Spectral - Lagrangian methods for Collisional Models of Non - Equilibrium Statistical States

Details Spectral - Lagrangian methods for Collisional Models of Non - Equilibrium Statistical States Spectral - Lagrangian methods for Collisional Models of Non - Equilibrium Statistical States

2007-10-28

arxiv.org/abs/0710.5308v2

Physics

Mathematical Physics

41 pages with 15 figures

Scientific paper

We propose a new spectral Lagrangian based deterministic solver for the non-linear Boltzmann Transport Equation for Variable Hard Potential (VHP) collision kernels with conservative or non-conservative binary interactions. The method is based on symmetries of the Fourier transform of the collision integral, where the complexity in its computing is reduced to a separate integral over the unit sphere $S^2$. In addition, the conservation of moments is enforced by Lagrangian constraints. The resulting scheme, implemented in free space is very versatile and adjusts in a very simple manner, to several cases that involve energy dissipation due to local micro-reversibility (inelastic interactions) or elastic model of slowing down process. Our simulations are benchmarked with the available exact self-similar solutions, exact moment equations and analytical estimates for homogeneous Boltzmann equation for both elastic and inelastic VHP interactions. Benchmarking of the simulations involves the selection of a time self-similar rescaling of the numerical distribution function which is performed using the continuous spectrum of the equation for Maxwell molecules as studied first in \cite{asympGranular} and generalized to a wide range of related models in \cite{BCG}. The method also produces accurate results in the case of inelastic diffusive Boltzmann equations for hard-spheres (inelastic collisions under thermal bath), where overpopulated non-Gaussian exponential tails have been conjectured in computations by stochastic methods in \cite{vanNoi-Ernst, ernst-brito, MSS, GRW04} and rigourously proven in \cite{diffGran} and \cite{highEnergyTails}.

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