Machine learning meso scale structure property performance relationships of energetic materials for multiscale modeling of shock induced detonation
Abstract
Performance of energetic materials such as propellants, explosives and pyrotechnics depends on local energy release phenomena at the meso scale, i.e. the scale of the particles and defects in the materials.However, to a designer, the overall system scale response of the energetic material is of interest. To predict the behavior of process scale systems that rely on energetic material response to loads, the smallscale physics must be correctly reflected in the large scale dynamics calculations. This calls for a multiscale approach connecting the small (meso) scales to the large (macro or process) scales. The proposed project seeks to advance the state of the art of multiscale modeling of heterogeneous energetic materials (HEs) by using machine learning techniques to represent and generalize the behavior of a wide class of commonly used nitramine based HEs. This proposal will advance modeling by extending a well developed capability for simulation of imaged 3 dimensional meso structures by using a state of the art computational code called SCIMITAR3D. Machine learning techniques, such as Kriging based surrogate modeling and Deep Neural Networks will be employed to learn the behavior at the meso scale and to construct functional relationships between mesostructure, material properties and performance of the HEs. It will also advance reliability by providing tools to bound uncertianties in the predictions by relating input parameter uncertainties in the inherently stochastic system to uncertainties in predicted performance. The combination of accurate computation of the shock response of heterogenous materials, surrogate model construction to link scales, uncertainty quantification in the multi scale framework and machine learning to assimilate behavior of a class of HEs will lead to a comprehensive predictive capability for HE performance. We expect that the tool developed as a result of the proposed work will be an integral part of an explosive and munitions design.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910238
Entities
People
- H. S. Udaykumar
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Iowa