Optimal viscosity modelling of 10W40 oil-based MWCNT (40%)-TiO2 (60%) nanofluid using Response Surface Methodology (RSM).

The science of nanofluids is still fairly new and due to this, the properties of many nanofluids are yet to be explored. Therefore, equations for precise calculations in this field are not available yet. For this reason, as a thermophysical property of an MWCNT (40%)/TiO2 (60%) hybrid nanofluid (HNF), in this research, the viscosity of HNF with 10W40 oil as the base fluid, in a temperature range of T = 5-55 °C and with solid volume fractions of SVF = 0.5-1% is studied and modelled. The viscosity of the nanofluid was examined in different conditions. Lab data were used to model dynamic viscosity of HNF using the Response Surface Methodology (RSM), and first, second, third, fourth and fifth-order models were created. An analysis of the statistical parameters concluded that with a correlation coefficient of 0.9999, the fifth-order model is the best performer. The trend of alterations in viscosity shows that an increase in temperature has great effects on viscosity, and its influence is also more important than that of changes in shear rate (SR) and SVF. Optimal viscosity was also calculated and was equal to 158.1 mPa.sec at SVF = 0.05 %, SR = 11,997 s- 1 and T = 14.97 °C.

Investigating the rheological behavior of a hybrid nanofluid (HNF) to present to the industry.

Hybrid nanofluids (HNFs) are potential fluids that have higher thermophysical properties than conventional nanofluids of heat transfer and viscosity. HNF is a new generation of nanofluid that is produced by dispersing two or more types of dissimilar nanoparticles (NPs) in the base fluid. In this study, the rheological behavior of MWCNT (25%)-MgO (75%)/SAE40 HNF was investigated experimentally, statistically and numerically. Temperature conditions are in the range of T = 50-25 °C, solid volume fractions (SVFs) are in the range of SVF = 0.0625-1% and shear rate (SR) is in the range of SR = 666.5-7998 s-1. This study aims to identify the rheological behavior of HNF based on the effective factors of temperature, SR, and SVF. Various methods show that HNFs exhibit non-Newtonian behavior. The numerical values of the power-law index (n) at T = 50 °C and SVF = 0.75% show the strongest non-Newtonian behavior of HNF and n = 0.9233 is reported. Using laboratory findings, the maximum and minimum viscosities of the base oil increase and decrease by 24% and -8.50%, respectively. Using the response surface methodology (RSM), the relationship between experimental data and modeled data is determined. A quadratic three-variable model with R2 = 0.9994 is used to predict the data.

Experimental Study of Rheological Behavior of MWCNT-Al2O3/SAE50 Hybrid Nanofluid to Provide the Best Nano-lubrication Conditions.

In this study, MWCNT-Al2O3 hybrid nanoparticles with a composition ratio of 50:50 in SAE50 base oil are used. This paper aims to describe the rheological behavior of hybrid nanofluid based on temperature, shear rate ([Formula: see text] and volume fraction of nanoparticles ([Formula: see text]) to present an experimental correlation model. Flowmetric methods confirm the non-Newtonian behavior of the hybrid nanofluid. The highest increase and decrease in viscosity ([Formula: see text]) in the studied conditions are measured as 24% and - 17%, respectively. To predict the experimental data, the five-point-three-variable model is used in the response surface methodology with a coefficient of determination of 0.9979. Margin deviation (MOD) of the data is determined to be within the permissible limit of - 4.66% < MOD < 5.25%. Sensitivity analysis shows that with a 10% increase in [Formula: see text] at [Formula: see text] 1%, the highest increase in [Formula: see text] of 34.92% is obtained.

An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid.

This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity ([Formula: see text]) is evaluated at volume fractions ([Formula: see text]=0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg-Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the [Formula: see text] of Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, [Formula: see text], temperature, and shear rate are considered as input variables, and [Formula: see text] is considered as output variable. From 400 different ANN structures for Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E-08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.

Application of conventional and hybrid nanofluids in different machining processes: A critical review.

This paper reviews the application of conventional and hybrid nano cutting fluids with different additives in various machining processes, namely turning, milling, drilling, and grinding ones. The literature states that using nanofluids, as cutting fluids, improves the lubrication and cooling in comparison with conventional cutting liquids, while the level of improvement depends on some parameters. In turning process, for each nanofluid, there is a specific pressure, flow rate, and nanoparticle volume fraction to reach optimum performance. Nanoparticle concentration in the range of 0.25%-0.5% (low and economical concentrations) is the most repetitive for optimal case in most of machining processes. Also, hybrid nanofluids show more positive effects compared with conventional nanofluids and base fluids. According to the reports, important parameters such as cutting temperature, cutting force, tool wear, and surface roughness experience 10%-40% and in some cases 50%-70% positive change after applying nanoparticles in turning processes. On the other hand, for the milling process, the SiO2, MoS2 and graphene nanoparticles are reported as most applied and effective ones in the literature. For the drilling process, the Cu and diamond nanoparticles are the most applied nanoparticles with positive effect. Moreover, the most utilized nanoparticles for grinding process are MoS2, Al2O3 and diamond families. The corresponding challenges in this field are also examined and directions for future research are recommended.

Mandatory and Self-citation; Types, Reasons, Their Benefits and Disadvantages.

This paper defines and discusses two important types of citations, self-citation and mandatory citation, in engineering journals. Citation can be classified in three categories: optional; semi-mandatory; and mandatory. There are some negative and positive impacts for the authors' paper and journals' reputation if mandatory citation of a paper or set of papers is requested. These effects can be different based on the recommended papers for citing in the new research. Mandatory citation has various types discussed in this paper. Self-citation and its reasons and impacts are also discussed in the present study.

Fake Journals: Their Features and Some Viable Ways to Distinguishing Them.

In this paper, we aim to discuss the fake journals and their advertisement and publication techniques. These types of journals mostly start and continue their activities by using the name of some indexed journals and establishing fake websites. The fake journals and publishers, while asking the authors for a significant amount of money for publishing their papers, have no peer-review process, publish the papers without any revision on the fake sites, and put the scientific reputation and prestige of the researchers in jeopardy. In the rest of the paper, we present some viable techniques in order for researchers and students to identify these journals.