Long wave expansions for water waves over random topography

TL;DR

This paper derives effective long wave equations for water waves over random bottom topography, revealing that randomness significantly influences wave behavior and cannot be fully homogenized, with effects characterized by a white noise process.

Contribution

It extends homogenization theory for water waves over random bottoms, deriving new effective equations and analyzing the impact of randomness on wave dynamics.

Findings

Random effects are as significant as dispersion and nonlinearity.

Effective equations include a stochastic component modeled as white noise.

Random topography influences wave scattering and solution modulation.

Abstract

In this paper, we study the motion of the free surface of a body of fluid over a variable bottom, in a long wave asymptotic regime. We assume that the bottom of the fluid region can be described by a stationary random process $\beta(x, \omega)$ whose variations take place on short length scales and which are decorrelated on the length scale of the long waves. This is a question of homogenization theory in the scaling regime for the Boussinesq and KdV equations. The analysis is performed from the point of view of perturbation theory for Hamiltonian PDEs with a small parameter, in the context of which we perform a careful analysis of the distributional convergence of stationary mixing random processes. We show in particular that the problem does not fully homogenize, and that the random effects are as important as dispersive and nonlinear phenomena in the scaling regime that is studied. Our principal result is the derivation of effective equations for surface water waves in the long wave small amplitude regime, and a consistency analysis of these equations, which are not necessarily Hamiltonian PDEs. In this analysis we compute the effects of random modulation of solutions, and give an explicit expression for the scattered component of the solution due to waves interacting with the random bottom. We show that the resulting influence of the random topography is expressed in terms of a canonical process, which is equivalent to a white noise through Donsker's invariance principle, with one free parameter being the variance of the random process $\beta$. This work is a reappraisal of the paper by Rosales & Papanicolaou \cite{RP83} and its extension to general stationary mixing processes.