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

Sacher, Matthieu; Hauville, Frédéric; Duvigneau, Régis; Maître, Olivier Le; Aubin, Nicolas; Durand, Mathieu

doi:10.1016/j.jfluidstructs.2016.12.006

Cited by 10 publications

(8 citation statements)

References 47 publications

(47 reference statements)

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“…Regarding three-dimensional FSI problems, the authors of [29] mention optimization of the trimming of sails, but within an inviscid flow approximation, and the authors provide very few details on the optimization procedure they used. In our opinion, our work in [6] was the first one to consider the trimming optimization of a realistic sail system, and validate the numerical optimization using a comparison with the optimization of an experimental sail system in a wind tunnel. The optimization procedure used in [6] is the SF-EGO method.…”

Section: Application To Nonlinear Fsi Problemmentioning

confidence: 99%

“…In our opinion, our work in [6] was the first one to consider the trimming optimization of a realistic sail system, and validate the numerical optimization using a comparison with the optimization of an experimental sail system in a wind tunnel. The optimization procedure used in [6] is the SF-EGO method.…”

Section: Application To Nonlinear Fsi Problemmentioning

confidence: 99%

“…Efficient Global Optimization (EGO) strategies [1] have been used in various application domains, and many works demonstrated their applicability for the optimization of complex systems with costly objective function evaluation [2]. Examples of applications are the aerodynamic drag reduction [3], the vibration reduction for rotating aircraft [4], the optimization of Fluid-Structure Interactions (FSI) problems [5] and the sail trimming optimization [6]. In a nutshell, the EGO method builds a statistical surrogate model of the costly objective function f to be optimized, usually a Gaussian Processes [7] (GP), and a statistical criterion accounting for the Expected Improvement (EI), often referred to as merit function, is maximized to select a new design point.…”

Section: Introductionmentioning

confidence: 99%

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A Non-Nested Infilling Strategy for Multifidelity Based Efficient Global Optimization

Sacher

Maître

Duvigneau

et al. 2021

Int. J. UncertaintyQuantification

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Efficient Global Optimization (EGO) has become a standard approach for the global optimization of complex systems with high computational costs. EGO uses a training set of objective function values computed at selected input points to construct a statistical surrogate model, with low evaluation cost, on which the optimization procedure is applied. The training set is sequentially enriched, selecting new points, according to a prescribed infilling strategy, in order to converge to the optimum of the original costly model. Multi-fidelity approaches combining evaluations of the quantity of interest at different fidelity levels have been recently introduced to reduce the computational cost of building a global surrogate model. However, the use of multi-fidelity approaches in the context of EGO is still a research topic. In this work, we propose a new effective infilling strategy for multi-fidelity EGO. Our infilling strategy has the particularity of relying on non-nested training sets, a characteristic that comes with several computational benefits. For the enrichment of the multi-fidelity training set, the strategy selects the next input point together with the fidelity level of the objective function evaluation. This characteristic is in contrast with previous nested approaches, which require estimation all lower fidelity levels and are more demanding to update the surrogate. The resulting EGO procedure achieves a significantly reduced computational cost, avoiding computations at useless fidelity levels whenever possible, but it is also more robust to low correlations between levels and noisy estimations. Analytical problems are used to test and illustrate the efficiency of the method. It is finally applied to the optimization of a fully nonlinear fluid-structure interaction system to demonstrate its feasibility on real large-scale problems, with fidelity levels mixing physical approximations in the constitutive models and discretization refinements.

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Section: Application To Nonlinear Fsi Problemmentioning

confidence: 99%

Section: Application To Nonlinear Fsi Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Non-Nested Infilling Strategy for Multifidelity Based Efficient Global Optimization

Sacher

Maître

Duvigneau

et al. 2021

Int. J. UncertaintyQuantification

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“…GP models have also been found especially appealing for optimization, in the framework of the Efficient Global Optimization (EGO) [10] method, because their statistical nature allows to provide both a prediction of the objective function, in terms of model mean, and an error estimate, in terms of model variance. EGO strategies have been used in several engineering applications, such as aerodynamic drag reduction of transonic wings [11], vibration reduction for rotating aircrafts [12,13], optimization of FSI problems [14] and sail trimming optimization [15]. The EGO efficiency has been demonstrated for the optimization of complex systems with costly objective function evaluations [1].…”

Section: Introductionmentioning

confidence: 99%

A classification approach to efficient global optimization in presence of non-computable domains

Sacher

Duvigneau

Maître

et al. 2018

Struct Multidisc Optim

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Gaussian-Process based optimization methods have become very popular in recent years for the global optimization of complex systems with high computational costs. These methods rely on the sequential construction of a statistical surrogate model, using a training set of computed objective function values, which is refined according to a prescribed infilling strategy. However, this sequential optimization procedure can stop prematurely if the objective function cannot be computed at a proposed point. Such a situation can occur when the search space encompasses design points corresponding to an unphysical configuration, an ill-posed problem, or a non-computable problem due to the limitation of numerical solvers. To avoid such a premature stop in the optimization procedure, we propose to use a classification model to learn non-computable areas and to adapt the infilling strategy accordingly. Specifically, the proposed method splits the training set into two subsets composed of computable and non-computable points. A surrogate model for the objective function is built using the training set of computable points, only, whereas a probabilistic classification model is built using the union of the computable and non-computable training sets. The classifier is then incorporated in the surrogate-based optimization procedure to avoid proposing new points in the noncomputable domain while improving the classification uncertainty if needed. The method has the advantage to automatically adapt both the surrogate of the objective function and the classifier during the iterative optimization process. Therefore, non-computable areas do not need to be a priori known. The proposed method is applied to several analytical problems presenting different types of difficulty, and to the optimization of a fully nonlinear fluid-structure interaction system. The latter problem concerns the drag minimization of a flexible hydrofoil with cavitation constraints. The efficiency of the proposed method compared favorably to a reference evolutionary algorithm, except for situations where the feasible domain is a small portion of the design space.

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“…We then rely on a surrogate model to reduce the computational burden of optimizing complex systems with costly objective function estimation [Simpson et al 2001]. Specifically, we use Gaussian processes [Kleijnen 2009] surrogate models with Efficient Global Optimization (EGO) strategies [Jones et al 1998], that have been previously applied to aerodynamic drag reduction [Jeong et al 2005], vibration reduction for rotating aircrafts [Glaz et al 2009], optimization of FSI problems [Aghajari and Schäfer 2015] and sail trimming optimization [Sacher et al 2017]. The classification approach for discontinuous or binary constraints proposed in Basudhar et al [2012] is extended to the treatment of the cavitation constraints in the optimization procedure.…”

Section: Introductionmentioning

confidence: 99%

Flexible hydrofoil optimization for the 35th America's Cup with constrained EGO method

et al. 2018

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This paper investigates the use of constrained surrogate models to solve the multi-design optimization problem of a flexible hydrofoil. The surrogate-based optimization (EGO) substitutes the complex objective function of the problem by an easily evaluable model, constructed from a limited number of computations at carefully selected design points. Associated with ad-hoc statistical strategies to propose optimum candidates within the estimated feasible domain, EGO enables the resolution of complex optimization problems. In this work, we rely on Gaussian processes (GP) to model the objective function and adopt a probabilistic classification method to treat non-explicit inequality constraints and non-explicit representation of the feasible domain. This procedure is applied to the design of the shape and the elastic characteristics of a hydrofoil equipped with deformable elements providing flexibility to the trailing edge. The optimization concerns the minimization of the hydrofoil drag while ensuring a non-cavitating flow, at selected sailing conditions (boat speed and lifting force). The drag value and cavitation criterion are determined by solving a two-dimensional nonlinear fluid-structure interaction problem, based on a static vortex lattice method with viscous boundary layer equations, for the flow, and a nonlinear elasticity solver for the deformations of the elastic components of the foil. We compare the optimized flexible hydrofoil with a rigid foil geometrically optimized for the same sailing conditions. This comparison highlights the hydrodynamical advantages brought by the flexibility: a reduction of the drag over a large range of boat speeds, less susceptibility to cavitation and a smaller angle of attack tuning range.

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

Cited by 10 publications

References 47 publications

A Non-Nested Infilling Strategy for Multifidelity Based Efficient Global Optimization

A Non-Nested Infilling Strategy for Multifidelity Based Efficient Global Optimization

A classification approach to efficient global optimization in presence of non-computable domains

Flexible hydrofoil optimization for the 35th America's Cup with constrained EGO method

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