Zobrazeno 1 - 10
of 233
pro vyhledávání: '"P. Kerfriden"'
A multi-scale methodology is developed in conjunction with a probabilistic fatigue lifetime model for structures with pores whose exact distribution, i.e. geometries and locations, is unknown. The method takes into account uncertainty in fatigue life
Externí odkaz:
http://arxiv.org/abs/2409.16565
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 8, Iss 1, Pp 1-23 (2021)
Abstract This paper presents a robust digital pipeline from CT images to the simulation of contact between multiple bodies. The proposed strategy relies on a recently developed immersed finite element algorithm that is capable of simulating unilatera
Externí odkaz:
https://doaj.org/article/eccb55a4a77441ecb9ad9faf557a9a5b
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-26 (2020)
Abstract We develop a novel unfitted finite element solver for composite materials with quasi-1D fibrous reinforcements. The method belongs to the class of mixed-dimensional non-conforming finite element solvers. The fibres are treated as 1D structur
Externí odkaz:
https://doaj.org/article/e6ddc763e8c44b6ba0b647ef48beeb11
This paper introduces a new local plastic correction algorithm that is aimed at accelerating elasto-plastic finite element (FE) simulations for structural problems exhibiting localised plasticity (around e.g. notches, geometrical defects). The propos
Externí odkaz:
http://arxiv.org/abs/2402.06313
In this work, the uncertainty associated with the finite element discretization error is modeled following the Bayesian paradigm. First, a continuous formulation is derived, where a Gaussian process prior over the solution space is updated based on o
Externí odkaz:
http://arxiv.org/abs/2306.05993
In this work we present a hybrid physics-based and data-driven learning approach to construct surrogate models for concurrent multiscale simulations of complex material behavior. We start from robust but inflexible physics-based constitutive models a
Externí odkaz:
http://arxiv.org/abs/2301.13547
Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element anal
Externí odkaz:
http://arxiv.org/abs/2209.07320
Stress prediction in porous materials and structures is challenging due to the high computational cost associated with direct numerical simulations. Convolutional Neural Network (CNN) based architectures have recently been proposed as surrogates to a
Externí odkaz:
http://arxiv.org/abs/2205.06562
In this paper, we present a robust and efficient unfitted concurrent multiscale method for continuum-continuum coupling, based on the Cut Finite Element Method (CutFEM). The computational domain is defined using approximate signed distance functions
Externí odkaz:
http://arxiv.org/abs/2201.04698
In this paper, we develop a novel unfitted multiscale framework that combines two separate scales represented by only one single computational mesh. Our framework relies on a mixed zooming technique where we zoom at regions of interest to capture mic
Externí odkaz:
http://arxiv.org/abs/2111.03199