Adaptive reduced basis strategy for rare events simulations

International audienceMonte-Carlo methods are well suited to characterize events of which associated probabilities are not too low with respect to the simulation budget. For very seldom observed events, these approaches do not lead to accurate results. Indeed, the number of samples are often insufficient to estimate such low probabilities (at least 10 +2 samples are needed to estimate a probability of 10 − with 10% relative deviation of the Monte-Carlo estimator). Even within the framework of reduced order methods, such as a reduced basis approach, it seems difficult to accurately predict low probability events. In this paper we propose to combine a cross-entropy method with a reduced basis algorithm to compute rare events (failure) probabilities

Requirements for artificial muscles to design robotic fingers

International audienceThis work is part of the ProMain project that concerns the modeling and the design of a soft robotic hand prosthesis, actuated by artificial muscles and controlled with surface Electromyography (EMG) signals. In a first stage, we designed a robotic finger based on the equivalent mechanical model of the human finger. The model takes into account three phalangeal joints, flexion and extension movements are studied. The robotic finger has three Degrees of Freedom (DoF). The finger is designed to be under-actuated and driven by tendons, i.e. only one servo motor actu-ates the whole finger, and the motor is coupled to the finger mechanism through two flexible wires. As the aim is to design a robotic hand prosthesis that uses artificial muscles, we propose and carry out two experiments to characterize the specifications of the actuator. The first experiment measures the pinch force of the human finger, and the second measures the achieved force using our robotic finger and five different servo motors. It allows us to enhance experimental results with the mathematical model of the finger, to identify the requirements of the artificial muscle

A posteriori error analysis of a domain decomposition algorithm for unilateral contact problem

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Modeling of composite plates with an arbitrary hole location using the variable separation method

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Adaptive analysis in elastoplasticity based on an enhanced error in constitutive relation

International audienceMany industrial analyses require the resolution of complex nonlinear problems. For such calculations, error‐controlled adaptive strategies must be used to improve the quality of the results. In this paper, adaptive strategies for nonlinear calculations in plasticity based on an enhanced error on the constitutive relation are presented. We focus on the adaptivity of the mesh and of the time discretization

Error estimation and adaptivity in elastoplasticity

International audienceIn this paper, a method is developed to control the parameters of a finite element computation for time-dependent material models. This method allows the user to obtain a prescribed accuracy with a computational cost as low as possible. To evaluate discretization errors, we use a global error measure in constitutive relation based on Drucker's inequality. This error includes, over the studied time interval, the error of the finite element model and the error of the algorithm being used. In order to master the size of the elements of the mesh and the length of the time increments, an error estimator, which permits estimating the errors due to the time discretization, is proposed. These tools are used to elaborate two procedures of adaptivity. Various examples for monotonous or non-monotonous loadings, for 2-D or axisymmetric problems, show the reliability of these procedures

Composite beam finite element based on the proper generalized decomposition

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Shell finite element based on the Proper Generalized Decomposition for the modeling of cylindrical composite structures.

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