Deep learning aided topology optimization of phononic crystals

In this work, a novel approach for the topology optimization of phononic crystals based on the replacement of the computationally demanding traditional solvers for the calculation of dispersion diagrams with a surrogate deep learning (DL) model is proposed. We show that our trained DL model is ultrafast in the prediction of the dispersion diagrams, and therefore can be efficiently used in the optimization framework

Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available

Solveur TLM multi-physique pour applications en dosimétrie

National audienceDans cette contribution, nous résumons le travail réalisé dans le Projet MEDUSES 2 en collaboration entre le Lab-STICC à IMT-Atlantique, le LEAT à l'Université Côte d'Azur et la DGA-TA à Toulouse. L'objectif principal de ce projet est de construire un solveur multi-physique robuste pour les phénomènes électromagnétiques et thermiques pour simuler des scénarios réalistes, tels que le calcul du DAS dans le corps humain et son élévation de température lorsqu'il est exposé à un rayonnement EM produit par une antenne. Les deux phénomènes physiques sont modélisésdans le domaine temporel avec un code de calcul basé sur la méthode TLM (Transmission-Line Matrix Method)