English

Boundary value for a nonlinear transport equation emerging from a stochastic coagulation-fragmentation type model

Probability 2015-06-17 v3 Mathematical Physics Analysis of PDEs math.MP

Abstract

We investigate the connection between two classical models of phase transition phenomena, the (discrete size) stochastic Becker-D\"oring, a continous time Markov chain model, and the (continuous size) deterministic Lifshitz-Slyozov model, a nonlinear transport partial differential equation. For general coefficients and initial data, we introduce a scaling parameter and prove that the empirical measure associated to the stochastic Becker-D\"oring system converges in law to the weak solution of the Lifshitz-Slyozov equation when the parameter goes to 0. Contrary to previous studies, we use a weak topology that includes the boundary of the state space (\ie\ the size x=0x=0) allowing us to rigorously derive a boundary value for the Lifshitz-Slyozov model in the case of incoming characteristics. The condition reads limx0(a(x)u(t)b(x))f(t,x)=αu(t)2\lim_{x\to 0} (a(x)u(t)-b(x))f(t,x) = \alpha u(t)^2 where ff is the volume distribution function, solution of the Lifshitz-Slyozov equation, aa and bb the aggregation and fragmentation rates, uu the concentration of free particles and α\alpha a nucleation constant emerging from the microscopic model. It is the main novelty of this work and it answers to a question that has been conjectured or suggested by both mathematicians and physicists. We emphasize that this boundary value depends on a particular scaling (as opposed to a modeling choice) and is the result of a separation of time scale and an averaging of fast (fluctuating) variables.

Keywords

Cite

@article{arxiv.1412.5025,
  title  = {Boundary value for a nonlinear transport equation emerging from a stochastic coagulation-fragmentation type model},
  author = {Julien Deschamps and Erwan Hingant and Romain Yvinec},
  journal= {arXiv preprint arXiv:1412.5025},
  year   = {2015}
}

Comments

42 pages, 3 figures, video on supplementary materials at http://yvinec.perso.math.cnrs.fr/video.html

R2 v1 2026-06-22T07:33:29.398Z