arXiv subjects

Rasoul Kheiri

Publications and source records attributed to Rasoul Kheiri.

4 recordsLinked to original sources

arXiv2022

Quantum "contact" friction: the contribution of kinetic friction coefficient from thermal fluctuations

A thermal model of kinetic friction is assigned to a classical loaded particle moving on a fluctuating smooth surface. A sinusoidal wave resembles surface fluctuations with a relaxation time. The Hamiltonian is approximated to the mean energy of the wave describing a system of Harmonic oscillators. The quantization of amplitudes yields in terms of annihilation and creation operators multiplied by a quantum phase. Further, we consider acoustic dispersion relation and evaluate the friction coefficient from the force autocorrelation function. While the sliding particle remains classical describing a nano-particle or a tip with negligible quantum effects like tunneling or delocalization in the wave function, the quantized model of the surface fluctuations results in the temperature dependence of the kinetic friction coefficient. It follows an asymptotic value for higher temperatures and supperslipperiness at low temperatures.

cond-mat.mes-hall↗

arXiv2022

Thermal Activation of Dry Sliding Friction at The Nano-scale

Molecular dynamic (MD) simulations are applied to investigate the dependency of the kinetic friction coefficient on the temperature at the nano-scale. The system is comprised of an aluminum spherical particle consisting of 32000 atoms in an FCC lattice sliding on a stack of several layers of graphene, and the simulations have done using LAMMPS. The interaction potential is charge-optimized many-body (COMB3) potential and a Langevin thermostat keep the system at a nearly constant temperature. With an assumption of linear viscous friction, $F_{fr}= - γv$, the kinetic friction coefficient $γ$ is derived and plotted at different temperatures in the interval of $T \in [1, 600] K$. As a result, by increasing temperature, the kinetic friction coefficient is decreased. Consequently, while the friction is assumed as a linear viscous model, the results are similar to the thermal activation in atomic-scale friction. That is, (1) by increasing sliding velocity friction force will be increased and (2) by increasing temperature, kinetic friction coefficient decreases.

physics.data-an↗

arXiv2022

The velocity dependence of dry sliding friction at the nano-scale

We performed molecular dynamics (MD) experiments to explore dry sliding friction at the nanoscale. We used the setup comprised of a spherical particle built up of 32,000 aluminium atoms, resting on a semi-space with a free surface, modelled by a stack of merged graphene layers. We utilized LAMMPS with the COMB3 many-body potentials for the inter-atomic interactions and Langevin thermostat which kept the system at $300 K$. We varied the normal load on the particle and applied different tangential force, which caused the particle sliding. Based on the simulation data, we demonstrate that the friction force $F_{\rm fr}$ linearly depends on the sliding velocity $v$, that is, $F_{\rm fr}=-γv$, where $γ$ is the friction coefficient. The observed dependence is in a sharp contrast with the macroscopic Amontons-Coulomb laws, which predict the velocity independence of sliding friction. We explain such a dependence by surface fluctuations of the thermal origin, which give rise to surface corrugation hindering sliding motion. This mechanism is similar to that of the viscous friction force exerted on a body moving in viscous fluid.

physics.data-an↗

arXiv2019

A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems

We introduce a kind of partial observability to the projective simulation (PS) learning method. It is done by adding a belief projection operator and an observability parameter to the original framework of the efficiency of the PS model. I provide theoretical formulations, network representations, and situated scenarios derived from the invasion toy problem as a starting point for some multi-agent PS models.

cs.AI↗