Browsing by Author "Zhang, Yuelei"

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Citation Count: 1
A numerical study of the effect of variable heat flux on the stability and thermal behavior of SARS-COV-2 structure: A molecular dynamics approach
(Elsevier, 2024) Xiao, Li; Salahshour, Soheıl; Zhang, Yuelei; Jasim, Dheyaa J.; Salahshour, Soheil; Li, Z.; Toghraie, Davood
One of the common methods is the molecular dynamics simulation which models the behavior of atoms and molecules. This paper used the molecular dynamics technique to simulate the behavior of SARS-COV-2 virus under variable heat flux conditions. By doing so, it can be observed how the virus structure responded to the changes in external heat flux and how this affected its stability. This paper studied the effect of external heat flux with different amplitudes of 0.1, 0.2, 0.3, and 0.5 W/m 2 on the stability of SARS in an aqueous medium. The present study showed that the implementation of external heat flux to modeled samples significantly affected their physical stability. Numerically, the mean square displacement of system decreased to 0.634 nm 2 by increasing the heat flux inside the computational box. This atomic evolution predicted the stability of target structure increased by heat flux implementation to samples. Physically, this behavior arose from increasing attraction force among various particles inside the SARS-COV-2 structure in the presence of external heat flux. So, we expect this atomic evolution in treatment method design in clinical cases.
Citation Count: 3
Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50
(Elsevier Sci Ltd, 2024) Zhang, Yuelei; Salahshour, Soheıl; Sajadi, S. Mohammad; Li, Z.; Jasim, Dheyaa J.; Nasajpour-Esfahani, Navid; Khabaz, Mohamad Khaje
This study aims to investigate the viscosity behavior of multi-walled carbon nanotube (MWCNT) - titanium dioxide (TiO2) (40-60) - SAE50 oil nanofluid using an Artificial Neural Network (ANN) modeling approach. The main objective is to develop a highly accurate predictive model for viscosity by considering three input parameters: temperature, solid volume fraction (SVF), and shear rate (SR). Rheological measurements provide experimental data used to train and validate the ANN model. The ANN model's architecture, activation functions, and training algorithms are carefully chosen. Data are divided to three subsets including train, validation and test. ANN is trained using trainlm algorithm for 50 times to vanish the effect of random nature of ANN weight initialization. The trained ANN model is then utilized to predict the viscosity of the nanofluid under varying conditions. The results demonstrate the efficacy of the proposed ANN model in capturing the complex relationship between viscosity and the input parameters, providing accurate viscosity predictions for the MWCNT-TiO2-oil SAE50 nanofluid. Furthermore, the influence of temperature, SVF, and SR on viscosity is analyzed, offering valuable insights into the flow behavior of the nanofluid. According to the obtained results, the developed ANN model presents a reliable and efficient approach to estimate the viscosity of the MWCNTTiO2-SAE50 oil nanofluid, eliminating the need for costly and extensive experimental measurements within the analyzed range. ANN could model the nanofluid viscosity with R2 = 0.9998 and MSE= 0.000189 that is quite acceptable. Also, the experimental data revealed that for the investigated nanofluid, temperature and shear rate have impressive effect on the viscosity (changing viscosity more than 100% for the analyzed margin), on the other hand, the nanoparticle volume fraction effect is much lower, to be more precise, increasing the nanoparticle percentage will increase the viscosity mean value around 30%.