Predicting the near field underwater explosion response of coated composite cylinders using multiscale simulations, experiments, and machine learning

Abstract

Prediction of underwater explosion response of coated composite cylinders using machine learning (ML) requires a large, consistent, accurate, and representative dataset. However, such reliable large experimental dataset is not readily available. Besides, the ML algorithms need to abide by the fundamental laws of physics to avoid non-physical predictions. To address these challenges, this paper synergistically integrates ML with high-throughput multiscale finite element (FE) simulations to predict the response of coated composite cylinders subjected to nearfield underwater explosion. The simulated responses from the multiscale approach correlate very well with the experimental observations. After validation of the multiscale approach, a representative and consistent dataset containing more than 3800 combinations is developed using high-throughput multiscale simulation by varying the fiber/matrix/coating material properties, coating thickness as well as experimental variables such as explosive energy and stand-off distance. The dataset is leveraged to predict the response of coated composite cylinders subjected to nearfield underwater explosion using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent predictions. Overall, the synergistic approach powered by physics-based simulations presented here can potentially enable materials scientists and engineers to make intelligent, informed decisions in the purview of innovative design strategies for underwater explosion mitigation in composite structures.