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

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