In this work, the response of an ammonium perchlorate (AP)‐hydroxyl‐terminated polybutadiene (HTPB) composite material under impact loading is presented, utilizing computational cohesive finite element method (CFEM) simulations that are validated with drop hammer experiments. This study examined the impact behaviour of AP crystal sizes between 200 and 400 μm by varying impact velocities between 3 and 10 m/s. Based on the outcome of CFEM simulations, analysis of variance (ANOVA) tests and a response surface method (RSM) were utilized to construct a mathematical model approximating the relationships between simulation inputs and outcomes. Both computational and experimental results show that the local strain rate has a considerable positive correlation with crystal size, and the rate of temperature change has positive correlations with both crystal size and impact velocity. Further, it was observed that stiffness and compression energy are the primary factors to variances in local strain rate and rate of change of temperature. RSM has been found to be an effective tool for modelling impact responses of materials under varying experimental conditions.

This study utilizes in situ multipoint time-gated Raman spectroscopy to simultaneously measure molecular structural changes along with the corresponding changes in mechanical properties in shock-compressed sucrose crystals. The measurements successfully captured full-field shock compression wave propagation, leading to visualization of the elastic and plastic phases of the shock compression. The shock wave initially generates an elastic front, increasing pressure and temperature, followed by a larger elastoplastic zone marked by peak shifts and new peaks. The crystal then transitions through a metastable phase before undergoing shock-induced phase transformation, eventually reverting to a stable phase, which differs from its original undeformed state. The Raman measurements focused on CH and CH2 peak shifts under shock loading, enabling the calculation of pressure (∼5 GPa) and temperature (>1000 °C) spatial maps as a function of time during shock wave propagation. For an impact velocity of 1.2 ± 0.1 km/s, the width of the shock pulse was determined to be 125 ± 5.2 μm, and the elastic shock velocity was 6.25 ± 0.52 km/s. The energy density dissipation, derived from shifts in the CH and CH2 stretch modes, was 6.67 ± 2.11 × 107 kJ/m3 and evenly distributed relative to the pressure and temperature fields. The primary mode of failure was found to be micromechanical crushing by the forward shock wave; however, in some cases, the compaction followed by crushing allowed for the return of a rarefaction wave, leading to separation along the spall plane.

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing.