https://popups.uliege.be/esaform21/index.php?id=4294 Index terms fr 0 https://popups.uliege.be/esaform21/index.php?id=1572 Finite element modeling (FEM) has recently become the most attractive computational tool to predict and optimize many industrial problems. However, the FEM becomes ineffective as far as complex multi-physics parameterized problems, such as induction heating process, are concerned because of high computational cost. This work aims at studying the possibility of applying a new approach based on the reduced order modeling (ROM) to obtain approximate solutions of a parametric problem. Basically, the effect of induction heating process parameters on some physical quantities of interest (QoI) will be analyzed under the real-time constraint. To achieve this dimensionality reduction, a set of precomputed solutions is first collected, at some sparse points in the space domain and for a properly selected process parameters, by solving the full-order models implemented in the commercial finite element software FORGE®. A Proper Orthogonal Decomposition (POD) based reducedorder model is then applied to the collected data to find a low dimensional space onto which the solution manifold could be projected and an approximated solution for new process parameters could be efficiently computed in real time. Besides, the POD is applied to build a reduced basis and to compute their corresponding modal coefficients. It is then followed by artificial intelligence techniques for regression purpose, such as sparse Proper Generalized Decomposition, to fit the low dimensional POD modal coefficients. Hence, the problem can be solved with a much lower dimension compared to the initial one. It was shown that a good approximation of the QoI was provided, in low-data limit, using a single POD modal coefficient as a response for the regression methods. However, the obtained approximation accuracy needs to be enhanced. Mon, 22 Mar 2021 20:13:16 +0100 Fri, 02 Apr 2021 17:05:46 +0200 https://popups.uliege.be/esaform21/index.php?id=1572 https://popups.uliege.be/esaform21/index.php?id=4293 Thermally supported clinching (Hotclinch) is a novel promising process to join dissimilar materials. Here, metal and fibre-reinforced thermoplastics (FRTP) are used within this single step joining process and without the usage of auxiliary parts like screws or rivets. For this purpose, heat is applied to improve the formability of the reinforced thermoplastic. This enables joining of the materials using conventional clinching-tools. Focus of this work is the modelling on mesoscopic scale for the numerical simulation of this process. The FTRP-model takes the material behaviour both of matrix and the fabric reinforced organo-sheet under process temperatures into account. For describing the experimentally observed phenomena such as large deformations, fibre failure and the interactions between matrix and fibres as well as between fibres themselves, the usage of conventional, purely Lagrangian based FEM methods is limited. Therefore, the combination of contact-models with advanced modelling approaches like Arbitrary-Lagrangian-Eulerian (ALE), Coupled-Eulerian-Lagrangian (CEL) and Smooth-ParticleHydrodynamics (SPH) for the numerical simulation of the clinching process are employed. The different approaches are compared with regard to simulation feasibility, robustness and results accuracy. It is shown, that the CEL approach represents the most promising approach to describe the clinching process. Thu, 01 Apr 2021 17:51:44 +0200 Thu, 01 Apr 2021 17:51:44 +0200 https://popups.uliege.be/esaform21/index.php?id=4293