Browsing by Author "Eftekhari, S. Ali"

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Citation Count: 0
Determining the best structure for an artificial neural network to model the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant
(Elsevier, 2024) Esfe, Mohammad Hemmat; Salahshour, Soheıl; Sajadi, S. Mohammad; Hashemian, Mohammad; Salahshour, Soheil; Motallebi, Seyed Majid
In this paper, an artificial neural network (ANN) was utilized to examine the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant. The effect of temperature, shear rate (SR) and solid volume fraction (SVF) on dynamic viscosity is studied at a temperature ranging from T = 5-55 degrees C, SR varying SR= 50-900 rpm, and SVF= 0.05-1%. A set of 172 experimental data is determined and applied as a training dataset of ANNs with various structures. A two-layer ANN with 17 neurons in the hidden layer is selected with R2 = 0.9999 and MSE= 7.77e-5 to predict the dynamic viscosity. Results show that SR is the most influential parameter having an inverse effect on the dynamic viscosity, i.e. by increasing this parameter from 50 to 900 rpm, the viscosity reduces from 600 cP to 40 cP.
Citation Count: 0
The molecular dynamics simulation of coronavirus- based compound (6OHW structure) interaction with interferon beta-1a protein at different temperatures and pressures: Virus destruction process
(Pergamon-elsevier Science Ltd, 2024) Sun, Di; Salahshour, Soheıl; Aljaafari, Haydar A. S.; Cardenas, Maritza Lucia Vaca; Kazem, Tareq Jwad; Mohammed, Abrar A.; Eftekhari, S. Ali
The Interferon beta-1a protein is a cytokine in the Interferon family that is used to treat a variety of ailments. Molecular Dynamics simulation was used to characterize the atomic disintegration of 6OHW structure of a corona virus-based compound with Interferon beta-1a protein in this computational study. Molecular Dynamics simulation results on the atomic evolution of the 6OHW structure were presented with estimating physical variables. Physically, our simulations showed the attraction forces between the virus and the atomic protein in the presence of H2O molecules, resulting in viral annihilation after t = 10 ns. The molecular dynamics package's initial pressure and temperature (Temp) changes were important for virus-protein system evolution. Numerically, increasing primary T and P from 300 K and 1 bar to 350 K and 5 bar reduced the atomic distance between virus and protein structures from 10 & Aring; to 2.71 & Aring; and 2.45 & Aring;. Bonding energy was another reported physical quantity in our Molecular Dynamics simulation work. The atomic parameter ranged from 152.57 kcal/mol to 148.54 kcal/mol due to changes in initial Temp and pressure. Ultimately, the diffusion coefficient of protein being simulated inside the atomic virus changed from 0.48 mu m2/s to 0.59 mu m2/s. This calculation demonstrated the suitable conduct of simulated protein throughout virus destruction process.
Citation Count: 0
A new model for viscosity prediction for silica-alumina-MWCNT/Water hybrid nanofluid using nonlinear curve fitting
(Elsevier - Division Reed Elsevier india Pvt Ltd, 2024) Qu, Meihong; Salahshour, Soheıl; Alizadeh, As'ad; Eftekhari, S. Ali; Nasajpour-Esfahani, Navid; Zekri, Hussein; Toghraie, Davood
One of the most crucial concerns is improving industrial equipment's ability to transmit heat at a faster rate, hence minimizing energy loss. Viscosity is one of the key elements determining heat transmission in fluids. Therefore, it is crucial to research the viscosity of nanofluids (NF). In this study, the effect of temperature (T) and the volume fraction of nanoparticles (phi) on the viscosity of the silica-alumina-MWCNT/Water hybrid nanofluid (HNF) is examined. In this study, a nonlinear curve fitting is accurately fitted using MATLAB software and is used to identify the main effect, extracting the residuals and viscosity deviation of these two input variables, i.e., temperature (T = 20 to 60 C-degrees) and volume fraction of nanoparticles (phi = 0.1 to 0.5 %). The findings demonstrate that the viscosity of silica-alumina-MWCNT/ Water hybrid nanofluid increases as the phi increases. In terms of numbers, the mu nf rises from 1.55 to 3.26 cP when the phi grows from 0.1 to 0.5 % (at T = 40 C-degrees). On the other hand, the mu nf decreases as the temperature was increases. The mu(nf) of silica-alumina-MWCNT/ Water hybrid nanofluid reduces from 3.3 to 1.73 cP when the temperature rises from 20 to 60 C-degrees (at phi = 0.3 %). The findings demonstrate that the mu nf exhibits greater variance for lower temperatures and higher phi.
Citation Count: 0
Numerical investigation of the heat flux frequency effect on the doxorubicin absorption by Bio MOF11 carrier: A molecular dynamics approach
(Elsevier, 2024) Ben Said, Lotfi; Salahshour, Soheıl; Jasim, Dheyaa J.; Aljaafari, Haydar A. S.; Ayadi, Badreddine; Aich, Walid; Eftekhari, S. Ali
The present study investigated the effect of heat flux frequency on doxorubicin adsorption by bio MOF11 biocarrier using molecular dynamics simulation. This simulation examined the effect of several heat flux frequencies (0.001, 0.002, 0.005, and 0.010 1/fs) on the quantity of drug particles absorbed, mean square displacement (MSD), diffusion coefficient, and interaction energy. The present outputs of simulations predicted the structural stability of the modeled MOF-drug system in 300 K. Also, simulation outputs predicted by frequency optimization, the adsorption of target drug inside MOF11 maximized, and efficiency of this sample in actual clinical applications, such as drug delivery process increased. Numerically, the optimum value of frequency was estimated to be 0.005 1/fs. Using this heat setting, the interaction energy between MOF 11 and the doxorubicin drug increased to -929.05 kcal/mol, and the number of penetrated drug particles inside MOF11 converged to 207 atoms. The results reveal that the MSD parameter reached 64.82 angstrom 2 after 100000 -time steps. By increasing frequency to 0.005 fs-1, this increased to 78.05 angstrom 2. By increasing MSD parameter, the drug diffusion process effectively occurred, and the diffusion coefficient increased from 67.29 to 82.47 nm2/ns. It is expected that the findings of present investigation guide the design of more efficient drug delivery platforms, enhance drugcarrier interactions, improve manufacturing processes, and aid in developing novel nanomaterials with enhanced adsorption properties for various applications.
Citation Count: 0
An RBF-based artificial neural network for prediction of dynamic viscosity of MgO/SAE 5W30 oil hybrid nano-lubricant to obtain the best performance of energy systems
(Elsevier, 2024) Gao, Jie; Salahshour, Soheıl; Sajadi, S. Mohammad; Eftekhari, S. Ali; Hekmatifar, Maboud; Salahshour, Soheil; Toghraie, Davood
Technological progress and complications in microfluidics usage have led researchers to use nanomaterials in different scientific fields. The properties and characteristics of hybrid Nanofluids are more enhanced compared to nanofluids based on single nanoparticles and conventional liquid. Recently, modeling methods have replaced most common statistical methods. Due to the high accuracy of the response and generalizability in various conditions, artificial neural networks (ANNs) to estimate nanofluids' viscosity and thermal conductivity have become common. Dynamic viscosity (mu) (estimation analyzes one of the key factors in determining the hydro-dynamic behavior of nanofluids. In this manuscript, an RBF-ANN is used to simulate the input-output relation of dynamic viscosity of the MgO-SAE 5W30 Oil hybrid nanofluid versus three important parameters, including volume fraction of nanoparticles, temperature, and shear rate. The results show that for this nanofluid, by increasing temperature and shear rate, the dynamic viscosity is decreased. In contrast, the volume fraction of nanoparticles directly affects the output, although this consequence can be neglected. By increasing the tem-perature from 5 degrees to 55 degrees C, the dynamic viscosity would decrease. Also, changing the shear rate from 50 to 1000 rpm decreases the dynamic viscosity from 400 cP to 25 cP. It is worth mentioning that the obtained trends and deviation of dynamic viscosity for MgO-SAE 5W30 Oil hybrid nanofluid versus temperature, the volume fraction of nanoparticles, and shear rate can be used by the academic community as well as an industrial section to obtain the best performance of energy systems based on this nanofluid.
Citation Count: 1
Using adaptive neuro-fuzzy inference system for predicting thermal conductivity of silica -MWCNT-alumina/water hybrid nanofluid
(Elsevier, 2023) Zhou, Yuan; Salahshour, Soheıl; Sajadi, S. Mohammad; Jasim, Dheyaa J.; Nasajpour-Esfahani, Navid; Salahshour, Soheil; Eftekhari, S. Ali
In this study, the thermal conductivity (knf) of Silicon Oxide-MWCNT-Alumina/Water hybrid nanofluid (HNF) is predicted versus solid volume fraction (SVF) and temperature. For this reason, various combinations of SVF and temperature are considered from SVF= 0.1-0.5% and 20-60 (degrees C) respectively. Then, an adaptive neuro-fuzzy inference system (ANFIS) has been effectively used to model the knf of HNF as one of the effective machine learning techniques. Various shapes of membership functions are considered and the generalized bell shape membership function showed to have acceptable accuracy for knf prediction using an ANFIS-based model. Moreover, the outcomes reveal that the effect of SVF is higher than temperature influence on the knf of HNF. Specifically, when the SVF is increased from 0.1% to 0.5%, there is an approximate 25% increase in knf. Conversely, an increase in temperature leads to a smaller ratio of knf increment. When the temperature rises from 20 degrees to 60 degrees C, knf only increases by less than 10%. The highest error value is found at phi = 0.2% and T = 60 degrees C, amounting to 0.01128 W/mK.
Citation Count: 0
Using different Heuristic strategies and an adaptive Neuro-Fuzzy inference system for multi-objective optimization of Hybrid Nanofluid to provide an efficient thermal behavior
(Elsevier, 2024) Wang, Zhe; Salahshour, Soheıl; Kazim, Khudhaier J.; Basem, Ali; Al-fanhrawi, Halah Jawad; Dacto, Karina Elizabeth Cajamarca; Eftekhari, S. Ali
The importance of multi-objective optimization in hybrid nanofluid research lies in its wide-ranging applications across fields such as microelectronics, aerospace, and renewable energy. These specialized fluids hold the potential to elevate the performance and efficiency of diverse systems through enhanced heat transfer capabilities. This research endeavor is centered around optimizing a hybrid nanofluid composed of Silicon Oxide-MWCNTAlumina/Water by leveraging a mix of heuristic approaches and an adaptive neuro-fuzzy inference system. To this end, the most influential set of input parameters has been identified using four state-of-the-art algorithms: Non-dominated Genetic Algorithm, multi-objective particle swarm optimization, Strength Pareto Evolutionary Algorithm 2, and Pareto Envelope-based Selection Algorithm 2. The goal of the optimization process is to modify the temperature (T = 20 degrees C to 60 degrees C) and the volume fraction of nanoparticles (SVF=0.1 % to 0.5 %). Finding the optimal combination of these parameters that results in the hybrid nanofluid with the maximum thermal conductivity (knf) and the lowest dynamic viscosity is the main objective. The findings of this research have the potential to drastically improve the performance of systems in a variety of applications and to change the creation of sophisticated, high-efficiency heat transfer fluids.