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. .. Motivations, 188 10.2 Publication soumise dans «, Computer Methods in Applied Mechanics and Engineering, vol.10

. .. Conclusion,

, Le Chapitre 10 contient des applications d'optimisation par méta-modèles multi-fidélité. Il permet d'illustrer l'utilisation de la méthode EGO formulée en multi-fidélité et

, Une publication récemment soumise concerne ensuite la Section 10.2. Dans cette publication, la méthode EGO multi-fidélité est validée sur différents problèmes analytiques. Une application originale d'optimisation par méta-modèles multi-fidélité des réglages d'un gréement de voilier, simulé en IFS, fait également partie de cette publication. Deux réglages sont optimisés avec l'utilisation de cinq niveaux de fidélité construits sur les modèles fluide et structures de différentes précisions, L'accent est mis sur l'optimisation des réglages d'un gréement de voilier, qui constitue en soi l'un des objectifs de cette thèse

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, dans les programmes de prédiction de vitesse, clôt cette Partie I

, La Partie II présente les méthodes d'optimisation, reposant sur l'utilisation de méta-modèles, développées et appliquées au cours de cette thèse. L'emploi de méta-modèles construits à partir de processus Gaussiens est détaillé dans le Chapitre 4, en particulier pour les étapes de construction des méta-modèles, mais également pour ce qui est de leurs applications en terme d'analyse de sensibilité

, La méthode itérative d'optimisation par méta-modèles Gaussiens, amplement utilisée dans cette thèse

, Le problème des points non évaluables, qui peuvent apparaître lors de la résolution d'un problème d'optimisation sur une application réelle, est tout d'abord introduit dans ce Chapitre 5. La prise en compte de contraintes d'inégalité approchées par des méta-modèles Gaussiens est ensuite décrite. Enfin, l'introduction d'un modèle de classification binaire, Le Chapitre 5 porte sur la thématique de l'optimisation globale et sous contraintes, effectuée à partir de méta-modèles

, Les méthodes de constructions de ces méta-modèles sont présentées et une nouvelle formulation de l'optimisation par méta-modèles multi-fidélité est proposée. Cette méthode permet d'introduire le choix du niveau de fidélité utilisé dans le processus d'optimisation itératif, à partir d'une estimation de la réduction d'incertitude dans les zones d'intérêts du méta-modèle de plus haute fidélité. Elle permet de plus l'utilisation de méta-modèles multi

. Sacher, Elle concerne l'optimisation de la performance d'un modèle réduit de grand-voile d'Imoca pour laquelle deux réglages sont optimisés, afin de maximiser une fonction performance mesurée expérimentalement, dans la soufflerie de l'université d'Auckland. Cette application permet de montrer la nécessité d'insérer le bruit expérimental dans la construction des méta-modèles Gaussiens, pour mener à bien l'optimisation. Cette même optimisation est résolue numériquement. Elle met en oeuvre un modèle couplé fluide-structure pour le calcul de la même fonction performance. Des comparaisons expérimentales et numériques sont effectuées, afin de valider les calculs d'interaction fluide-structure. Une analyse de sensibilité est aussi menée sur le modèle numérique, dans le but de quantifier les variations de la performance provenant des incertitudes expérimentales, autour du réglage optimal, La dernière Partie III regroupe les différentes contributions de cette thèse. Les contributions sont présentées sous la forme d'articles publiés ou soumis dans des revues scientifiques. Le Chapitre 7 reprend une application originale de la méthode d'optimisation par méta-modèles Gaussiens, 2017.

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