Abstract
Thermal transport in a water–Cu nanocolloid system was investigated using equilibrium molecular dynamics. A systematic analysis of the Green–Kubo calculations is presented to clarify the effect of simulation parameters. Several sources of error were identified and quantified for the thermal conductivity estimations, and the effect of the base fluid potential was investigated. Simulations were carried out with a single copper particle for different diameters and water potentials, and thermal enhancements exceeding both theoretical and experimental results were observed in parallel with some other studies in the literature. The anomalous Green–Kubo thermal enhancement results could be explained by the interfacial dynamics and the neglect of calibrating the interaction potential to satisfy the physically-observed energy flow at the interface.
Highlights
Colloidal suspensions have recently been the focus of numerous scientific studies due to their potential engineering applications [1]
A heat flux is imposed on the system in Non-equilibrium Molecular Dynamics (NEMD) and the thermal conductivity is calculated based on the resulting temperature gradients
The confidence interval usually reported in the literature is for a single autocorrelation function (ACF), and corresponds to the fluctuations in the thermal conductivity within a single Molecular Dynamics (MD) simulation
Summary
Colloidal suspensions have recently been the focus of numerous scientific studies due to their potential engineering applications [1]. The physics of thermal transport in these systems requires further study, and simulations are frequently used for this purpose because of experimental limitations at the relevant length scale. A heat flux is imposed on the system in NEMD and the thermal conductivity is calculated based on the resulting temperature gradients. Equilibrium Molecular Dynamics instead calculates thermal conductivity from the time decay of heat flux fluctuations based on the fluctuation-dissipation theorem. While this requires more computational power, the method does not suffer from the drawbacks of NEMD and is widely used in the literature
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