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Nanoscale evaporation of liquids plays a key role in several applications including cooling, drag reduction and liquid transport. This research investigates the Leidenfrost effect at the nanoscale as a function of substrate material, droplet size and temperature using molecular dynamics models. Water droplets ranging from 4 nm to 20 nm were simulated over gold and silicon substrates at 293 K, 373 K, 473 K, and 573 K. A significant increase in the kinetic energy (>5000 kcal mol −1 ) was observed for molecules in the vicinity of the substrates, indicating the presence of a vapor barrier layer between substrate and liquid. Higher droplet velocities were tracked for hydrophobic gold substrates as compared to hydrophilic silicon substrates indicating the influence of the surface wettability on the Leidenfrost effect. Droplets over silicon substrates had a higher number of fluctuations (peaks and valleys) as compared to gold due to the cyclic behavior of vapor formation. An increase in the interfacial kinetic energies and translatory velocities (>10 m s −1 ) were observed as the droplet sizes reduced confirming the Leidenfrost effect at 373 K. This research provides understanding of the Leidenfrost effect at the nanoscale which can impact several applications in heat transfer and droplet propulsion. 

Molecular dynamics (MD) models require comprehensive computational power to simulate nanoscale phenomena. Traditionally, central processing unit (CPU) clusters have been the standard method of performing these numerically intensive computations. This article investigates the use of graphical processing units (GPUs) to implement large-scale MD models for exploring nanofluidic-substrate interactions. MD models of water nanodroplets over flat silicon substrate are tracked wherein the simulation attains a steady state computational performance. Different classes of GPU units from NVIDIA (C2050, K20, and K40) are evaluated for energy efficiency performance with respect to three green computing measures: simulation completion time, power consumption, and CO2 emissions. The CPU+K40 configuration displayed the lowest energy consumption profile for all the measures. This research demonstrates the use of energy efficient graphical computing versus traditional CPU computing for high-performance molecular dynamics simulations.