Explicit Runge-Kutta algorithm to solve non-local equations with memory effects: case of the Maxey-Riley-Gatignol equation

TL;DR

This paper introduces an explicit Runge-Kutta algorithm tailored for non-local equations with memory effects, exemplified by the Maxey-Riley-Gatignol equation, enabling efficient numerical solutions for complex integro-differential systems.

Contribution

The authors develop a novel Runge-Kutta scheme for equations with memory by embedding them into an extended Markovian system, allowing standard integration techniques to be applied.

Findings

The method effectively solves the Maxey-Riley-Gatignol equation.

It maintains constant memory storage and linear computational effort.

The approach can be generalized to other non-local equations with memory.

Abstract

A standard approach to solve ordinary differential equations, when they describe dynamical systems, is to adopt a Runge-Kutta or related scheme. Such schemes, however, are not applicable to the large class of equations which do not constitute dynamical systems. In several physical systems, we encounter integro-differential equations with memory terms where the time derivative of a state variable at a given time depends on all past states of the system. Secondly, there are equations whose solutions do not have well-defined Taylor series expansion. The Maxey-Riley-Gatignol equation, which describes the dynamics of an inertial particle in nonuniform and unsteady flow, displays both challenges. We use it as a test bed to address the questions we raise, but our method may be applied to all equations of this class. We show that the Maxey-Riley-Gatignol equation can be embedded into an extended Markovian system which is constructed by introducing a new dynamical co-evolving state variable that encodes memory of past states. We develop a Runge-Kutta algorithm for the resultant Markovian system. The form of the kernels involved in deriving the Runge-Kutta scheme necessitates the use of an expansion in powers of $t^{1/2}$. Our approach naturally inherits the benefits of standard time-integrators, namely a constant memory storage cost, a linear growth of operational effort with simulation time, and the ability to restart a simulation with the final state as the new initial condition.