Prediction of tear propagation path of stratospheric airship envelope material based on deep learning

Abstract

Tear propagation of the envelope material could cause fatal damage to the stratospheric airship (SSA) and it is very important to detect the crack and predict its tear propagation path. A hybrid deep neural network (DNN) model is proposed in this paper to predict tear propagation of the SSA envelope material mainly including stress field predictor and crack map predictor by considering the time and spatial characteristics. The Gated Recurrent Unit (GRU) is applied by using the gating network signaling that control how the present input and previous memory for the stress field predictor. A Feature Pyramid Network (FPN)-based faster region-based Convolutional Neural Network (CNN) is proposed to predict the crack location by declaring the crack propagation direction and velocity of the envelope material. Furthermore, two deformable operation modules are embedded into the crack detector to achieve better identification of out-of-plane cracks of the envelope material for a real airship with curvature. The dataset is obtained by extended finite element method (XFEM) analysis. The proposed approach has potential applications in the field of envelope material design and structural health monitoring of the SSA.