Integrating Multiscale Modeling and Experiments to Develop a Meso Informed Predictive Capability for Explosives Safety and Performance
Abstract
We propose tightly integrated experimental, computational, and theoretical research that will result in transformational next generation reactive burn models for the ignition, initiation, and detonation of heterogeneous energetic (HE) materials. Accurate, predictive burn models are required to design munitions with precise, well controlled energy release. By combining cutting edge high throughput physical chemistry and mechanics experiments, advanced meso scale continuum simulation methods, and machine learning, augmented by detailed, quantified descriptions of thermo physical, mechanical, and chemical properties, we will develop a meso informed, micro structure aware reactive burn model. This burn model, MISSEL (for Micro structure Informed Surrogate Surface for Energy Localization), will be broadly applicable as a framework for CHNO HE materials such as HMX, RDX, PETN, TATB, and polymer bonded formulations derived from them. MISSEL will be expressed in the form of a surrogate model suitable for practical use in DoD-NNSA codes such as ALE3D and xRAGE. The broader outcome of this collaborative effort will be significant advances in methodologies related to energetic materials research. Key specific advances will include: 1) Improved meso scale models embedding fundamentally established thermophysical and mechanical models; 2) New sub scale physics based models for continuum mechanics with properties and parameters fine tuned using atomistic simulations and experiments; 3) New ideas in machine learning and efficient surrogate model construction techniques to bridge scales; 4) Methods for developing reactive mechanics models for HE crystals, binders, and interfaces; 5) New experiments designed and executed for measuring hitherto difficult to measure temperature, stress, and species evolution. Thus, the proposed work will have wide impact in terms of the research output, student and postdoctoral training and knowledge generation that will benefit DoD-NNSA codes.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910318
Entities
People
- Thomas Sewell
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Missouri System