The two-phase mixture model developed by Baer and Nunziato ͑BN͒ to study the deflagration-to-detonation transition ͑DDT͒ in granular explosives is critically reviewed. The continuum-mixture theory foundation of the model is examined, with particular attention paid to the manner in which its constitutive functions are formulated. Connections between the mechanical and energetic phenomena occurring at the scales of the grains, and their manifestations on the continuum averaged scale, are explored. The nature and extent of approximations inherent in formulating the constitutive terms, and their domain of applicability, are clarified. Deficiencies and inconsistencies in the derivation are cited, and improvements suggested. It is emphasized that the entropy inequality constrains but does not uniquely determine the phase interaction terms. The resulting flexibility is exploited to suggest improved forms for the phase interactions. These improved forms better treat the energy associated with the dynamic compaction of the bed and the single-phase limits of the model. Companion papers of this study ͓Kapila et al., Phys. Fluids 9, 3885 ͑1997͒; Kapila et al., in preparation; Son et al., in preparation͔ examine simpler, reduced models, in which the fine scales of velocity and pressure disequilibrium between the phases allow the corresponding relaxation zones to be treated as discontinuities that need not be resolved in a numerical computation.

An efficient approach that combines short-term (minutes) highenergy dry ball milling and wet grinding to tailor the nano-and microstructure of Ni +Al composite reactive particles is reported. Varying the ball-milling conditions allows control of the volume fraction of two distinct milling-induced microstructures, that is, coarse and nanolaminated. It is found that increasing the fraction of nanolaminated structure present in the composite particles leads to a decrease in their ignition temperature (T ig ) from 700 and 500 K. Material with nanolaminated microstructure is also found to be more sensitive to impact ignition when compared with particles with a coarse microstructure. It is shown that kinetic energy (W cr ) thresholds for impact ignition, obtained for an optimized nanolaminated microstructure, is only 100 J. High-speed imaging showed that the impact-induced ignition occurs through formation of hot spots caused by impact. Molecular dynamic simulations of a model system suggest that impact-induced localized plastic deformation raises the local temperatures to ∼600 K, enough to initiate exothermic reactions. Analysis of the kinetics and reaction mechanism shows that the reason for low T ig and W cr for nanolaminated microstructure is the rapid solid-state dissolution of nickel in aluminum lattices.

Boron nanoparticles prepared by milling in the presence of a hypergolic energetic ionic liquid (EIL) are suspendable in the EIL and the EIL retains hypergolicity leading to the ignition of the boron. This approach allows for incorporation of a variety of nanoscale additives to improve EIL properties, such as energetic density and heat of combustion, while providing stability and safe handling of the nanomaterials.

The structure of the velocity relaxation zone in a hyperbolic, nonconservative, two-phase model is examined in the limit of large drag, and in the context of the problem of deflagration-to-detonation transition in a granular explosive. The primary motivation for the study is the desire to relate the end states across the relaxation zone, which can then be treated as a discontinuity in a reduced, equivelocity model, that is computationally more efficient than its parent. In contrast to a conservative system, where end states across thin zones of rapid variation are determined principally by algebraic statements of conservation, the nonconservative character of the present system requires an explicit consideration of the structure. Starting with the minimum admissible wave speed, the structure is mapped out as the wave speed increases. Several critical wave speeds corresponding to changes in the structure are identified. The archetypal structure is partly dispersed, monotonic, and involves conventional hydrodynamic shocks in one or both phases. The picture is reminiscent of, but more complex than, what is observed in such ͑simpler͒ two-phase media as a dusty gas.

We 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. This work demonstrates the first use of complete 3D electronic structure for machine learning of molecular properties.