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SUMMARYIn this paper, a method is proposed for modeling explosive-driven fragments as spherical particles with a point-particle approach. Lagrangian particles are coupled with a multimaterial Eulerian solver that uses a three-dimensional finite volume framework on unstructured grids. The Euler-Lagrange method provides a straightforward and inexpensive alternative to directly resolving particle surfaces or coupling with structural dynamics solvers. The importance of the drag and inviscid unsteady particle forces is shown through investigations of particles accelerated in shock tube experiments and in condensed phase explosive detonation. Numerical experiments are conducted to study the acceleration of isolated explosive-driven particles at various locations relative to the explosive surface. The point-particle method predicts fragment terminal velocities that are in good agreement with simulations where particles are fully resolved, while using a computational cell size that is eight times larger. It is determined that inviscid unsteady forces are dominating for particles sitting on, or embedded in, the explosive charge. The effect of explosive confinement, provided by multiple particles, is investigated through a numerical study with a cylindrical C4 charge. Decreasing particle spacing, until particles are touching, causes a 30-50% increase in particle terminal velocity and similar increase in gas impulse.

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Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The only existing non-linear spherical CNN layer that is strictly equivariant has complexity OpC 2 L 5 q, where C is a measure of representational capacity and L the spherical harmonic bandlimit. Such a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity OpCL 4 q and OpCL 3 log Lq, making larger, more expressive models computationally feasible. Moreover, we adopt efficient sampling theory to achieve further computational savings. We show that these developments allow the construction of more expressive hybrid models that achieve state-of-the-art accuracy and parameter efficiency on spherical benchmark problems.

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An approach to modeling blast and fragment risks from improvised explosive devices, Applied Mathematical Modelling (2017),

A novel set of experiments and reactive flow modeling of pentaerythritol tetranitrate (PETN) is presented. Here, the specific phenomenon of shock to detonation transition is examined, where an initial, relatively weak shock is propagated into pressed PETN powder at 1.65 g/cm3 and the subsequent buildup to detonation is observed experimentally. These experiments, in conjunction with reactant and products’ equations of state, are utilized for building reactive flow models.

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