Artificial neural network–based constitutive relation modelling for the laminated fabric used in stratospheric airship

Abstract

There have been gradually-increasing interests in the stratospheric airship (SSA) as a cost-effective alternative to earth orbit satellites for telecommunication and high-resolution earth observation. Lightweight and high strength envelopes are the keys to the design of SSAs, as it directly determines the endurance flight performance and loading deformation characteristics of the airship. Typical SSA envelope material is a laminated fabric, which is composed of fabric layer and other functional layers. Compared with conventional composite structures, the laminated fabric has complex nonlinear mechanical characteristics. Artificial neural network (ANN) has good processing ability to nonlinear information so that it is suitable to model the constitutive relation of laminated fabrics. In this work, an ANN based on the Scaled Conjugate Gradient (SCG) algorithm is proposed firstly to model the constitutive relation of fabric Uretek3216LV. Considering significant errors of the SCG ANN results, the network model is optimized through methods of selecting the number of hidden-layer nodes and training algorithms. Results show that the improved network model based on Bayesian Regularization (BR) algorithm and eight nodes of single hidden layer can better describe the constitutive relation of the laminated fabric than other conventional training algorithms. The proposed constitutive modelling method with ANN is expected to gain a deeper understanding of the mechanical mechanism and guide structural design of envelope material in further work.