Contribution à la résolution de problèmes inverses sous contraintes et application de méthodes de conception robuste pour le dimensionnement de pièces mécaniques de turboréacteurs en phase avant-projets.

Abstract : The aim of this PhD dissertation is to propose a new approach to improve and accelerate preliminary design studies for turbofan engine components. This approach consists in a comprehensive methodology for robust design under constraints, following three stages : dimension reduction and metamodeling, robust design under constraints and finally inverse problem solving under constraints. These are the three main subjects of this PhD dissertation. Dimension reduction is an essential pre-processing for any study. Its aim is to keep only inputs with large effects on a selected output. This selection reduces the size of the domain on which is performed the study which reduces its computational cost and eases the (qualitative) understanding of the system of interest. Metamodeling also contributes to these two objectives by replacing the time-consuming computer code by a faster metamodel which approximates adequately the relationship between system inputs and the studied output. Robust design under constraints is a bi-objectives optimization where different uncertainty sources are included. First, uncertainties must be collected and modeled. Then a propagation method of uncertainties in the computation code must be chosen in order to estimate moments (mean and standard deviation) of output distribution. Optimization of these moments are the two robust design objectives. Finally, a multi-objectives optimization method has to be chosen to find a robust optimum under constraints. The development of methods to solve ill-posed inverse problems is the innovative part of this PhD dissertation. These problems can have infinitely many solutions constituting non convex or even disjoint sets. Inversion is considered here as a complement to robust design in the case where the obtained optimum doesn't satisfy one of the constraints. Inverse methods then enable to solve this problem by finding several input datasets which satisfy all the constraints and a condition of proximity to the optimum. The aim is to reach a target value of the unsatisfied constraint while respecting other system constraints and the optimum proximity condition. Applied to preliminary design of high pressure compressor, this methodology contributes to the improvement and acceleration of studies currently characterized by a numerous of loopbacks which are expensive in terms of cpu-time and human resources.
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Maëva Biret. Contribution à la résolution de problèmes inverses sous contraintes et application de méthodes de conception robuste pour le dimensionnement de pièces mécaniques de turboréacteurs en phase avant-projets.. Statistiques [math.ST]. Université Pierre et Marie Curie - Paris VI, 2016. Français. ⟨NNT : 2016PA066294⟩. ⟨tel-01454988⟩

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