This thesis addresses the challenges associated with thermal management systems (TMS) evaluation and selection in the conceptual design of hypersonic, air-breathing vehicles with sustained cruise. The proposed methodology identifies analysis tools and techniques which allow the proper investigation of the design space for various thermal management technologies. The design space exploration environment and alternative multi-objective decision making technique defined as Pareto-based Joint Probability Decision Making (PJPDM) is based on the approximation of 3-D Pareto frontiers and probabilistic technology effectiveness maps. These are generated through the evaluation of a Pareto Fitness function and Monte Carlo analysis. In contrast to Joint Probability Decision Making (JPDM), the proposed PJPDM technique does not require preemptive knowledge of weighting factors for competing objectives or goal constraints which can introduce bias into the final solution. Preemptive bias in a complex problem can degrade the overall capabilities of the final design. The implementation of PJPDM in this thesis eliminates the need for the numerical optimizer which is required with JPDM in order to improve upon a solution. In addition, a physics-based formulation is presented for the quantification of TMS safety effectiveness corresponding to debris impact/damage and how it can be applied towards risk mitigation. Lastly, a formulation loosely based on non-preemptive Goal Programming with equal weighted deviations is provided for the resolution of the inverse design space. This key step helps link vehicle capabilities to TMS technology subsystems in a top-down design approach. The methodology provides the designer more knowledge up front to help make proper engineering decisions and assumptions in the conceptual design phase regarding which technologies show greatest promise, and how to guide future technology research.

Dans le domaine d’optimisation de forme de structures, la réduction des coûts et l’amélioration des produits sont des défis permanents à relever. Pour ce faire, le procédé de mise en forme doit être optimisé. Optimiser le procédé revient alors à résoudre un problème d’optimisation. Généralement ce problème est un problème d’optimisation multicritère très coûteux en terme de temps de calcul, où on cherche à minimiser plusieurs fonctions coût en présence d’un certain nombre de contraintes. Pour résoudre ce type de problème, on a développé un algorithme robuste, efficace et fiable. Cet algorithme, consiste à coupler un algorithme de capture de front de Pareto (NBI ou NNCM) avec un métamodèle (RBF), c’est-à-dire des approximations des résultats des simulations coûteuses. D’après l’ensemble des résultats obtenus par cette approche, il est intéressant de souligner que la capture de front de Pareto génère un ensemble des solutions non dominées. Pour savoir lesquelles choisir, le cas échéant, il est nécessaire de faire appel à des algorithmes de sélection, comme par exemple Nash et Kalai-Smorodinsky. Ces deux approches, issues de la théorie des jeux, ont été utilisées pour notre travail. L’ensemble des algorithmes sont validés sur deux cas industriels proposés par notre partenaire industriel. Le premier concerne un modèle 2D du fond de la canette (elasto-plasticité) et le second est un modèle 3D de la traverse (élasticité linéaire). Les résultats obtenus confirment l’efficacité de nos algorithmes développés. / One of the current challenges in the domain of the multiobjective shape optimization is to reduce the calculation time required by conventional methods. The high computational cost is due to the high number of simulation or function calls required by these methods. Recently, several studies have been led to overcome this problem by integratinga metamodel in the overall optimization loop. In this thesis, we perform a coupling between the Normal Boundary Intersection -NBI- algorithm and The Normalized Normal constraint Method -NNCM- algorithm with Radial Basis Function -RBF- metamodel in order to have asimple tool with a reasonable calculation time to solve multicriteria optimization problems. First, we apply our approach to academic test cases. Then, we validate our method against two industrial cases, namely, shape optimization of the bottom of a can undergoing nonlinear elasto-plastic deformation and an optimization of an automotive twist beam. Then, in order to select solutions among the Pareto efficient ones, we use the same surrogate approach to implement a method to compute Nash and Kalai-Smorodinsky equilibria.