Non‐Shock Ignition Probability of Octahydro‐1,3,5,7‐Tetranitro‐Tetrazocine‐Based Polymer Bonded Explosives Based on Microcrack Stochastic Distribution

Abstract

We investigate stochastic microcracks of a heterogeneous microstructure as the primary mechanism determining the non‐shock ignition probability of octahydro‐1,3,5,7‐tetranitro‐1,2,3,5‐tetrazocine‐based polymer‐bonded explosives. To quantify the ignition probability, we modify the viscoelastic cracking constitutive model by considering randomly distributed microcracks and combine this model with the hot‐spot model and the Monte Carlo method. This method is validated by using a standard Steven test simulation, and we quantify how the microcrack stochasticity affects the ignition probability. The high‐temperature region clearly changes compared with the case of single microcrack size. The discrepancy in the mechanical response of each element due to the heterogeneous microcracks causes a shear deformation, which increases the temperature. The difference between a uniform distribution and a normal distribution in microcrack size is also discussed. For a given impact velocity, the ignition probability for the normal microcrack size distribution exceeds that for the uniform microcrack size distribution. Although the two distributions have the same mean and variance, the real range in microcrack size corresponding to the normal distribution exceeds that for the uniform distribution. The results show that the sample radius does not significantly affect the ignition probability, whereas the opposite is true for the sample thickness. The ignition‐probability curve is obtained in these cases. For sample dimensions of φ70 mm×13 mm, φ98 mm×13 mm, φ140 mm×13 mm, and φ98 mm×26 mm, the impact velocity thresholds corresponding to ignition probabilities in the range 0 %–99 % are 41.0–43.7, 41.0–44.3, 40.0–44.6, and 62.0–67.2 m/s, respectively, which is consistent with experimental results.

Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model

“…e SR model is good at the solution of high nonlinear problems between input variables and output response, by introducing a maximum classification margin subject to inequality constraints [30]. Hence, the SR can improve the computational efficiency and accuracy of structural reliability optimization [9]. As for large-scale parameters and high-nonlinearity problems, the traditional SR is easy to fall into local optimization in the process of searching hyperparameter, which affects the precision of structural reliability optimization.…”

“…Blade material is GH4133B alloy [36]. Based on the random variable selection method [9], blade density, rotor rotational speed, gas temperature, gas pressure, and gravity acceleration are termed input random variables (parameters). e distribution features of the parameters are listed in 1, in which all the variables follow normal distributions of mutual independence.…”

“…With the requirement of design analysis for advanced mechanical systems, many reliability optimization methods have emerged in engineering practice [4][5][6][7][8][9]. Surrogate model method (also called response surface method) is a promising way to ensure efficiency and accuracy in structural reliability optimization, instead of the direct simulation method with the FE model [4].…”

Fatigue Reliability Analysis of Motor Hanger for High‐Speed Train Based on Bayesian Updating and Subset Simulation

“…e data of prior distribution obey the lognormal distribution of N(4.945, 0.041), and the real measured data obey the lognormal distribution of N(4.962, 0.0425). Two kinds of distribution data are substituted into equations (2)- (7) to obtain the probability and statistical parameters of the FPS after Bayesian updating. Figure 8 plots the probability distribution of the FPS before and after Bayesian updating.…”

“…But, the disadvantages of this method are that the results of evaluation and analysis are too ideal to consider the design tolerance and manufacturing error, and the comparison with the experimental results is too single and not universal, which is not enough to reflect the fatigue reliability of all products. In response to the above problems, some scholars have combined the reliability theory and fatigue analysis theory to study the fatigue reliability of mechanical products and proposed some fatigue reliability analysis methods [2][3][4][5][6][7][8][9][10][11][12]. Chen et al [13] proposed a fatigue reliability analysis method that considers the uncertainty of parameters, draws a fatigue limit diagram without safety factor, and gives a more reasonable fatigue reliability analysis result, which is helpful for lightweight design of products.…”