A neural network-based data-driven local modeling of spotwelded plates under impact

Solving large structural problems with multiple complex localized behaviors is extremely challenging. To address this difficulty, both intrusive and non-intrusive Domain Decomposition Methods (DDM) have been developed in the past, where the refined model (local) is solved separately in its own space and time scales. In this work, the Finite Element Method (FEM) at the local scale is replaced with a data-driven Reduced Order Model (ROM) to further decrease computational time. The reduced model aims to create a low-cost, accurate and efficient mapping from interface velocities to interface forces and enable the prediction of their time evolution. The present work proposes a modeling technique based on the Physics-Guided Architecture of Neural Networks (PGANNs), which incorporates physical variables other than input/output variables into the neural network architecture. We develop this approach on a 2D plate with a hole as well as a 3D case with spot-welded plates undergoing fast deformation, representing nonlinear elastoplasticity problems. Neural networks are trained using simulation data generated by explicit dynamic FEM solvers. The PGANN results are in good agreement with the FEM solutions for both test cases, including those in the training dataset as well as the unseen dataset, given the loading type is present in the training set

Real-time tracking of crack propagation in active structures

International audienc

Real-time inverse crack tracking in uncertain microstructures using PGD-based model reduction and extended Kalman filtering

International audienc

A data-driven approach for the real-time modelling of spot-welded patches under impact using deep learning

International audienc

Suivi d'une interface solide mobile au sein d'un écoulement diphasique par une méthode de frontière immergée

International audienceLa simulation numérique de l’interaction entre une structure en mouvement et un écoulement diphasique est un challenge important pour le monde des industries navale, nucléaire ou aéronautique. Une méthode de frontière immergée définissant le domaine fluide-structure comme un milieu poreux est présentée. Elle est implémentée au sein d’un code CFD volumes-finis dédié aux écoulements diphasiques et basé sur une approche bi-fluide. La structure est définie de manière lagrangienne à l’aide d’une porosité nulle sur une grille cartésienne. Par conséquent, le bilan des fractions volumiques de phases, le bilan de quantité de mouvement de chaque phase, et le bilan de masse sont corrigés de manière à reconstruire l’interface fluide-structure. La méthode dite de “porosité variable en temps et en espace” est évaluée sur différents cas mono- et diphasiques

A data-driven approach for the real-time modelling of spot-welded patches under impact using deep learning

International audienc

A data-driven approach using Neural Networks for real-time modeling and simulation of structures with many welded points under impact

International audienceLarge structural problems with complex localized behaviour are extremely difficult to solve. In the past, intrusive and non-intrusive domain decomposition methods (DDMs) have been developed to address this challenge. To further reduce computational time, a data-driven neural network-based metamodeling of the local scale is proposed here, which could establish an efficient and accurate mapping from interface velocities to interface forces and predict their time evolution, in the context of dynamics problems. It is difficult to obtain direct input-output relationships in explicit dynamics because these quantities are highly noisy by nature. To address this fundamental problem, we develop a new architecture called Physics-Guided Neural Networks (PGNN) . The key idea is to inject high-fidelity simulation quantities (such as displacement, stress, strain, and so on) from the local domain between the input and output layers of NN to improve learning within a solution space. In this study, we only inject one of these quantities, displacement. It can also account for unmodeled physics with fewer experimental data by imposing fundamental solid mechanics principles. The proposed method is exemplified by a spotwelded plates undergoing rapid deformation. The neural networks are trained with explicit FEM solver-generated simulation data. The PGNN results are in good agreement with the FEM solutions for both the training dataset and the unseen dataset test cases. At the conference, a preliminary implementation of a local/global coupling framework for explicit dynamics with a developed metamodel will be presented

Non-Intrusive Coupling of Neural Network-Based Local Model and Explicit Dynamics Scheme: Application to Spot-Welded Plates under Impact

International audienceSolving large structural problems with multiple complex localized behaviors poses significant challenges, primarily due to the requirement of a fine mesh to capture local features and the need for a fine time step to satisfy the CFL condition. To address these difficulties, both intrusive and non-intrusive Domain Decomposition Methods (DDM) have been developed in the past, which involve solving the fine (local) and coarse (global) models separately at their respective time and space scales, with interface quantities exchanged between them. This study presents an innovative approach to further reduce computational time by replacing the Finite Element Model (FEM) at the local scale with a data-driven Reduced Order Model (ROM).The work consists of two main parts: the development of a data-driven Reduced Order Model (ROM) at the local scale, and the formulation of a non-intrusive local/global coupling [1] method to integrate the ROM with an Explicit solver. The ROM aims to establish an accurate and efficient mapping from interface velocities to interface forces, enabling the prediction of their temporal evolution. This paper proposes a modeling technique based on the Physics-Guided Architecture of Neural Networks (PGANN) [2], which incorporates physical variables beyond the input/output variables into the neural network architecture.The local/global coupling strategy relies on an iterative exchange of interface quantities between the global and local computations. An extended version, as proposed [3] for explicit dynamics problems allows the global computation to be performed only once per global time step, while multiple solutions are required for the local problems. To achieve this, we propose replacing the FEM local problem with PGANN, resulting in a significant reduction in computational time.To demonstrate the efficiency and robustness of the proposed approach, two examples will be presented: a 2D plate with a hole and a 3D case involving fast deformation of spot-welded plates.[1] L. Gendre, O. Allix, and P. Gosselet. Non-intrusive and exact global/local techniques for structural problems with local plasticity. Comput Mech., 44: 233–245 (2009).[2] J. Willard, X. Jia, S. Xu, et al. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. ACM Computing Surveys (2022).[3] O. Bettinotti, O. Allix, U. Perego, V. Oancea, B. Malherbe A fast weakly-intrusive multiscale method in explicit dynamics IJNME, Volume 100, Issue 8, 23: 577-595 (2014)