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The purpose of this study is to evaluate the potentials of nano-additives in enhancement of oxidation and thermal stability of biolubricants thereby, improving the resistance of dispersed nanolubricants to thermal degradation under elevated temperature.

This study evaluates the oxidation stability and tribological performance of nano-enhanced biolubricants. Graphene and maghemite nanoparticles at 0.1% volume concentration were dispersed into coconut oil. Oxidation stability was analysed using a thermal analyser to understand the effect of nano-additives on thermal degradation of lubricants under increasing temperature. In addition, tribological performance and viscosity of the tested lubricants were evaluated using a four-ball friction tester and viscometer according to American Society for Testing and Materials standards.

The results reveal that the oxidation stability of biolubricants dispersed with nano-additives improves due to delayed thermal degradation. The nano-enhanced biolubricants’ oxidation onset temperature was delayed by 18.75 °C and 37.5 °C, respectively, for maghemite (MGCO) and graphene (XGCO) nanolubricants. This improvement imparts the performance viscosity and tribological performance positively. For graphene-enhanced nanolubricant, 10.4% and 5.6% were reduced, respectively, in coefficient of friction (COF)and wear scar diameter (WSD), whereas 3.43% and 4.3% reduction in COF and WSD, respectively, for maghemite-enhanced nanolubricant compared with coconut oil. The viscosity index of nanolubricants was augmented by 7.36% and 13.85%, respectively, for maghemite and graphene nanolubricants.

The excellent performance of nanolubricants makes them suitable candidate as sustainable lubricants for machining with regard to environmental benefits and energy saving.

The effect of graphene and maghemite nanoparticles on the oxidation stability and tribological performance of biolubricants has been investigated. It is an original work and yet to be published elsewhere.

The present work aims to numerically investigate the natural convective heat transfer performance of aluminium oxide (Al₂O₃)-gear oil nanolubricant used in heavy earth moving machinery (HEMM).

Viscosity, density and thermal conductivity of nanolubricants have been experimentally determined. The numerical simulation has been performed by using computational fluid dynamics (CFD) for a cylinder cavity which resembles shape of automatic transmission system of HEMM. The left wall temperature has been maintained at 293 to 313 K, and right wall is at a constant temperature of 283 K. Due to absence of any experimental study on natural convective heat transfer performance of Al₂O₃-gear oil nanolubricant, initially CFD model has been tested for accuracy by comparing experimental, and CFD results for Al₂O₃-water nanofluid has been available in open literature.

It has been observed that Nusselt number increases with increase in Rayleigh number, but it decreases with increasing particle volume fraction. The gear oil-based nanolubricant is expected to have the better thermal performance in HEMM at higher temperature.

The numerical analysis will help to predict the thermal performance of nanolubricant. The outcome may help the designers, researchers and manufacturers of HEMM.

Most of the previous studies have been limited with base fluid as water, ethylene glycol, etc. in the field of nanofluid. CFD study for thermal performance of Al₂O₃-gear oil nanolubricant is essential before the experimental work. This work is the preliminary stage of application of, nanolubricant for heat transfer.

The purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant.

Nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity.

The designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations.

This study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils.

To the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/

The purpose of this study is to investigate the effects of nanoadditive lubricants on the vibration and noise characteristics of helical gears compared with conventional lubricants. The experiment aims to analyze whether nanoadditive lubricants can effectively reduce gear vibration and noise under different speeds and loads. It also analyzes the sensitivity of the vibration reduction to load and speed changes. In addition, it compares the axial and radial vibration reduction effects. The goal is to explore the application of nanolubricants for vibration damping and noise reduction in gear transmissions. The results provide a basis for further research on nanolubricant effects under high-speed conditions.

Helical gears of 20CrMnTi were lubricated with conventional oil and nanoadditive oils. An open helical gearbox with spray lubrication was tested under different speeds (200–500 rpm) and loads (20–100 N·m). Gear noise was measured by a sound level meter. Axial and radial vibrations were detected using an M+P VibRunner system and fast Fourier transform analysis. Vibration spectrums under conventional and nanolubrication were compared. Gear tooth surfaces were observed after testing. The experiment aimed to analyze the noise and vibration reduction effects of nanoadditive lubricants on helical gears and the sensitivity to load and speed.

The key findings are that nanoadditive lubricants significantly reduce the axial and radial vibrations of helical gears under low-speed conditions compared with conventional lubricants, with a more pronounced effect on axial vibrations. The vibration reduction is more sensitive to rotational speed than load. At the same load and speed, nanolubrication reduces noise by 2%–5% versus conventional lubrication. Nanoparticles change the friction from sliding to rolling and compensate for meshing errors, leading to smoother vibrations. The nanolubricants alter the gear tooth surfaces and optimize the microtopography. The results provide a basis for exploring nanolubricant effects under high speeds.

The originality and value of this work is the experimental analysis of the effects of nanoadditive lubricants on the vibration and noise characteristics of hard tooth surface helical gears, which has rarely been studied before. The comparative results under different speeds and loads provide new insights into the vibration damping capabilities of nanolubricants in gear transmissions. The findings reveal the higher sensitivity to rotational speed versus load and the differences in axial and radial vibration reduction. The exploration of nanolubricant effects on gear tribological performance and surface interactions provides a valuable reference for further research, especially under higher speed conditions closer to real applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0220/

Studies on this topic have shown the remarkable lubricating properties, viz. friction-reducing and anti-wear, of certain nanoparticles. This makes them potential candidates for replacing the lubrication additives currently used in automobile lubricants, especially because the latter is known to be pollutants and less efficient in some specific conditions. This has not gone unnoticed to professionals in the sector, including those commercializing these additives, the oil companies and the car industry, all of whom are following this burgeoning research area with keen interest. All of them are faced with the problem of providing lubricants that meet the needs of the technological evolution of engines while respecting ever-stricter environmental norms.

The impact of copper oxide (CuO) and zinc oxide (ZnO) nanoparticles on the tribological properties of the SAE-40 pure diesel oil is studied in this paper. The two nanoparticles are not oxide or deteriorate with the base oil. The average size of CuO and ZnO nanoparticles is 40 and 20 nm, respectively. Nanoparticle concentrations of 0.1 Wt.%, 0.2 Wt.%, 0.3 Wt.%, 0.4 Wt.% and 0.5 Wt.% are tested using a pin-on-disk tribometer to evaluate their impact on friction and wear. The test is carried out at different loads and rotating speeds of 58.86 N and 300 rpm, 39.24 N and 500 rpm and 78.48 N and 900 rpm at room temperature, respectively.

The obtained results of the nanolubricants are compared with those of pure diesel oil in terms of % improvement in tribological properties. However, it is observed that an increase in the nanoparticle concentrations does not guarantee to enhance the tribological properties. Similarly, increasing the applied load and the rotating speed does not lead to improving the anti-friction and anti-wear properties. The results obtained revealed that the optimal improvements in the anti-friction and anti-wear properties of the pure oil are 69% and 77% when CuO nanoparticle concentrations of 0.3 Wt.% and the ZnO nanoparticle concentrations of 0.1 Wt.% are used, where the applied load and rotating speed are 39.24 N and 500 rpm, respectively. It has also been noticed that the CuO nanolubricants have a significant impact on the anti-friction property compared with ZnO nanolubricants.

All these nanoparticles have been the subject of detailed investigation in this research and many key issues have been tackled, such as the conditions leading to these properties, the lubrication mechanisms coming into play, the influence of parameters such as size, structure and morphology of the nanoparticles on their tribological properties/lubrication mechanisms and the interactions between the particles and the lubricant co-additives. To answer such questions, state-of-the-art characterization techniques are required, often in situ, and sometimes an extremely complex set up. Some of these can even visualize the behavior of a nanoparticle in real time during a tribological test. The research on this topic has given a good understanding of the way these nanoparticles behave, and we can now identify the key parameters to be adjusted when optimizing their lubrication properties.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2022-0234/

The purpose of this paper is to propose a nanodiamond-particle-loaded food-grade lubricating oil, a nanolubricant, that can be used over a broad range of loads in factories (low load applications like conveyor systems and high load applications like heavy machinery).

Tribological performance of the nanolubricant was studied at both load levels. A typical factory-sized conveyor belt used for beverage packaging (aluminium cans, glass and PET bottles) was employed for the low load range. Coefficients of friction and wear scars were measured and the lubricating performance was quantified. A four-ball tester was used to characterise the performance of the nanolubricant as per ASTM D2783/D4172. A comparison between the nanolubricant and baseline oils was carried out.

Results show an overall decrease in the coefficients of friction and wear scars for all packages at low pressures when the nanolubricant is used. They also show a better friction-reduction performance in the high load regimes. The results indicate that the nanolubricant is versatile in both ranges of loading.

The current protocols for lubrication in the food and drink factories involve the use of water-based detergents for the conveyor lines and industry-grade oils for the machinery. The use of a single and versatile lubricant for both ranges of loads may have a positive impact on the sustainability and environmental performance of the sector.

Beverage processing and packing factories need their mechanised conveyor systems suitably lubricated to avoid excessive friction between the containers and the load-bearing surface of the conveyors (e.g. belts or chains). Other areas of the conveying systems, such as motors, gears, rollers and bearings, also need suitable lubrication to prevent failure and lengthen their operating life. There is a myriad of lubricants and lubricating solutions for each of these areas independently, but there is no commercial lubricating fluid that could be used on both successfully.

The purpose of this paper is to investigate the anti-wear (AW) and extreme pressure (EP) performances of CuO and graphite nanoparticles as a palm oil additive.

In this study, the AW and EP performances of CuO and graphite nanoparticles as additives in palm oil were evaluated using four ball tribotester in accordance to ASTM D4172 and ASTM D2783, respectively. The wear worn surfaces of the steel balls were analysed using high resolution microscope.

The results obtained demonstrate that CuO and graphite nanoparticles improved the AW and EP performances of the palm oil up to 2.77 and 12 per cent, respectively. The graphite nanoparticles provide better AW and EP performance than that of CuO nanoparticles.

This demonstrates the potential of CuO and graphite nanoparticles for improving AW and EP performances of palm oil base lubricant. Different morphology of nanoparticles will affect the AW and EP performances of nanolubricants.

The purpose of this paper is to experimentally investigate the effect of aluminium oxide (Al₂O₃) nanoparticles on gear oil (SAE EP 90) as a lubricant in heavy earth moving machinery (HEMM).

Particle size distribution, viscosity, density, stability and other rheological properties have been measured. The variations in rheological properties with varying nanoparticle volume fraction and temperature have been investigated at atmospheric pressure over a temperature range of 15-40°C. Classical as well as modified Krieger – Dougherty models have been used for finding out viscosity variation and a new empirical model has been presented.

Dynamic light scattering data confirm the presence of large agglomeration of about 5.5 times of primary nanoparticles in nanofluid. Nanofluid starts behaving as a non-Newtonian fluid with increasing nanoparticle volume fraction. Viscosity of nanofluid is enhanced by 1.7 times of base fluid with 2 per cent volume fraction of Al₂O₃ nanoparticles, while it significantly decreases with increase in temperature. The stability of nanofluid decreases with increase in nanoparticle volume fraction due to settling down of nanoparticles. It has also been observed that shear thinning increases with increasing nanoparticle volume fraction.

It is expected that these findings will contribute towards the improvement in rheological and thermal properties of the conventional lubricants used in HEMM. The outcome may help the designers, researchers and manufacturers of the HEMM.

Most of the previous research in this field is confined with base fluid as water, ethylene glycol, transformer oil, etc. Gear oil in HEMM performs under high mechanical and thermal load. The Al₂O₃/gear oil nanofluid is expected to have better cooling and lubrication properties.

The purpose of the present work is to investigate the hydrodynamic and thermal performance of a thermal storage based on the numerical and experimental approaches using the lattice Boltzmann method and the experimental observation on the thermo-physical properties of the operating fluid.

For this purpose, the Al₂O₃ nanoparticle is added to the lubricant with four nanoparticle concentrations, including 0.1, 0.2, 0.4 and 0.6Vol.%. After preparing the nanolubricant samples, the thermal conductivity and dynamic viscosity of nanolubricant are measured using thermal analyzer and viscometer, respectively. Finally, the extracted data are used in the numerical simulation using provided correlations. In the numerical process, the lattice Boltzmann equations based on Bhatnagar–Gross Krook model are used. Also, some modifications are applied to treat with the complex boundary conditions. In addition, the second law analysis is used based on the local and total views.

Different types of results are reported, including the flow structure, temperature distribution, contours of local entropy generation, value of average Nusselt number, value of entropy generation and value of Bejan number.

The originality of this work is combining a modern numerical methodology with experimental data to simulate the convective flow for an industrial application.

This paper aims to evaluate the dynamic viscosity and thermal conductivity of halloysite nanotubes (HNTs) suspended in SAE 5W40 using machine learning methods (MLMs).

A two-step method with surfactant was selected to prepare nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 wt%. Thermal conductivity and dynamic viscosity of nanofluids were ascertained over the temperature range of 25–70 °C, with an increment step of 5 °C, using a KD2-Pro analyser device and a digital viscometer MRC VIS-8. Additionally, four different MLMs, including Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), were used for predicting dynamic viscosity and thermal conductivity by using nanoparticle concentration and temperature as input parameters.

According to the achieved results, the dynamic viscosity and thermal conductivity of nanolubricants mostly increased with the rise of nanoparticle concentration in the base oil. All the proposed models, especially GPR with root mean square error mean values of 0.0047 for dynamic viscosity and 0.0016 for thermal conductivity, basically showed superior ability and stability to estimate the viscosity and thermal conductivity of nanolubricants.

The results of this paper could contribute to optimising the cost and time required for modelling the thermophysical properties of lubricants.

To the best of the author’s knowledge, in this available literature, there is no paper dealing with experimental study and prediction of dynamic viscosity and thermal conductivity of HNTs-based nanolubricant using GPR, ANN, SVM and DT.