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Reduced order models for multiparametric analyses of buckling problems : application to additive manufacturing

Abstract : The development of additive manufacturing allows structures with highly complex shapes to be produced. Complex lattice shapes are particularly interesting in the context of lightweight structures. However, although the use of this technology is growing in numerous engineering domains, this one is not enough matured and the correlations between the experimental data and deterministic simulations are not obvious. To take into account observed variations of behavior, multiparametric approaches are nowadays efficient solutions to tend to robust and reliable designs. The aim of this thesis is to integrate material and geometric uncertainty, experimentally quantified, in buckling analyses. To achieve this objective, different surrogate models, based on regression and correlation techniques as well as different reduced order models have been first evaluated to reduce the prohibitive computational time. The selected projections rely on modes calculated either from Proper Orthogonal Decomposition, from homotopy developments or from Taylor series expansion. Second, the proposed mathematical model is integrated in fuzzy and probabilistic analyses to estimate the evolution of the critical buckling load for lattice structures.
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Submitted on : Wednesday, August 22, 2018 - 12:09:06 PM
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van Tu Doan. Reduced order models for multiparametric analyses of buckling problems : application to additive manufacturing. Mechanics of the structures [physics.class-ph]. Université de Valenciennes et du Hainaut-Cambresis, 2018. English. ⟨NNT : 2018VALE0017⟩. ⟨tel-01859576⟩

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