Evaluation of the importance of void shapes in predictive meso-informed ignition and growth surrogate models for heterogeneous energetic materials

The void collapse behavior of a heterogeneous energetic material at the mesoscale, leading to energy localization, is dependent on void geometrical features. In MES-IG, a meso-informed ignition and growth model, the surrogate reaction rate is taken as a function of shock loading as well as void morphometry. In this paper, we focus on the effects of void shapes on the performance of MES-IG. A large void collection of arbitrary shapes is extracted from SEM images of real, pressed HMX samples and classified into groups based on similarity in their shapes. Reactive void collapse direct numerical simulation (DNS) is performed using SCIMITAR3D code. The reaction rates obtained from DNS are compared with their counterpart MES-IG values for voids within each group and across groups. It is found that overall, the parameterization of complex void morphometry using orientation and aspect ratio gives fairly good agreement between DNS and MES-IG in reaction rate prediction. The intricate details of highly complex void shapes, however, do impact their hotspot characteristics to a significant extend. This work suggests possible improvements for the prediction of reaction rate in energetic microstructure by adopting a more detailed shape analysis, which points to future work.