Generalized Langevin equation with shear flow and its fluctuation-dissipation theorems derived from a Caldeira-Leggett Hamiltonian
Sara Pelargonio, Alessio Zaccone

TL;DR
This paper derives a first-principles generalized Langevin equation for particles under shear flow, revealing its non-Markovian nature at higher shear rates and establishing the conditions under which simplified models are valid.
Contribution
It provides a first-principles derivation of the Langevin equation with shear flow from a Caldeira-Leggett Hamiltonian, clarifying the validity of common approximations.
Findings
Standard Langevin equation is valid only at very weak shear rates.
At higher shear rates, the Langevin dynamics become strongly non-Markovian.
Derived fluctuation-dissipation theorems applicable to sheared systems.
Abstract
We provide a first-principles derivation of the Langevin equation with shear flow and its corresponding fluctuation-dissipation theorems. Shear flow of simple fluids has been widely investigated by numerical simulations. Most studies postulate a Markovian Langevin equation with a simple shear drag term \`a la Stokes. However, this choice has never been justified from first principles. We start from a particle-bath system described by a classical Caldeira-Leggett Hamiltonian modified by adding a term proportional to the strain-rate tensor according to Hoover's DOLLS method, and we derive a generalized Langevin equation for the sheared system. We then compute, analytically, the noise time-correlation functions in different regimes. Based on the intensity of the shear-rate, we can distinguish between close-to-equilibrium and far-from-equilibrium states. According to the results presented…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Statistical Mechanics and Entropy · Complex Systems and Time Series Analysis
