uncertainties and characterize the robustness of the optima.
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https://hal.inria.fr/hal-01589317
Contributor : Régis Duvigneau <>
Submitted on : Monday, September 18, 2017 - 2:29:14 PM
Last modification on : Tuesday, May 7, 2019 - 6:30:12 PM

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Matthieu Sacher, Frédéric Hauville, Régis Duvigneau, Olivier Le Maître, Nicolas Aubin, et al.. Efficient optimization procedure in non-linear fluid-structure interaction problem: Application to mainsail trimming in upwind conditions. Journal of Fluids and Structures, Elsevier, 2017, 69, pp.209 - 231. ⟨10.1016/j.jfluidstructs.2016.12.006⟩. ⟨hal-01589317⟩

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