A novel study on rheological behavior of ZnO-MWCNT/10w40 nanofluid for automotive engines

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

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, and a shear rate range of SR = 40-1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO₂, GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R² = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Rheological Behavior of SAE50 Oil-SnO2-CeO2 Hybrid Nanofluid: Experimental Investigation and Modeling Utilizing Response Surface Method and Machine Learning Techniques

In this study, for the first time, the effects of temperature and nanopowder volume fraction (NPSVF) on the viscosity and the rheological behavior of SAE50-SnO₂-CeO₂ hybrid nanofluid have been studied experimentally. Nanofluids in NPSVFs of 0.25% to 1.5% have been made by a two-step method. Experiments have been performed at temperatures of 25 to 67 °C and shear rates (SRs) of 1333 to 2932.6 s^-1. The results revealed that for base fluid and nanofluid, shear stress increases with increasing SR and decreasing temperature. By increasing the temperature to about 42 °C at a NPSVF of 1.5%, about 89.36% reduction in viscosity is observed. The viscosity increases with increasing NPSVF about 37.18% at 25 °C. In all states, a non-Newtonian pseudo-plastic behavior has been observed for the base fluid and nanofluid. The highest relative viscosity occurs for NPSVF = 1.5%, temperature = 25 °C and SR = 2932.6 s^-1, which increases the viscosity by 37.18% compared to the base fluid. The sensitivity analysis indicated that the highest sensitivity is related to temperature and the lowest sensitivity is related to SR. Response surface method, curve fitting method, adaptive neuro-fuzzy inference system and Gaussian process regression (GPR) have been used to predict the dynamic viscosity. Based on the results, all four models can predict the dynamic viscosity. However, the GPR model has better performance than the other models.

Comparative Study of Carbon Nanosphere and Carbon Nanopowder on Viscosity and Thermal Conductivity of Nanofluids

A comparative research on stability, viscosity (µ), and thermal conductivity (k) of carbon nanosphere (CNS) and carbon nanopowder (CNP) nanofluids was performed. CNS was synthesized by the hydrothermal method, while CNP was provided by the manufacturer. Stable nanofluids at high concentrations 0.5, 1.0, and 1.5 vol% were prepared successfully. The properties of CNS and CNP nanoparticles were analyzed with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), specific surface area (SBET), X-ray powder diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and energy dispersive X-ray analysis (EDX). The CNP nanofluids have the highest k enhancement of 10.61% for 1.5 vol% concentration compared to the base fluid, while the CNS does not make the thermal conductivity of nanofluids (knf) significantly higher. The studied nanofluids were Newtonian. The relative µ of CNS and CNP nanofluids was 1.04 and 1.07 at 0.5 vol% concentration and 30 °C. These results can be explained by the different sizes and crystallinity of the used nanoparticles.

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.