No abstract

Molecular dynamics (MD) simulations are conducted to determine energy and momentum accommodation coefficients at the interface between rarefied gas and solid walls. The MD simulation setup consists of two parallel walls, and of inert gas confined between them. Different mixing rules, as well as existing ab-initio computations combined with interatomic Lennard-Jones potentials were employed in MD simulations to investigate the corresponding effects of gas-surface interaction strength on accommodation coefficients for Argon and Helium gases on a gold surface. Comparing the obtained MD results for accommodation coefficients with empirical and numerical values in the literature revealed that the interaction potential based on ab-initio calculations is the most reliable one for computing accommodation coefficients. Finally, it is shown that gas-gas interactions in the two parallel walls approach led to an enhancement in computed accommodation coefficients compared to the molecular beam approach. The values for the two parallel walls approach are also closer to the experimental values.

Because of their high thermal conductivity, graphene nanoribbons (GNRs) can be employed as fillers to enhance the thermal transfer properties of composite materials, such as polymer-based ones. However, when the filler loading is higher than the geometric percolation threshold, the interfacial thermal resistance between adjacent GNRs may significantly limit the overall thermal transfer through a network of fillers. In this article, reverse non-equilibrium molecular dynamics is used to investigate the impact of the relative orientation (i.e., horizontal and vertical overlap, interplanar spacing and angular displacement) of couples of GNRs on their interfacial thermal resistance. Based on the simulation results, we propose an empirical correlation between the thermal resistance at the interface of adjacent GNRs and their main geometrical parameters, namely the normalized projected overlap and average interplanar spacing. The reported correlation can be beneficial for speeding up bottom-up approaches to the multiscale analysis of the thermal properties of composite materials, particularly when thermally conductive fillers create percolating pathways.

Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique. Physical Review E, 104(1-2), [015309].