Investigation on the condensation process of HFO refrigerants by molecular dynamics simulation

Molecular dynamics simulation of water condensation with nucleus under electromagnetic wave irradiation

The condensation process of water with different nuclei under electromagnetic wave irradiation was studied by molecular dynamics simulation. It was found that there is a different electric-field effect when the condensation nucleus was a small (NH₄)₂SO₄ cluster than a CaCO3 nucleus. Through the analysis of the hydrogen-bond number, energy change, and dynamic behavior, we found that the effect of external electric field on the condensation process mainly comes from the change of potential energy caused by the dielectric response and there is a competition effect between the dielectric response and the dissolution in the system with (NH₄)₂SO₄.

Molecular dynamics simulation study used in systems with supercritical water

Supercritical water (SCW) is a green solvent. The supercritical fluids have been increasingly concerned and studied in many areas such as SCW gasification, biofuel production, SCW hydrothermal conversion, organic wastes treatment and utilization, nanotechnology, etc. Because of the severe circumstances and rapid reactions in supercritical water, it is difficult for experimental researchers to disentangle various fundamental reaction steps from the intermediate and product distributions. From this perspective, molecular dynamics (MD) simulation based on quantum chemistry is an efficient tool for studying and exploring complex molecular systems. In recent years, molecular simulations and quantum chemical calculations have become powerful for illustrating the possible internal mechanism of a complex system. However, now there is no literature about the overview of MD simulation study of the system with SCW. Therefore, in this paper, an overview of MD simulation investigation applied in various systems with SCW is presented. In the current review we explore diverse research areas. Namely, the applications of MD simulation on investigating the properties of SCW, pyrolysis/gasification systems with SCW, dissolution systems and oxidation systems with SCW were summarized. And the corresponding problems in diverse systems were discussed. Furthermore, the advances and problems in MD simulation study were also discussed. Finally, possible directions for future research were outlined. This work is expected to be one reference for the further theoretical and molecular simulation investigations of systems involving SCW.

Investigation of dielectric constants of water in a nano-confined pore

We report the dielectric properties of pure water confined in a silica hydrophilic nanopore determined from the computation of the density of liquid in the confined phase by the grand canonical Monte Carlo (GCMC) simulations. The silica cylindrical nanopore is divided into n concentric radial shells to get a better insight into the dielectric properties of nanoconfined water. We find that the average values of the dielectric constants are very close and almost independent of the number of concentric radial shells. The decrease in the dielectric constant of confined pure water is due to the strong orientation of water dipoles in the vicinity of the surface while water dipoles do not exhibit any preferential orientation in bulk phase.

Investigation of dielectric properties in a silica hydrophilic pore with two water reservoirs.

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels

Oxygenated fuel is a promising alternative fuel for engines because of the advantage of low emission. In this work, a general model based on back-propagation neural networks was developed for estimating the viscosities of different kinds of oxygenated fuels including esters, alcohols, and ethers, whose input variables are pressure, temperature, critical pressure, critical temperature, molar mass, and acentric factor. The viscosity data of 31 oxygenated fuels (1574 points) at temperatures ranging from 243.15 to 413.15 K and at pressures ranging from 0.1 to 200 MPa were collected to train and test the back-propagation neural network model. The comparison result shows that the predictions of the proposed back-propagation neural network model agree well with the experimental viscosity data of all studied oxygenated fuels using the general parameters (weight and bias). The average absolute relative deviations for training data, validation data, and testing data are 1.19%, 1.27%, and 1.30%, respectively.