Seokpum Kim

Prediction of probabilistic ignition behavior of polymer-bonded explosives from microstructural stochasticity

Random variations in constituent properties, constituent distribution, microstructural morphology, and loading cause the ignition of explosives to be inherently stochastic. An approach is developed to computationally predict and quantify the stochasticity of the ignition process in polymer-bonded explosives (PBXs) under impact loading. The method, the computational equivalent of carrying out multiple experiments under the same conditions, involves subjecting sets of statistically similar microstructure samples to identical overall loading and characterizing the statistical distribution of the ignition response of the samples. Specific quantities predicted based on basic material properties and microstructure attributes include the critical time to ignition at given load intensity and the critical impact velocity below which no ignition occurs. The analyses carried out focus on the influence of random microstructure geometry variations on the critical time to ignition at given load intensity and the critic...

Ignition probability of polymer-bonded explosives accounting for multiple sources of material stochasticity

Accounting for the combined effect of multiple sources of stochasticity in material attributes, we develop an approach that computationally predicts the probability of ignition of polymer-bonded explosives (PBXs) under impact loading. The probabilistic nature of the specific ignition processes is assumed to arise from two sources of stochasticity. The first source involves random variations in material microstructural morphology; the second source involves random fluctuations in grain-binder interfacial bonding strength. The effect of the first source of stochasticity is analyzed with multiple sets of statistically similar microstructures and constant interfacial bonding strength. Subsequently, each of the microstructures in the multiple sets is assigned multiple instantiations of randomly varying grain-binder interfacial strengths to analyze the effect of the second source of stochasticity. Critical hotspot size-temperature states reaching the threshold for ignition are calculated through finite element ...

Molecular Dynamic Simulation on the Effect of Polymer Molecular Size in Thermal Nanoimprint Lithographic (T-NIL) Process

Molecular dynamic simulation of thermal nanoimprint lithographic (T-NIL) process has been performed to study the effect of the polymer molecular size on polymer flow for various mold cavity geometries. First, simulations of T-NIL process with several temperature settings were performed to determine the optimal temperature for the process. Simulations were also done to obtain the size of polymer molecule represented by the radius of gyration (Rg). Then, the relation between the Rg of the polymer and the processibility was investigated for various mold cavity sizes. The results showed that there existed a minimum cavity size for the Rg value of polymer for successful processing. Based on the results, it was shown that the polymer cannot be well patterned if the mold cavity size becomes 2Rg of polymer or smaller. Therefore, it could be concluded that the Rg value of a polymer can be a good indicating parameter when choosing the suitable pattern size.

Computational prediction of probabilistic ignition threshold of pressed granular octahydro-1,3,5,7-tetranitro-1,2,3,5-tetrazocine (HMX) under shock loading

The probabilistic ignition thresholds of pressed granular octahydro-1,3,5,7-tetranitro-1,2,3,5-tetrazocine explosives with average grain sizes between 70 μm and 220 μm are computationally predicted. The prediction uses material microstructure and basic constituent properties and does not involve curve fitting with respect to or prior knowledge of the attributes being predicted. The specific thresholds predicted are James-type relations between the energy flux and energy fluence for given probabilities of ignition. Statistically similar microstructure sample sets are computationally generated and used based on the features of micrographs of materials used in actual experiments. The predicted thresholds are in general agreement with measurements from shock experiments in terms of trends. In particular, it is found that grain size significantly affects the ignition sensitivity of the materials, with smaller sizes leading to lower energy thresholds required for ignition. For example, 50% ignition threshold of...