Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMarkov State Modeling of Sliding Friction
117
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Markov State Modeling has recently emerged as a key technique for analyzing rare events in thermal equilibrium molecular simulations and finding metastable states. Here we export this technique to the study of friction, where strongly non-equilibrium events are induced by an external force. The approach is benchmarked on the well-studied Frenkel-Kontorova model, where we demonstrate the unprejudiced identification of the minimal basis microscopic states necessary for describing sliding, stick-slip and dissipation. The steps necessary for the application to realistic frictional systems are highlighted.
rate research
Read More
Non-equilibrium Markov State Modeling (MSM) has recently been proposed [Phys. Rev. E 94, 053001 (2016)] as a possible route to construct a physical theory of sliding friction from a long steady state atomistic simulation: the approach builds a small set of collective variables, which obey a transition-matrix based equation of motion, faithfully describing the slow motions of the system. A crucial question is whether this approach can be extended from the original 1D small size demo to larger and more realistic size systems, without an inordinate increase of the number and complexity of the collective variables. Here we present a direct application of the MSM scheme to the sliding of an island made of over 1000 harmonically bound particles over a 2D periodic potential. Based on a totally unprejudiced phase space metric and without requiring any special doctoring, we find that here too the scheme allows extracting a very small number of slow variables, necessary and sufficient to describe the dynamics of island sliding.
With the purpose of explaining recent experimental findings, we study the distribution $A(lambda)$ of distances $lambda$ traversed by a block that slides on an inclined plane and stops due to friction. A simple model in which the friction coefficient $mu$ is a random function of position is considered. The problem of finding $A(lambda)$ is equivalent to a First-Passage-Time problem for a one-dimensional random walk with nonzero drift, whose exact solution is well-known. From the exact solution of this problem we conclude that: a) for inclination angles $theta$ less than $theta_c=tan(av{mu})$ the average traversed distance $av{lambda}$ is finite, and diverges when $theta to theta_c^{-}$ as $av{lambda} sim (theta_c-theta)^{-1}$; b) at the critical angle a power-law distribution of slidings is obtained: $A(lambda) sim lambda^{-3/2}$. Our analytical results are confirmed by numerical simulation, and are in partial agreement with the reported experimental results. We discuss the possible reasons for the remaining discrepancies.
We propose a model based on extreme value statistics (EVS) and combine it with different models for single asperity contact, including adhesive and elasto-plastic contacts, to derive a relation between the applied load and the friction force on a rough interface. We find that when the summit distribution is Gumbel, and the contact model is Hertzian we have the closest conformity with Amontons law. The range over which Gumbel distribution mimics Amontons law is wider than the Greenwood-Williamson Model. However exact conformity with Amontons law does not seem for any of the well-known EVS distributions. On the other hand plastic deformations in contact area reduce the relative change of pressure slightly with Gumbel distribution. Elastic-plastic contact mixes with Gumbel distribution for summits. it shows the best conformity with Amonton`s law. Other extreme value statistics are also studied, and results presented. We combine Gumbel distribution with GW-Mc Cool model which is an improved case of GW model, it takes into account a bandwidth for wavelengths of {alpha}. Comparison of this model with original GW-Mc Cool model and other simplifie
Rate processes are often modeled using Markov-State Models (MSM). Suppose you know a prior MSM, and then learn that your prediction of some particular observable rate is wrong. What is the best way to correct the whole MSM? For example, molecular dynamics simulations of protein folding may sample many microstates, possibly giving correct pathways through them, while also giving the wrong overall folding rate, when compared to experiment. Here, we describe Caliber Corrected Markov Modeling (C2M2): an approach based on the principle of maximum entropy for updating a Markov model by imposing state- and trajectory- based constraints. We show that such corrections are equivalent to asserting position-dependent diffusion coefficients in continuous-time continuous-space Markov processes modeled by a Smoluchowski equation. We derive the functional form of the diffusion coefficient explicitly in terms of the trajectory-based constraints. We illustrate with examples of 2D particle diffusion and an overdamped harmonic oscillator.
In this paper, we propose a new derivation for the Green-Kubo relationship for the liquid-solid friction coefficient, characterizing hydrodynamic slippage at a wall. It is based on a general Langevin approach for the fluctuating wall velocity, involving a non-markovian memory kernel with vanishing time integral. The calculation highlights some subtleties of the wall-liquid dynamics, leading to superdiffusive motion of the fluctuating wall position.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
