Nano Lubricant Research Articles

A comparative analysis of dovetail and rectangular fins with insulated tips wetted with ZnO-SAE 50 nanolubricant for energy transfer process

This study focuses on exploring innovative methods of energy conservation specifically through the nonlinear fin equation. To improve the heat transfer efficiency in cooling systems, the research suggests utilizing nano lubricants that possess superior thermo-physical properties. Unlike other nanofluids, the use of nanolubricants can decrease corrosion and abrasion in operational parts such as the bore-piston, gasket, and valve mechanisms. The study concentrates on the temperature distribution in both rectangular and longitudinal dovetail fins utilized in double-pipe heat exchanger applications. The energy equation is formulated to consider radiation, temperature-dependent internal heat generation, and wetted conditions, and is subsequently transformed into a dimensionless form. Numerical solutions to the resulting differential equation are obtained through the utilization of the collocation method. The study explores the use of a specific heat transfer liquid (ZnO-SAE 50) nanolubricant to analyze thermal dispersion in both straight rectangular and dovetail fins. Additionally, it investigates how temperature-dependent internal heat generation impacts convection-conduction heat transfer. The study finds that an increase in surface emissivity results in greater heat transfer through the fin's surface in the presence of nanolubricant.

Tribological characterization of laminated hybrid AA1050/TiC/Graphite composite bars

Hybrid composites (HC) refer to a type of material that combines aluminum (Al), titanium carbide (TiC), and graphite (Gr) at the nano level. These HC have shown promise in applications requiring high strength, wear resistance (WR), and tribological performance, such as automotive, aerospace, and industrial sectors. In this study, these HC are made using a combination of Powder metallurgy (PM) and accumulative press bonding (APB) processes have been developed. This is the first time that the wear resistance of a hybrid metal matrix composite fabricated with Gr as a solid lubricant has been done and thid is the novelty of this study. In fact, the presence of TiC nanoparticles (NP) provides improved mechanical properties, such as hardness (Hr), strength, and WR for HC. On the other hand, Gr acts as a solid nano-lubricant (NLU) in HC, reducing friction and WR during sliding contact. The presence of Gr-NP also helps to form a durable Gr-nanolayer on tribo surfaces and further improves the WR of HC. This study used a scanning electron microscope (SEM). The results demonstrated that incorporating TiC- NP reduced the WR rate and promoted NL development at extended sliding distances, creating a durable TiC/Gr HC on the TS. Finally, the improved WR of Al/TiC/Gr-HC can be attributed to the stability of the Gr-NL on the TS.

Elucidation of the synergistic mechanism of nano silicon carbide and titanium nitride as highly efficient additives of polyalphaolefin on strengthening the lubrication performance of movable contact interface

This study comprehensively investigated physicochemical and tribological performance of polyalphaolefin (PAO4) oil introducing oleic acid (OA)-functionalized SiC/TiN hybrid nanomaterials. The dispersion stability, wettability, thermal stability, and friction properties of the nanolubricants (NL) system were greatly improved following the application of hybrid NL. Additionally, the anti-wear and reducing-friction behavior were assessed via a four-ball tribometer. The results showed that the COF and WSD values of wear scar lubricated by hybrid NL were greatly reduced by approximately 32.7% and 35.1%, respectively, compared with the base oil. Finally, it is strongly confirmed that the significant improvement of friction performance is mainly due to improved interfacial effect of NL, the stable chemical tribo-film generated onto the friction surface, and the synergistic effect among SiC, TiN nanoparticles, and OA surfactant.

Tribological performance for an Mg alloy subjected to sliding at elevated temperatures: A comparison between Al2O3 and MoS2 nano additives in silicone oil-based lubricant

Magnesium (Mg) and its alloys are crucial for energy-efficient vehicles due to their strong and lightweight properties, necessitating high-temperature fabrication and lubrication to reduce oxidation, friction, and wear. This paper investigated the tribological performance of lubricating oil using different particles to a Mg alloy subjected to contact sliding in an open system at high temperatures up to 250 °C. The overall experimental results showed improved tribological performances by using the two types of nanoparticles / nanoparticle-clusters (concentration ≥ 0.5 wt%) compared with the base silicone oil. The mechanisms by which the two types of nano lubricants improved the tribological performance are different. Al2O3 particles provided a more stable Al-rich anti-wear surface at higher temperatures in the range of 200–250 °C, while a tribo-chemical reaction film was formed with MoS2 additives, generating MoO3 and MgSO4. It is generally concluded that Al2O3 has more positive effect in terms of reduced coefficient of friction and wear rate as comparison to MoS2.

Tribological Performance Evaluation of TMPTO Based Nano-Lubricants

In recent years, nano lubricants have gained significant attention due to their potential to enhance lubricant efficacy and reduce friction and wear. This research work aims to investigate the effects of TiO2 nanoparticles into trimethylolpropane trioleate (TMPTO) based bio-lubricant on its friction and wear characteristics. The four-ball tester is employed to evaluate the lubricating performance of the TMPTO-TiO2 nano-lubricant at varying additive concentrations under controlled conditions of speed, load, and temperature. The parameters were ranked based on the results of the Taguchi experiments and their corresponding single-to-noise ratios. The combination of TMPTO base oil and TiO2 nano-lubricant exhibited a synergistic effect in diminishing friction and wear. This research aligns with the growing demand for environmentally friendly and efficient lubrication solutions in various metalworking industries. Further investigation of the wear mechanism under TMPTO oil-based nano lubricants and its applicability in high-speed metal cutting applications is suggested.

Enhanced extreme pressure and tribological performance of hybrid nano lubricant

ABSTRACT The present study reports the tribological performance of a novel hybrid nano-lubricant for bronze/AISI 4340 alloy-steel tribopair. The hybrid nano-additive used in the present study helps to improve the lubricity and can sustain under extreme conditions. Initially, friction and wear properties are recorded by varying concentrations of Al2O3 (0.5, 0.8, 1.0, and 1.5 wt. %) and GNP (0.05, 0.1, 0.2, and 0.5 wt. %). Based on the results, an optimum hybrid concentration (OHC) is prepared by adding 0.8 wt. % of Al2O3 and 0.2 wt. % of GNP which resulted in 83.84% and 79.75% reductions in coefficient of friction (COF) in comparison to optimum Al2O3-based and GNP-based lubricants, respectively. However, the wear volume (WV) for OHC lubricant indicated 98.28% and 75% reduction in comparison to the above-mentioned lubricants, correspondingly. Extreme pressure (EP) tests were carried out and OHC lubricant reported improved weld point for hybrid nano additive-based lubricant.

Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment

Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary to full-film hydrodynamic lubrication. The possibility to improve the tribological performance of lubricating oils using various types of nanoparticles has been investigated. In this study, we proposed a data-driven approach that utilizes machine learning (ML) techniques to optimize the composition of a hybrid oil by adding ceramic and carbon-based nanoparticles in varying concentrations to the base oil. Supervised-learning-based regression methods including support vector machines, random forest trees, and artificial neural network (ANN) models are developed to capture the inherent non-linear behavior of the nano lubricants. The ANN hyperparameters were fine-tuned with Bayesian optimization. The regression performance is evaluated with multiple assessment metrics such as the root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The ANN showed the best prediction performance among all ML models, with 2.22 × 10−3 RMSE, 4.92 × 10−6 MSE, 2.1 × 10−3 MAE, and 0.99 R2. The computational models’ performance curves for the different nanoparticles and how the composition affects the interface were investigated. The results show that the composition of the optimized hybrid oil was highly dependent on the lubrication regime and that the coefficient of friction was significantly reduced when optimal concentrations of ceramic and carbon-based nanoparticles are added to the base oil. The proposed research work has potential applications in designing hybrid nano lubricants to achieve optimized tribological performance in changing lubrication regimes.

Tribological evaluation of PEG-based nanolubricants modified with graphene and copper oxide

Nanolubricants are dispersions of nanomaterials in a lubricating base. In this work, polyethylene glycol (PEG) was modified with graphene (G) and copper oxide (CuO) to obtain a nanolubricant (NL). The nanolubricants were prepared and their physicochemical and tribological properties were evaluated. The base and the nanomaterials were evaluated using Fourier-transform infrared spectrometry (FTIR), field-emission scanning electron microscopy coupled with energy dispersive spectroscopy (FE-SEM-EDS), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and Raman spectroscopy (Raman). Rheology and Tribology tests were also performed using a four-ball tester according to ASTM 2783. The results showed that nanolubricants have a Newtonian behavior and the viscosity was not significantly modified with the additives. The weld point obtained for the nanolubricants was 160 kgf. In addition, when compared to the PEG, the size of the wear scars was reduced by 9.6% (PEG with 0.01% w/w of CuO) and 35.3% (PEG with 0.05% w/w of CuO and G). The wear protection provided by G is higher than the protection provided by CuO. When the two additives were added, the wear protection increased, and a synergistic effect was observed. Two protection mechanisms were observed in the SEM: rolling bearing/ball effect (CuO) and deposition of G at valleys and peaks of the surface roughness. This article shows novel results related to a laboratory evaluation of nanolubricants in a typical tribological test (four-ball). A novel result was observed in the synergistic effect between the two additives (graphene and CuO) determined by the wear mechanism observed in the tests.

Enhancement in tribological performance of plastic oil by solid lubricant additives

AbstractIn this study, plastic oil (PO) as a potential lubricant was investigated. The base PO was incorporated with graphene nanoplatelets (GNP) and hexagonal boron nitride (hBN) nano additives in varying concentrations to form nano lubricants. Their viscosity, tribological, acidic/basic nature, thermal degradation and dispersion stability properties were investigated. It was observed that 1.5 wt% GNP and 1.0 wt% hBN added separately to the base PO, provided the lowest coefficient of friction (COF) and wear volume. Based on these lowest COF and wear volume insights, three nano lubricant mixtures were formulated by incorporating both GNP and hBN at different combinations using base PO. Positive synergistic behaviour was observed for COF (49%–60% reduced) and wear volume (90%–97% reduced) for two combination mixtures compared to the base PO. These improvements in the mixture were due to the polishing, mending mechanisms and tribofilm that protected the interacting surfaces.

A tribological behavior assessment of steel contacting interface lubricated by engine oil introducing layered structural nanomaterials functionalized by oleic acid

Traditional lubricating oil in the lubrication process leads to oil film rupture and lubrication failure due to thermal instability under high temperatures. Tribological characteristics of synthetic oil (5W-40) can be potentially improved by the application of layered nanoparticles (h-BN and MoS2 NPs). The tribological properties of base oil, MoS2, and h-BN nanolubricants (NLs) were comprehensively investigated under varying loads, rotating speeds, and temperatures using a four-ball tribometer. Moreover, this study confirmed that the COF and wear rate values of wear scar lubricated by h-BN NL and MoS2 NL decreased by approximately 21.5% and 13%, 91.6% and 90.9%, respectively, compared with base oil, showing the brilliant tribological performance following the introduction of NL. The results showed the wear interface lubricated by NL with oleic acid (OA) functionalization contributed to the appearance of tribo-film along with chemical absorption film due to the complicated oxidizing reaction. Eventually, the synergistic role between OA and nanomaterials responsible for the increasing tribological properties is elaborately elucidated.

Effect of copper nano-cutting fluids in end milling of al 6061 and its influence on work piece morphology

Due to its exceptional mechanical qualities, aluminium 6061alloy is frequently utilised at automotive and aerospace industries. During the machining process, lubricating oil is typically supplied into the cutting zone to enhance machining performance, including tool life and workpiece surface quality. However, conventional coolants perform less effectiveat higher temperatures. The introduction of Copper Nano coolant at cutting interface could sustain greater temperatures. The surface finish of work piece is improved significantly as the Nano coolants at the tool-chip interface considerably reduces friction and thermal deformation. Utilizing the Minimum Quantity Lubrication (MQL) approach could reduce the amount of lubrication oil needed, reducing pollution. Cu Nano lubricants are made in concentrations of 0.2, 0.6, and 1.0% vol. in a water–oil base lubricant in a 20:1 ratio. The size, structure, and elemental content of the Cu nanoparticles are studied using EDX and FESEM. Cutting force, temperature, and surface quality are alsomeasured. It has been noted that the use of copper Nano coolants enhanced the workpiece's surface polish and greatly decreased the cutting forces and cutting temperatures. The development of thin layer over work piece is responsible for improved surface finish and reduced cutting forces and temperatures.

Nanotechnology as a sustainable approach for combating the environmental effects of climate change

Currently, there are a huge number of environmental issues that the world is facing from past few decades. However, the climate change is possibly the major environmental threat to deal with. The rise of 1.5–2 °C surface temperature has been recorded in last 40–50 years. In next 50–100 years the earth temperature will create harsh conditions for living and consequences would be catastrophic. Global warming is the major cause of climate change. The common reason of global warming is carbon-associated gas emission (greenhouse gases) from burning of fossil fuels in various industries, transportation, electricity production, agriculture and commercial sources. Though, pollution, urbanization, population etc. in similar way also contribute to climate change by disturbing the balance of the ecosystem. Nanotechnology, because of unique properties of nanomaterials, offers a wide range of applications in environment, agriculture, food and energy sectors. Not only the environmental nanotechnology can handle a range of environmental problems but also the nanotechnological products and processes are considered as the most effective and innovative tools/mode to accomplish sustainability goals. Nanostructured materials such as nanocomposites, functionalized nanomaterials, metal organic frameworks, nanocatalysts, carbonaceous materials, nano zeolites, nano silica, nano lubricants and nano coatings etc. have enormous possibilities in sequestration and reduction of greenhouse gases, biofuel production, wastewater treatment and environmental remediation using a sustainable approach. The present paper is an attempt to summarize the nanotechnology-based approaches to combat climate change. It aims to review the long-term effects of new nanocompounds to environment and promotion of sustainable methods to solve the climate change related problems.

Enhancement of R600a vapour compression refrigeration system with MWCNT/TiO2 hybrid nano lubricants for net zero emissions building

Net zero emissions building is widely investigated with great environmental care. In the case of refrigeration selection for net zero emissions building (NZEB), the ozone depletion potential is the primary criterion to choose the refrigerant. For achieving the NZEB, the R600a was preferred as it possesses the potential for global warming lower and zero potential for ozone depletion. This paper aims to amplify the coefficient of performance by utilizing MWCNT/TiO2 hybrid Nano lubricants in the R600a vapour compression refrigeration system. As numerous factors and equations are involved in the study and prediction of the Coefficient of Performance in vapour compression refrigeration systems which is comparatively complex and takes more time for promoting the development of precise prediction and results. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. By using the ANFIS technique enhanced cooling effect of 200 W with a 50 % increment was obtained with 0.4 g/L of MWCNT/TiO2 hybrid nano lubricants which is better in comparison with ANN and experimental results. The minimum energy utilization of 90 W was obtained with the ANFIS technique. This method also predicted the enhanced COP of 3.7 with a 32 % increase in comparison to the ANN prediction method. When compared to the ANN prediction model, the ANFIS model's estimated least training error value. The results indicate that when compared to ANN prediction the ANFIS predicted values produced results that were more accurate and were the proper approach for predicting COP parameters and consumed 35 % less energy.

Impact of Thermal and Activation Energies on Glauert Wall Jet (WJ) Heat and Mass Transfer Flows Induced by ZnO-SAE50 Nano Lubricants with Chemical Reaction: The Case of Brinkman-Extended Darcy Model

Heat transfer machinery or technology is rapidly expanding due to the need for effective cooling and heating systems in the requisite automotive, chemical, and aerospace industries. This study aims to provide a numerical solution to wall jet (WJ) flow with mass and heat transport phenomenon comprising of the colloidal mixture of SAE50 and zinc oxide nanoparticles immersed in a Brinkman-extended Darcy model. The idea of WJ flow suggested by Glauert is further discussed along with the impact of the activation energy, thermal radiation, and binary chemical reaction. The leading equations are transformed into ordinary differential equations through proper similarity variables and then worked out numerically by employing a very efficient bvp4c method. The importance of pertaining quantities is illustrated and well explained through several tables and graphs. The major results suggest that the velocity profiles decline while the temperature and concentration augment due to the higher impact of nanoparticles volume fraction. In addition, the shear stress and heat transfer rate are accelerated by rising the volume fraction of nanoparticles while the Sherwood number declines with bigger impacts of nanoparticle volume fraction. In addition, the radiation factor progresses the quantitative outcomes of the heat transfer rate.

Tribological Performance for Steel–Steel Contact Interfaces Using Hybrid MWCNTs/Al2O3 Nanoparticles as Oil-Based Additives in Engines

Numerous problems occur during engine operation, such as start-up, lack of lubrication, and overheating, resulting in engine components’ wear, power loss, and fuel consumption. Nanomaterials dispersed in engine oil can play an important role in improving the tribological properties of oil lubricants. This study investigated the influence of multi-walled carbon nanotubes (MWCNTs) and aluminum oxide nanoparticles (Al2O3 NPs) as nano-additives for lubricants. Different engine oil samples were loaded with 0.5–2.0 wt% Al2O3 NPs and 0.5–1.0 wt% MWCNTs and compared with unmodified oil. The tribological performance of the nano lubricants was investigated using the four-ball test method. In addition, the wear scar in the engine was evaluated using 3D micrographs and scanning electron microscopy (SEM). The results of the sliding surfaces with hybrid MWCNTs/Al2O3 NPs showed better friction performance and wear resistance. The coefficient of friction (COF) and wear scar width were improved by 47.9% and 51.5%, respectively, compared with unmodified oil.

Increasing the accuracy of estimating the dynamic viscosity of hybrid nano-lubricants containing MWCNT-MgO by optimizing using an artificial neural network

Artificial neural network (ANN) is utilized as efficient models to forecast the nanofluids (NFs) viscosity (μnf). In this examination, ANN is used to forecast the μnf of the MWCNT-MgO (25 % −75 %) / SAE40 nano-lubricant (NL) experimental data set. Experimental evaluation of NLs is taken in volume fraction of nanoparticles (NPs) φ= 0.0625 % −1% and temperature range of T = 25 to 50 °C. To predict the μnf of the data using ANN, a multilayer perceptron (MLP) ANN with the algorithm of Levenberg-Marquardt (LM) is utilized. For ANN modeling, temperature, φ and shear rate (γ̇) are determined as inputs and μnf is determined as output. From 400 various ANN samples for NL, the optimal sample (OS) is selected, comprising two hidden layers (HLs) with the OS of 8 and 5 neurons in the primary and second layer, respectively. Eventually, for the OS, the amount of the regression coefficient (RC) and the mean square error (MSE) are set equal to 0.9999882 and 0.001453292, respectively. The margin of deviation (MOD) for all ANN information is in the range of less than −1% <MOD <+1%. It's good because the ANN pattern is more precise and has a great ability to forecast μnf. The main goal of this research is to model and estimate the μnf of MWCNT-MgO (25:75)/SAE40 NL through ANN and also to select the optimal structure from the set of predicted ANN structures and manage time and cost.

Nanoparticle Additives for Improving Lubricant Performance

The lubricant is an important industrial product widely used in vehicles and other machines. The main purpose of a lubricant is to reduce friction loss and save energy. However, with the development of the vehicle, the appearance of electrical vehicles, for example, and machines, lubricants need to satisfy more demands. Normally, this was fixed by adding additives. Nanoparticles are one of the best additives because they can efficiently reduce friction loss and provide a more useful function. But nanoparticles may form sedimentation in lubrication, which will increase friction loss and cause damage. Common methods like stirring, ball milling, and chemical methods are used in lab production to keep particles stable in lubricant. All those methods can form relatively stable samples of nano lubricants, but it is still hard for industrial manufacturers because of the technology blank and high cost. Nano lubricant is a potential market and can save more energy in the future.

Optimizing the Process Parameters for Eco-Friendly Minimum Quantity Lubrication-Turning of AISI 4340 Alloy with Nano-Lubricants Using a Grey Wolf Optimization Approach

Optimization of turning process parameters in minimum quantity lubrication (MQL)-assisted mode is obligatory for enhanced efficiency and product integrity. However, little attention has been paid to analyzing situations where high search precision is needed when evaluating the optimal turning process parameters. This article applies the grey wolf optimization (GWO) approach to optimize the turning of parameters AISI 4340 alloy to enhance cutting force, surface roughness and tool wear. Based on the literature data, turning was conducted with MQL-assisted CuO and Al2O3 nanofluids. The problem was formulated by mimicking six wolves in six different objective functions. The objective functions have the responses as the dependent variables and the parameters including cutting speed, feed and cutting depth as independent variables. The hunting behavior of the wolves as they encircle the prey is interpreted to the machining task optimization. It involves three hierarchically-evaluated guides- the alpha, beta and delta wolves- positioned optimally and other wolves are updated accordingly. The cutting speed, feed and cutting depth are bound in the lower and upper limits as 80 and 140m/min, 0.05 and 0.20m/m/rev and 0.1 and 0.4mm, respectively. The grey wolf optimization algorithm optimizes the parameters to yield the cutting force, surface roughness and tool wear using Al2O3 as 199.50N, -23.54mm and 0.06mm, respectively. For the CuO, the corresponding cutting force, surface roughness and tool wear, the CuO, Al2O3 and CuO nano lubricants produced the best results. However, for mass production, selective use of CuO and Al2O3 should be made. The usefulness of this research endeavor is to help process engineers to make decisions in producing low-cost components in manufacturing.

Analysis of Jet Wall Flow and Heat Transfer Conveying ZnO-SAE50 Nano Lubricants Saturated in Darcy-Brinkman Porous Medium

The problem of 2D (two-dimensional) wall jet flow, along with heat transfer incorporated by nanofluid in a Darcy-Brinkman medium, while recognizing the requirement for efficient heating and cooling systems. Following the use of similarity variables, the resultant system of ODEs (ordinary differential equations) is solved using the well-known and efficient bvp4c (boundary-value problem of the 4th order) technique. The significance of physical quantities for the under-consideration parameters is illustrated and explained. The findings show that the nanoparticle volume fraction and porosity parameters decrease the velocity, but increase the temperature. In addition, the temperature uplifts in the presence of radiation effect. The suction parameter initially decreases and then increases the velocity near the surface, while the temperature declines.

Tribological performance of silicone oil based Al2O3 nano lubricant for an Mg alloy subjected to sliding at elevated temperatures

Lubricants are commonly used in manufacturing processes, such as rolling and drawing, to protect contact surfaces in a relative movement against friction and wear. Currently, there are few studies on fluid lubricants for the fabrication of large-scale Mg alloy components at high temperatures in the open air. This paper aims to design a type of silicone oil based Al2O3 nano lubricant and investigate its effect on the tribological performance of Mg AZ31B sliding against 86CrMoV7 steel under elevated temperatures with the aid of the pin-on-disc experiments. It was found that the width and depth of the wear tracks at 250 °C are dramatically reduced when the concentration of the Al2O3 nanoparticle additives reaches 0.50 wt% and above, and that there is a strong correlation between the wear rate and average friction coefficient. The investigation revealed that the lubrication mechanism for the contact pair using pure silicon oil at 100 °C is due to the formation of a Si-rich tribofilm, whereas the lubricant with Al2O3 nanoparticles provides a more stable Al-rich anti-wear surface at higher temperatures in the range of 200–250 °C.