The unreacted equation of state (EOS) for an unreacted explosive can provide fundamental information to understand any analytical model for the shock and initiation process. Based on the Hugoniot expression in Jones–Wilkins–Lee (JWL) form derived from the Mie–Grüneisen EOS and conservation equation across the shock wave, a three-point calibrating method to determine the JWL EOS parameters for unreacted explosives was developed using intelligent algorithms and shock Hugoniot relationship of the explosives considered. The calibration method proposed utilizes the back propagation neural network to predict the nonlinear system composed of different JWL parameter sets; the genetic algorithm is then used to find the optimal solution of the JWL parameter set. Unreacted JWL EOS parameters of eight typical explosives were calibrated using the calibrating method developed, and an excellent agreement can be observed between JWL EOS and experimental p–v curves for all eight explosives selected, indicating the high accuracy of the three-point calibrating method. However, the effectiveness of the three-point calibrating method was experimentally validated with the experimental data measured from the shock tests of the dihydroxylammonium 5,5′-bitetrazole-1,1′-dioxide (TKX-50)-based explosive, where the JWL p–v curve derived from the three-point calibrating method is in good agreement with the experimental curve.

Taking pressure-bearing equipment as a prototype, the mechanical behavior of hollow corrugated sandwich cylinders under inner pressure loading was investigated. Considering the hollow cylindrical shell with finite length, the elastic closed-form solutions of hollow corrugated sandwich cylinders under inner pressure loading were presented. The optimization for minimum weight was performed. The stress distribution characteristics of the structures with different topological parameters were discussed. It can be found that an inflection point of the structure loading efficiency exists during an increase in the pressure, providing that under lower inner pressure loading, the loading efficiency of hollow corrugated sandwich structures is not invariably superior to homogeneous structures, yet while subjected to higher inner pressures, the sandwich structures exhibit a significant advantage in load-bearing efficiency.

In order to determine the parameters of the Lee–Tarver trinomial ignition growth model of explosives, a method for determining the parameters of the Lee–Tarver trinomial ignition growth model using genetic algorithm and the relationship of reaction rate is proposed. This method first extracts the characteristic points of the test pressure, and then uses the self-programmed genetic algorithm program to analyze and process the physical information of the flow field and calibrate the parameters of the Lee–Tarver trinomial ignition growth model. The results show that the pressure curve calculated by the parameters of the Lee–Tarver trinomial ignition growth model obtained by the genetic algorithm is consistent with that calculated by the standard parameters, and the maximum peak error is 6.3%, indicating the high accuracy and effectiveness of the proposed calibration method.