This research will focus on the understanding of fundamental detonation phenomena, developing appropriate experimentation, development of phenomenological models and the validation or development of computational models. Modeling approaches include conventional high-fidelity physics or Artificial Intelligence (AI) and/or Machine Learning models. Most conventional explosive models are described as composite models, which means they treat a complex reacting flow system with 1000’s of chemical reactions and intermediate material states as having only two. Those two states are joined by a reaction rate rule that governs how energy is liberated by moving between those states. Other simplifications include the homogenization of explosive microstructure within continuum models.  We are conducting research to increase the fidelity of our models by integrating mesoscale effects and relevant chemistry models into our M&S tools. This is being accomplished by a multidiscipline team of modelers, experimenters and theoreticians.

Specific interest exists around assessing the efficacy of AI and ML models. That assessment may include means to generate essential data via experimentation. By developing physics informed AI/ML tools, significant opportunities may exist to reduce complexity of experimentation which in turn may radically increase the rate at which relevant data may be generated. This may include roboticization of experimentation.

In the area of experimentation, small scale experiments are being invented or matured to facilitate the measurement of needed material characteristics such as equations-of-state for the unreacted, partially, and fully reacted energetic materials. Typical measurement techniques include dual streak cameras, Photon Doppler Velocimetry, and high-speed imaging that can be resolved to the nanosecond or sub nanosecond timescale.

The Associate will be able to design and participate in the execution of complex experiments or be an expert user/developer of models. In either case the Associate will work within a multidiscipline team to plan and execute research with primary deliverables being papers and presentations in refereed journals or conferences that are open to the general community or limited based on work content.

Parepalli, P., Hardin, D. B., Molek, C. D., Welle, E. J., & Udaykumar, H. S. (2024). Determining reaction rate parameters for an energetic material through inverse multi-scale analysis of shock-to-detonation transition. Journal of Energetic Materials, 1–36. https://doi.org/10.1080/07370652.2024.2425108

Parepalli, P., Nguyen, J.T, Sen, O., Hardin, D. B., Molek, C.D., Welle, E.J., and Udaykumar, H.S., Multi-scale modeling of shock initiation of a pressed energetic material III: Effect of Arrhenius chemical kinetic rates on macro-scale shock sensitivity, J. Appl. Phys. 135, 085106 (2024) https://doi.org/10.1063/5.0187735

Baek, S.S., Gray, Z.J., Choi, J.B., Nguyen, Y.T., Udaykumar, H.S., Welle, E.J., Molek, C.D., and Stuthers, M., Predicting Shock Initiation Threshold Using Physics-Aware Deep Learning Models, Proceedings - 17th International Detonation Symposium, IDS 2024, Vol.2, pp.964-974, 2024.

Detonation; Explosive; Optical diagnostics; Equation of state; Photon Doppler Velocimetry; Shock to detonation; Thin pulse initiation; Explosive microstructure; Laser interferometry; Modeling and Simulation; Mesoscale; Scale Bridging; Machine Learning; Artificial Intelligence

citizenship

Open to U.S. citizens

level

Open to Postdoctoral and Senior applicants

Additional Benefits

relocation

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

health insurance

A group health insurance program is available to awardees and their qualifying dependents in the United States.