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Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since investigations on the mechanical behavior of materials at micro/nanoscale have been becoming much more widespread, it is necessary to determine an adequate potential which accurately models the interaction of the atoms for desired applications. In this framework, reliability of multiple embedded atom method based interatomic potentials for predicting the elastic properties was investigated. Assessments were carried out for different copper, aluminum, and nickel interatomic potentials at room temperature which is considered as the most applicable case. Examined force fields for the three species were taken from online repositories of National Institute of Standards and Technology, as well as the Sandia National Laboratories, the LAMMPS database. Using molecular dynamic simulations, the three independent elastic constants, C11, C12, and C44, were found for Cu, Al, and Ni cubic single crystals. Voigt-Reuss-Hill approximation was then implemented to convert elastic constants of the single crystals into isotropic polycrystalline elastic moduli including bulk modulus, shear modulus, and Young's modulus as well as Poisson's ratio. Simulation results from massive molecular dynamic were compared with available experimental data in the literature to justify the robustness of each potential for each species. Eventually, accurate interatomic potentials have been recommended for finding each of the elastic properties of the pure species. Exactitude of the elastic properties was found to be sensitive to the choice of the force fields. Those potentials that were fitted for a specific compound may not necessarily work accurately for all the existing pure species. Tabulated results in this paper might be used as a benchmark to increase assurance of using the interatomic potential that was designated for a problem. [ABSTRACT FROM AUTHOR]

This article aims to study suitability and merits of employing a biofuel in methane-air jet flames for energy security and environmental sustainability purposes. A special biofuel (methyl decanoate, methyl 9-decenoate, and n-heptane) oxidation with 118 species reduced/skeletal mechanism and 837 reactions is combusted instead of methane. The biofuel was fed in the main jet inlet of the well-known Sandia flame D (SFD) while the hot pilot jet is still responsible for ignition. The open-source software OpenFOAM was used for simulating turbulent biofuel-air combustion. To check the accuracy of computational results, the system was initially validated with SFD experimental results and good agreements were found. After ignition, mean temperature distribution and species mean mass fraction at different distances in axial and radial directions were investigated. Results showed that the biofuel can be effectively used as an alternative to SFD not only for generating a reasonable temperature as methane does, but also for significantly reduction in principal greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Effect of embedding aluminum porous metal foam inside the flow channels of an air-cooled Li-ion battery module was studied to improve its thermal management. Four different cases of metal foam insert were examined using three-dimensional transient numerical simulations. The effects of permeability and porosity of the porous medium as well as state of charge were investigated on the standard deviation of the temperature field and maximum temperature inside the battery in all four cases. Compared to the case of no porous insert, embedding aluminum metal foam in the air flow channel significantly improved the thermal management of Li-ion battery cell. The results also indicated that, decreasing the porosity of the porous structure decreases both standard deviation of the temperature field and maximum temperature inside the battery. Moreover, increasing the permeability of the metal foam drops the maximum temperature inside the battery while decreasing this property leads to improving the temperature uniformity. Our results suggested that, among the all studied cases, desirable temperature uniformity and maximum temperature were achieved when two-third and the entire air flow channel is filled with aluminum metal foam, respectively. [ABSTRACT FROM AUTHOR]

• Prediction of surface tension (ST) value of pendant drops by machine learning (ML). • Convolutional neural network trained using pendant drop images of known ST. • Estimating ST for unknown pendant drops outside the range of training data. • ST prediction of any arbitrary pendant drop shape during transient drop generation. • ML model capable of ST prediction independent of fluid properties. An image-based machine learning (ML) method is introduced to predict the surface tension of an ethanol-water pendant drop made of unknown ethanol-water composition. In contrast to previous neural-network based surface tension predictors that rely on using either the simplified molecular-input line-entry system or experimentally measured surface tension values, the present image-based deep neural network model directly predicts surface tensions based on arbitrary pendant drop shapes at any stage before breakup. Using convolutional neural networks (CNNs), surface tension values are accurately obtained independent of the drop size and liquid properties. Two CNN architectures are presented that accurately predict the surface tension of a pendant drop for three different ML models. To improve the generality of the ML models, image data augmentation technique is used to generate more representatives from available data. Approximating the surface tension for unknown pendant drops outside the range of the given classes has also been demonstrated. The trained machine learning models have an overall accuracy of about 98% in predicting the surface tension of a pendant drop containing an unknown ethanol-water composition. Additionally, the ML models are tested on unknown methanol-water mixtures to demonstrate the generality and the results show good predicting accuracy. Besides the accuracy of CNN models, performance measures including Precision, Recall and F1-score are reported for each surface tension value in the dataset. Compared with the physics-based axisymmetric drop shape analysis techniques, the present method is not limited to equilibrium pendant drop images and is much faster and more versatile. This image-based ML method shows great promise in directly predicting the surface tension values based on vastly available pendant drop images. [ABSTRACT FROM AUTHOR]

• The Zeolite nanosheet removed hazardous CuCl 2 and HgCl 2 from contaminated water. • Cu2+ and Hg2+ ions were 100% rejected at any applied pressure to the system. • The nanostructured membrane showed a large water flux during purification. • Fresh water flux increased by induced pressure from 10 to 200 MPa. Water purification and detoxification is an important topic that protects the environment and ecosystems. The feasibility of using Zeolite MFI as a nanostructured membrane to remove hazardous chemicals from a contaminated water solution is studied in this paper. A non-equilibrium molecular dynamics analysis was performed to see the potentials of the zeolite porous nanosheet to separate the mercury chloride (HgCl 2) and copper chloride (CuCl 2) from water as two major hazardous contaminants. A reverse osmosis system was simulated and tested at different induced pressures from 10 to 200 MPa. Ion removal, water flux, water molecules accumulation at different locations, number of hydrogen bonds, Van der Waals interactions, ions tracking path and radial distribution function between water molecules and the ions, were investigated in detail. The results indicated that the zeolite MFI nanomembrane can effectively prevent mercury, copper and chlorine ions from permeation while keeping a large water flux through the membrane. This behavior of the zeolite introduces a competitive candidate for the water purification industry and sets it apart from other nanostructured membranes. [ABSTRACT FROM AUTHOR]