Non-intrusive proper generalized decomposition involving space and parameters: application to the mechanical modeling of 3D woven fabrics
Article dans une revue avec comité de lecture
JournalAdvanced Modeling and Simulation in Engineering Sciences
In our former works we proposed different Model Order Reduction strategies for alleviating the complexity of computational simulations. In fact we proved that separated representations are specially appealing for addressing many issues, in particular, the treatment of 3D models defined in degenerated domains (those involving very different characteristic dimensions, like beams, plate and shells) as well as the solution of parametrized models for calculating their parametric solutions. However it was proved that the efficiency of solvers based on the construction of such separated representations strongly depends on the affine decompositions (separability) of operators, parameters and geometry. Even if our works proved that different techniques exists for performing such beneficial separation prior of applying the separated representation constructor, the complexity of the solver increases in certain circumstances too much, as the one involving the space separation of complex microstructures concerned by 3D woven fabrics. In this paper we explore an alternative route that allows circumventing the just referred difficulties. Thus, instead of following the standard procedure that consists of introducing the separated representation of the unknown field prior to discretize the models, the strategy here proposed consists of proceeding inversely: first the model is discretized and then the separated representation of the discrete unknown field is enforced. Such a procedure enables the consideration of very complex and non separable features, like complex domains, boundary conditions and microstructures as the ones concerned by homogenized models of complex and rich 3D woven fabrics. It will be proved that such a procedure can be also easily coupled with a non-intrusive treatment of the parametric dimensions by using a sparse hierarchical collocation technique.
Files in this item
- PIMM_AMSES_2019_CHINESTA.pdf .pdf
Showing items related by title, author, creator and subject.
Article dans une revue avec comité de lectureVERMIGLIO, Simona; CHAMPANEY, Victor; SANCARLOS, Abel; DAIM, Fatima; KEDZIA, Jean Claude; DUVAL, Jean Louis; DIEZ, Pedro; CHINESTA, Francisco (MDPI, 2020)Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave ...
On the effective conductivity and the apparent viscosity of a thin rough polymer interface using PGD‐based separated representations Article dans une revue avec comité de lectureAMMAR, Amine; GHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; CUETO, Elias; CHINESTA, Francisco (Wiley, 2020)Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion ...
Article dans une revue avec comité de lectureQUARANTA, Giacomo; ZIANE, Mustapha; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; CHINESTA, Francisco; ESI GROUP (SpringerOpen, 2019)Most of mechanical systems and complex structures exhibit plate and shell components. Therefore, 2D simulation, based on plate and shell theory, appears as an appealing choice in structural analysis as it allows reducing ...
Article dans une revue avec comité de lectureREILLE, Agathe; CHAMPANEY, Victor; DAIM, Fatima; TOURBIER, Yves; HASCOET, Nicolas; GONZALEZ, David; CUETO, Elias; DUVAL, Jean Louis; CHINESTA, Francisco (EDP Sciences, 2021)Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized ...
Structural health monitoring by combining machine learning and dimensionality reduction techniques Article dans une revue avec comité de lectureQUARANTA, Giacomo; LOPEZ, Elena; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; HUERTA, Antonio; CHINESTA, Francisco (Universitat politecnica de Catalunya, 2019)Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate ...