Fluid atomic behavior is an important factor for industrial applications. Computer simulations based on simple models predict Poiseuille flow for these atomic structures with the presence of external force. In this work, we describe the dynamical properties of Ar and O2 flows with precise atomic arrangement via dissipative particle dynamics (DPD) and molecular dynamics (MD) simulation approaches. In these methods, each model is represented by using Large-scale Atomic/Molecular Massively Parallel Simulator package. Simulation results show that maximum rate for velocity of Ar flow in platinum and copper microchannels is 0.100 (unit less)/0.091 Å ps?1 and 0.121 (unit less)/0.105 Å ps?1 by using DPD/MD approach. This atomic parameter changes to 0.111 (unit less)/0.102 Å ps?1 and 0.125 (unit less)/0.108 Å ps?1 for O2 fluid with mentioned approaches. By decreasing the microchannel size, the maximum rate of velocity reaches to 0.101 (unit less)/0.099 Å ps?1 and maximum temperature rate decreases to 485 (unit less)/440 K with DPD/MD approaches. These calculated parameters can be used in industrial application designing for some processes such as heat transfer in structures. It was seen that the developed DPD approach was able to simulate the fluid flow and heat transfer of various types of fluids at micro- and nanoscales with suitable accuracy versus MD.
相似文献In this paper, at the first, new correlations were proposed to predict the rheological behavior of MWCNTs–SiO2/EG–water non-Newtonian hybrid nanofluid using different sets of experimental data for the viscosity, consistency and power law indices. Then, based on minimum prediction errors, two optimal artificial neural network models (ANNs) were considered to forecast the rheological behavior of the non-Newtonian hybrid nanofluid. One hundred and ninety-eight experimental data were employed for predicting viscosity (Model I). Two sets of forty-two experimental data also were considered to predict the consistency and power law indices (Model II). The data sets were divided to training and test sets which contained respectively 80 and 20% of data points. Comparisons between the correlations and ANN models showed that ANN models were much more accurate than proposed correlations. Moreover, it was found that the neural network is a powerful instrument in establishing the relationship between a large numbers of experimental data. Thus, this paper confirmed that the neural network is a reliable method for predicting the rheological behavior of non-Newtonian nanofluids in different models.
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