PREDICTING ENERGETIC MATERIAL PROPERTIES AND INVESTIGATING THE EFFECT OF PORE MORPHOLOGY ON SHOCK SENSITIVITY VIA MACHINE LEARNING
thesisposted on 28.07.2020, 13:25 by Alex Donald CaseyAlex Donald Casey
An improved understanding of energy localization ("hot spots'') is needed to improve the safety and performance of explosives. In this work I establish a variety of experimental and computational methods to aid in the investigation of hot spots. In particular, focus is centered on the implicit relationship between hot spots and energetic material sensitivity. To begin, I propose a technique to visualize and quantify the properties of a dynamic hot spot from within an energetic composite subjected to ultrasonic mechanical excitation. The composite is composed of an optically transparent binder and a countable number of HMX crystals. The evolving temperature field is measured by observing the luminescence from embedded phosphor particles and subsequent application of the intensity ratio method. The spatial temperature precision is less than 2% of the measured absolute temperature in the temperature regime of interest (23-220 C). The temperature field is mapped from within an HMX-binder composite under periodic mechanical excitation.
Following this experimental effort I examine the statistics behind the most prevalent and widely used sensitivity test (at least within the energetic materials community) and suggest adaptions to generalize the approach to bimodal latent distributions. Bimodal latent distributions may occur when manufacturing processes are inconsistent or when competing initiation mechanisms are present.
Moving to simulation work, I investigate how the internal void structure of a solid explosive influences initiation behavior -- specifically the criticality of isolated hot spots -- in response to a shock insult. In the last decade, there has been a significant modeling and simulation effort to investigate the thermodynamic response of a shock induced pore collapse process in energetic materials. However, the majority of these studies largely ignore the geometry of the pore and assume simplistic shapes, typically a sphere. In this work, the influence of pore geometry on the sensitivity of shocked HMX is explored. A collection of pore geometries are retrieved from micrographs of pressed HMX samples via scanning electron microscopy. The shock induced collapse of these geometries are simulated using CTH and the response is reduced to a binary "critical'’ / "sub-critical'' result. The simulation results are used to assign a minimum threshold velocity required to exhibit a critical response to each pore geometry. The pore geometries are subsequently encoded to numerical representations and a functional mapping from pore shape to a threshold velocity is developed using supervised machine-learned models. The resulting models demonstrate good predictive capability and their relative performance is explored. The established models are exposed via a web application to further investigate which shape features most heavily influence sensitivity.
Finally, I develop a convolutional neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential represented as a 4D tensor. This method effectively bypasses the need to construct complex representations, or descriptors, of a molecule. This is beneficial because the accuracy of a machine learned model depends on the input representation. Ideally, input descriptors encode the essential physics and chemistry that influence the target property. Thousands of molecular descriptors have been proposed and proper selection of features requires considerable domain expertise or exhaustive and careful statistical downselection. In contrast, deep learning networks are capable of learning rich data representations. This provides a compelling motivation to use deep learning networks to learn molecular structure-property relations from "raw'' data. The convolutional neural network model is jointly trained on over 20,000 molecules that are potentially energetic materials (explosives) to predict dipole moment, total electronic energy, Chapman-Jouguet (C-J) detonation velocity, C-J pressure, C-J temperature, crystal density, HOMO-LUMO gap, and solid phase heat of formation. To my knowledge, this demonstrates the first use of the complete 3D electronic structure for machine learning of molecular properties.