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In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO2), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25–1.5%, a temperature range of T = 5–55 °C,...
Citations
... A higher heat transfer rate was obtained with heavy-density nanoparticles. Some experimental studies were also conducted to understand the rheological properties of ternary hybrid nanofluid in various applications, see [5][6][7]. Then, Bilal et al. [8] performed a numerical investigation on ternary hybrid nanofluid flow with various shapes such as planes, sheets, cones, and wedges, with the inclusion of a magnetic field. ...
... And, the boundary conditions from Eq. (13) become ya(1) − S, ya(2) − λ, ya(4), ya(5) − λ, ya(7), ya(9) − 1, yb(2), yb(5), yb(7) − 1, yb(9), } ...
... viscosity of diesel. The modification of surfactant with the nanoparticles increased the constancy of the nano-fuel and similarly reduced the diesel viscosity [39] [41]. With the increase in temperature the viscosity of pure diesel, nanoparticle added diesel with surfactant is obtained to be reduced. ...
... • Artificial neural network (ANN) [49][50][51][52][53]. ...
... Nanofluids' TPPs can create a complicated system when multiple input variables are present. This complexity makes it an excellent benchmark for regression algorithms that rely on machine learning [38]. Table 1 summarizes recent ML applications for anticipating the TPPs of hybrid nanofluids with various base fluids containing MWCNT-oxide nanomaterials. ...
... This research discusses two methods, GMDH-NN and COMBI, for ML modeling of thermophysical properties. GMDH-NN technique was historically preferred due to its formula-based system, resulting in high accuracy [38]. Sephernia et al. [38] found that the COMBI method, in comparison with GMDH-NN, could produce more accurate models with less complexity. ...
... GMDH-NN technique was historically preferred due to its formula-based system, resulting in high accuracy [38]. Sephernia et al. [38] found that the COMBI method, in comparison with GMDH-NN, could produce more accurate models with less complexity. COMBI algorithm, despite its significant potential, has been considered in a few researches. ...
The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their ther-mophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanoflu-ids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparti-cle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques , TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al 2 O 3-based nanofluid, around 90 % of the optimal points were within 40-50°C. Conversely, for nanofluids containing CeO 2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO 2 /ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.
... Metal oxide nanoparticles have high thermal and chemical stability and are free of environmental pollutants commonly found in traditional extreme pressure and anti-wear agents like chlorine, phosphorous, and sulfur [23]. Numerous metal nanoparticles such as nickel [24][25][26][27][28], copper [29][30][31][32][33][34][35], silver [36][37][38][39][40][41], iron [30,42,43], titanium [44][45][46][47][48][49], tin [43,50], gold [40,51], ceria [52][53][54][55] have proved to be effective to modify the rheological and/or the tribological properties of the lubricant. Typically, metal nanoparticles can progressively J Mater Sci leads to novel applications [60]. ...
This work reports the tribological assessment of ceria (CeO2) nanoparticles supported on multiwalled carbon nanotubes as water-based lubricant additives. Crystalline multiwalled carbon nanotubes (MWCNTs) were obtained by a spray pyrolysis method using alpha-pinene, a renewable feedstock, as carbon source and ferrocene as catalyst. CeO2 nanoparticles with an average diameter of ~ 1.7 nm homogenously distributed on the MWCNTs were synthetized using a microwave-assisted method. MWCNTs at 0.10 wt% and CeO2/MWCNTs composite at 0.10, 0.05 and 0.01 wt% concentrations were completely dispersed in water. Tribological tests were carried out in a commercial pin-on-disk tribometer at an operating load of 10 N for 1 h in steel–steel contacts. The results indicate that CeO2/MWCNTs composite can reduce friction and wear at the proper concentration. The lowest coefficient of friction (~ 0.11) was observed at 0.10 wt%, which represents a 74.4% reduction with respect to pure water. A 39.8% wear reduction was obtained at a 0.05 wt% concentration. The synergistic effect of CeO2 nanoparticles and MWCNTs promotes the formation of a tribofilm consisting of highly aligned graphene layer inside the wear tracks.
... The µ is due to the intermolecular force (IMF), and the movement of molecules boosts with enhancing T and the IMF weakens. Sepehrnia et al. 39,40 in their researches investigated THNF and approved that GO-MoO 3 -MWCNT-5W30 and GO-CeO 2 -SA-10W40 had a non-Newtonian-pseudoplastic behavior. ...
... µ nf > µ bf in all ϕ . Sepehrnia et al. 39,40 in their research showed that the values of the relative µ of the THNF is greater than the unit value. The results in Fig. 7 show that at low ϕ , the relative viscosity values are close to each other at different temperatures, but as the ϕ increases, the relative viscosity values at the minimum and maximum temperatures differ greatly, and it gets its highest value at ϕ=1.5%. ...
In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions ([Formula: see text] of 0.3%, 0.6%, 0.9%, 1.2%, and 1.5% and at temperatures 5 to 65 °C has been measured. This THNF is made in a two-step method and a viscometer device made in USA is used for viscosity measurements. The wear test was performed via a pin-on-disk tool according to the ASTM G99 standard. The outcomes show that the viscosity increases with the increase in the [Formula: see text], and the reduction in temperature. By enhancing the temperature by 60 °C, at [Formula: see text] = 1.2% and a shear rate (SR) of 50 rpm, a viscosity reduction of approximately 92% has been observed. Also, the results showed that with the rise in SR, the shear stress increased and the viscosity decreased. The estimated values of THNF viscosity at various SRs and temperatures show that its behavior is non-Newtonian. The efficacy of adding nanopowders (NPs) on the stability of the friction and wear behavior of the base oil has been studied. The findings of the test display that the wear rate and friction coefficient increased about 68% and 4.5% for [Formula: see text] = 1.5% compared to [Formula: see text] = 0. Neural network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gaussian process regression (GPR) based on machine learning (ML) have been used to model viscosity. Each model predicted the viscosity of the THNF well, and Rsquare > 0.99.
... For example, machine learning (ML), as a subset of AI, receives some data about a phenomenon, learns it well, and tries to present extra information about it by providing a precise model far beyond human capabilities [26,27]. The extraordinary efficiency of ML-based algorithms in various applications has caused the use of classical methods to be limited [28][29][30]. ...
... Meanwhile, the sensitivity analysis performed in the study by Hussain et al. [61] aided in determining sensitive parameters to the local skin friction coefficient and Nusselt number. Other researchers have recently incorporated sensitivity analysis in various fluid flow studies [62][63][64][65][66][67]. ...
... Sepehrnia et al. (2022b) performed an experimental analysis on the DV of engine oil based nanofluid with ZnO and MWCNT. They reported that by increment in the shear rate from 50 to 300 rpm, the highest decrement of DV was observed at 5 • C and was equal to 25.6% while with augmentation in shear rate from 700 to 1000 rpm, the least reduction in the DV (7.6%) was observed in VF of 0.05% and temperature of 55 • C. In another study (Sepehrnia et al., 2022a), DV of a ternary hybrid nanofluid, CeO 2 -GO-SA/10W40, was investigated by considering different temperatures and shear rates in range of 40-1000 rpm. It was pointed out that value of decrement in the DV of the nanofluid with increment of shear rate was dependent on the temperature. ...
Nanofluids, as the fluids with modified thermal characteristics, are usable for improving the efficiency of renewable energy systems and enhancement of thermal management. Dynamic viscosity is one of the most significant features of fluids involved in their heat transfer and flow characteristics. This property of nanofluids is under effect of various elements and their modeling is somehow complex. Intelligent methods, regarding their outstanding performance in modeling complex problems, are attractive tool for this purpose. Due to the importance of dynamic viscosity, it is necessary to have accurate model for prediction of this property of nanofluids. Nanofluids with MgO nanoparticles have shown significant performance in thermal mediums and energy devices. In the present article, two intelligent approaches including Adaptive Neuro Fuzzy Inference (ANFIS) and Group Method of Data Handling (GMDH) are made use for modeling dynamic viscosity of nanofluids with MgO nanoparticles. Comparing the determined values by the intelligent methods and the actual quantities revealed significant function of the applied approaches. Among these two methods, using GMDH is preferred regarding its more exactness based on the statistical criteria. R 2 of the mentioned methods based on GMDH and ANFIS were 0.9996 and 0.9616, respectively. Average Absolute Relative Deviation (AARD) is another criterion used for comparison of the proposed models that was around 5.13% and 19.41% for the mentioned models, respectively. According to these values, it is concluded that employment of GMDH is preferred in term of exactness.
... This method could prepare model development, parameter evaluation, and condition optimization. Also, the sensitivity analysis of the optimal conditions can be determined according to the various variables [38][39][40] . This method is applied for optimization to overcome expensive experimental costs. ...
Decreasing the conventional sources of oil reservoirs attracts researchers’ attention to the tertiary recovery of oil reservoirs, such as in-situ catalytic upgrading. In this contribution, the response surface methodology (RSM) approach and multi-objective optimization were utilized to investigate the effect of reaction temperature and catalysts soaking time on the concentration distribution of upgraded oil samples. To this end, 22 sets of experimental oil upgrading over Ni–W–Mo catalyst were utilized for the statistical modeling. Then, optimization based on the minimum reaction temperature, catalysts soaking time, gas, and residue wt.% was performed. Also, correlations for the prediction of concentration of different fractions (residue, vacuum gas oil (VGO), distillate, naphtha, and gases) as a function of independent factors were developed. Statistical results revealed that RSM model is in good agreement with experimental data and high coefficients of determination (R² = 0.96, 0.945, 0.97, 0.996, 0.89) are the witness for this claim. Finally, based on multi-objective optimization, 378.81 °C and 17.31 h were obtained as the optimum upgrading condition. In this condition, the composition of residue, VGO, distillate, naphtha, and gases are 6.798%, 39.23%, 32.93%, 16.865%, and 2.896%, respectively, and the optimum condition is worthwhile for the pilot and industrial application of catalyst injection during in-situ oil upgrading.
