SearcharxivSearch

arXiv subjects

Cale Harnish

Publications and source records attributed to Cale Harnish.

3 recordsLinked to original sources

On a spectral method for $\beta$-particle bound excitation collisions in kilonovae

The interaction of $\beta$-particles with the weakly ionized plasma background is an important mechanism for powering the kilonova transient signal from neutron star mergers. For this purpose, we present an implementation of the approximate fast-particle collision kernel, described by Inokuti (1971) following the seminal formulation of Bethe (1930), in a spectral solver of the Vlasov-Maxwell-Boltzmann equations. In particular, we expand the fast-particle plane-wave atomic excitation kernel into coefficients of the Hermite basis, and derive the relevant discrete spectral system. In this fast-particle limit, the approach permits the direct use of atomic data, including optical oscillator strengths, normally applied to photon-matter interaction. The resulting spectral matrix is implemented in the MASS-APP spectral solver framework, in a way that avoids full matrix storage per spatial zone. We numerically verify aspects of the matrix construction, and present a proof-of-principle 3D simulation of a 2D axisymmetric kilonova ejecta snapshot. Our preliminary numerical results indicate that a reasonable choice of Hermite basis parameters for $\beta$-particles in the kilonova are a bulk velocity parameter $\vec{u}=0$, a thermal velocity parameter $\vec{\alpha}=0.5c$, and a 9x9x9 mode velocity basis set (Hermite orders 0 to 8 in each dimension). For ejecta-interior sample zones, we estimate the ratio of thermalization from large-angle ($\gtrsim2.5^{\circ}$) bound excitation scattering to total thermalization is $\sim$0.002-0.003.

astro-ph.HE

An adaptive wavelet method for nonlinear partial differential equations with applications to dynamic damage modeling

Multiscale and multiphysics problems need novel numerical methods in order for them to be solved correctly and predictively. To that end, we develop a wavelet based technique to solve a coupled system of nonlinear partial differential equations (PDEs) while resolving features on a wide range of spatial and temporal scales. The algorithm exploits the multiresolution nature of wavelet basis functions to solve initial-boundary value problems on finite domains with a sparse multiresolution spatial discretization. By leveraging wavelet theory and embedding a predictor-corrector procedure within the time advancement loop, we dynamically adapt the computational grid and maintain accuracy of the solutions of the PDEs as they evolve. Consequently, our method provides high fidelity simulations with significant data compression. We present verification of the algorithm and demonstrate its capabilities by modeling high-strain rate damage nucleation and propagation in nonlinear solids using a novel Eulerian-Lagrangian continuum framework.

math.NA

A multiresolution adaptive wavelet method for nonlinear partial differential equations

The multiscale complexity of modern problems in computational science and engineering can prohibit the use of traditional numerical methods in multi-dimensional simulations. Therefore, novel algorithms are required in these situations to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. To meet these challenges, we present a multiresolution wavelet algorithm to solve PDEs with significant data compression and explicit error control. We discretize in space by projecting fields and spatial derivative operators onto wavelet basis functions. We provide error estimates for the wavelet representation of fields and their derivatives. Then, our estimates are used to construct a sparse multiresolution discretization which guarantees the prescribed accuracy. Additionally, we embed a predictor-corrector procedure within the temporal integration to dynamically adapt the computational grid and maintain the accuracy of the solution of the PDE as it evolves. We present examples to highlight the accuracy and adaptivity of our approach.

math.NA