The research presented in this dissertation presents a methodology to experimentally predict and simulate the mechanical behavior of polymers under high strain rate deformation. Specifically, the interplay between the effects of temperature and strain rate on polymer behavior is examined and then used as a tool to help recreate the high rate mechanical response of several different polymers: ranging from rubbers to amorphous polymers to composites. Multiple literature reviews are conducted and presented in this thesis, e.g. experimental mechanics test methods, high rate behavior, time-temperature equivalence, constitutive modeling, and temperature measurement methods. In accordance with mechanical theory, an experimental and analytical protocol in rate- and temperature- dependence was applied to a range of PVC materials ranging in plasticizer contents. Further to this, these PVC materials were modeled with a rubbery model describing the network stress seen in polymer behavior, and an amorphous polymer model to describe PVC low to high rate responses to deformation. This modeling develops insights in the adiabatic nature of high rate response. Time-temperature equivalence, and the temperature rise during adiabatic deformation, are studied and exploited in order to implement a proposed experimental method which simulates the high rate deformation of polymeric materials. The development of an experimental methodology to simulate and predict high rate behavior is presented, applied, and expanded to a range of materials: amorphous polymers (e.g. PVC 20wt% plasticizer, PMMA, PC) and composites (e.g. polymer bonded explosive simulant). The work also presents and highlights the fact that micro to nano-scale imaging may be used in parallel with the simulation method in order to better understand high rate behavior. Furthermore, in result of the studies conducted in this body of work, several novel techniques were developed, or improved upon, and applied to the current research (e.g. additions to time-temperature equivalence, temperature measurement methods at high, moderate, and low strain rates, and a method for measuring the high rate behavior of soft materials).

<p dir="ltr">Energetic materials contain microstructural defects like cracks, voids, grain boundaries, and interfaces which act as nucleation sites for ignition and detonation when shocked. Finite element (FE) models are currently unable to capture explicit microstructure with voids, cracks, and randomly oriented grains with representative mechanics, thermal conduction, and reactivity that exhibit the full shock to detonation transition (SDT). Modern computational efforts seek to accurately model material response while also balancing efficiency and speed. Work presented in this thesis will highlight all of these microstructural features, investigate mechanical and thermal response of each microstructure, connect these results to what is observed in other experimental and computational work, and bring computational modeling even closer to an efficient model that contains all processes necessary to replicate SDT.</p><p dir="ltr">In energetic materials (EM), voids are irregular in shape, but most computational work has focused on circular void collapse behavior. However, geometries that contain irregularities or corners are more likely to act as initiation sites due to stress concentrations. Validation and calibration of void simulations with experimental lengthscales and loading conditions is still limited. Plus, pore collapse modeling efforts at low impact velocities do not model fracture, and it is known that cracks cause more extreme temperatures than pores.</p><p dir="ltr">Other microstructure characteristics like cracks and grains have sub-micrometer length scale, and influence the mechanical and thermal response of materials under extreme conditions. However, approximations and coarse-graining must be applied to continuum FE simulations to fit length and timescales required to capture phenomena such as detonations that occur at a millimeter scale. With the use of machine learning (ML), numerical models can be trained on results of small-scale microstructure simulations and applied to larger length and time-scale simulations. The ML model follows Microstructure-Informed Shock-induced Temperature net (MISTnet) model and is trained upon stress, strain, temperature, pressure, and slip data and includes crystal plasticity, fracture, friction, an equation of state, and heat conduction. The ML model is able to predict temperature fields behind the shock, concentrations at grain boundaries, and the influence of grain orientation.</p><p dir="ltr">Accurate temperature values are extremely important to modeling EM because thermal hot spots (HS) are the main cause of ignition. Critical HS cause the chemical reactions which transition the shock front into a detonation, but many continuum models do not include chemistry in their framework. A 1-step Arrhenius reaction model is added to FE mechanics model to investigate the relationship HS have on the run to detonation (RTD).</p>