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The purpose of this study is to design and process the optimal V-shaped microstructure for 7075 aluminum alloy and reveal its wear resistance mechanism and performance.

The hydrodynamic pressure lubrication models of the nontextured, V-shaped, circular and square microtextures are established. The corresponding oil film pressure distributions are explored. The friction and wear experiments are conducted on a rotating device. The effects of the microstructure shapes and sizes on the wear mechanisms are investigated via the friction coefficients and surface morphologies.

In comparison, the V-shaped microtexture has the largest oil film carrying capacity and the lowest friction coefficient. The wear mechanism of the V-shaped microtexture is dominated by abrasive and adhesive wear. The V-shaped microtexture has excellent wear resistance under a side length of 300 µm, an interval of 300 µm and a depth of 20 µm.

This study is conductive to the design of wear-resistant surfaces for friction components.

The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives.

The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions.

The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel.

This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

To enhance the performance of hydrostatic bearings, graphene serves as a lubricant additive. Using the high thermal conductivity of graphene, the purpose of this study is to focus on the impact of graphene nano-lubricating oil hydrostatic bearing temperature rise at various speeds and eccentricities.

The thermal conductivity of graphene nano-lubricating oil was calculated by molecular dynamics method and based on the viscosity–temperature effect, the coupled heat transfer finite element model of hydrostatic bearing was established; temperature rise of pure lubricating oil and graphene nano-lubricating oil hydrostatic bearing were analysed at different speed and eccentricity based on computational fluid dynamics method.

With the increase of speed and eccentricity, the temperature rise of 0.2% graphene nano-lubricating oil bearings is lower than that of pure lubricating oil bearings; in addition with the increase of graphene mass fraction, the temperature rise of graphene nano-lubricating oil bearings is always higher than that of pure lubricating oil bearings, and the higher the speed, the more obvious the phenomenon.

The effects of graphene as a lubricant additive on the thermal conductivity of nano-lubricating oil and the variation of the temperature rise of graphene nano-lubricating oil bearings compared to pure lubricating oil bearings were analysed by combining micro and macro methods.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0388

Due to the environmental issues caused by petroleum lubricants used in lubrication, the concept of creating various bio-lubricants requires research globally. Thus, this study aims to develop, characterize and test the base ficus carica oil (fig oil) for its ethylene vinyl acetate copolymer (EVA) and sodium dodecylbenzene sulfonate (SDBS) content.

The sample characterization was done using the Fourier transmission infrared spectrum, whereas the morphologies of the EVA, SDBS particles and lubricated surfaces were carried out under scanning electron microscope equipment. To ensure the homogeneity of the solution (base oil and additives), the formulations were subjected to the sonication process. The anti-friction and anti-wear properties of EVA and SDBS particles as lubricant additives were investigated using a ball on a flat high-frequency reciprocating rig tribo-tester.

According to the findings, the base oil’s anti-friction and anti-wear capabilities can be greatly enhanced by the additions. revealed that the best results were obtained when 1.2% EVA + 2% SDBS was applied for the examination of wear (597.8 µm) and friction coefficient (0.106). Commercial references were used, nevertheless, and the results were excellent. This is because the particles in the contact area during lubrication have strong solubility and quickly penetrate the contact zone. The lubricating mechanisms were explained by a tribological model of the EVA + SDBS and SDBS particles.

The coefficient of friction and wear reduction caused by the use of the additives will certainly enhance system performance and protect the machine components from excessive wear that could cause damage or failure.

The originality and uniqueness of this work are officially affirmed by the authors. The authors’ autonomous and original contribution to the development of sustainable lubrication is represented in this work. To the best of the authors’ knowledge, no other study has been published or made publicly available that duplicates the precise scope and goals of our research, and this conclusion is based on a thorough literature assessment.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

This study aims to investigate the consumer behavior toward telemedicine services in India during the COVID-19 pandemic onset. With lockdown restrictions and safety concerns in visiting brick-and-mortar clinics or hospitals during the pandemic, Telemedicine had emerged as a potent alternative for seeking redressal to health issues. Based on theory and focus interviews with the telemedicine users, the researchers proposed a model to understand the intent and actual usage of telemedicine in India.

The cross-sectional study undertaken used a questionnaire designed on a seven-point Likert scale and administered to respondents with the objective of identifying the determinants of intent and actual usage of telemedicine services. Simple random sampling was used to collect primary data. The data was cleaned and finally a sample of 405 responses complete in all respects was considered for analysis. The questionnaire comprised of 34 items and following the recommendation of Hair et al. (2016), which says the minimum sample size in structural equation modeling should be ten times the number of indicator variables, a sample size of 405 was deemed adequate.

The research paper finds that performance expectancy, attitude, credibility and self-efficacy positively impact the intention of consumers to use telemedicine services. As the effort expectancy or risk perception toward telemedicine increases the intent and actual usage of telemedicine decreases. The intention to use telemedicine emerged as a strong predictor of the actual usage of telemedicine. Intent to use telemedicine was explained 81.4% by its predictors of performance expectancy, effort expectancy, attitude, risk, credibility and self-efficacy, and actual usage was explained 79.9% by its predictors. This study also reports that telemedicine was found to be popular among chronic as well as episodic patients though the preference was skewed in favor of the episodic patients. One of the advantages of telemedicine is its availability round the clock, and the study found that 8 a. m. to 12 noon time slot as the most preferred slot for seeking telemedicine services.

Chang (2004) opined that telemedicine can fulfill the needs of all stakeholders: citizens, health-care consumers, medical doctors and health-care professionals, policymakers, and so on. Considering the promise telemedicine holds, this realm must be studied and leveraged to the full potential. The study found that patients were using telemedicine even for their day-to-day aliments. This indicates a growing popularity of telemedicine and as such an opportunity for telemedicine companies to leverage it. In India, pharmaceutical companies cannot give commercial advertisements for medicines, and the same can only be sold through a registered medical practitioner’s prescription. As such there is total dependency on the medical practitioner for the sale of medicines. Telemedicine companies offer services of home delivering medicines clubbed with medical consultation thus giving them forward integration in their business models. Using telemedicine the patients had control over the timings of the services offered, and as such the waiting time to get a consultation and subsequent treatment was reduced considerably. Best medical advice from across the globe is available to the patient at less cost. Medical practitioners also stand to benefit as they can treat a variety of cases, collaborate among the medical fraternity and give consultation safely in case of fatal contagious diseases.

This study points to a definite growing popularity of telemedicine services not only in episodic patients but also chronic patients. Telemedicine with its unique advantages holds the promise to grow exponentially in the future and is a compelling health-care segment to focus on for delivering health-care solution to the geographically distant consumers.

This paper aims at studying numerically the entropy generation of nanofluid flowing over an inclined sheet in the presence of external magnetic field, heat source/sink, chemical reaction along with slip boundary conditions imposed on an impermeable wall.

A suitable similarity transformation technique has been used to convert the coupled nonlinear partial differential equations to ordinary differential equations (ODEs). The ODEs are then solved simultaneously using the finite difference method implemented through an in-house computer program. The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.

The relative strengths of the irreversibilities due to heat transfer, fluid friction and the mass diffusion arising due to the change in each of the controlling variables have been delineated both in the near-wall and far-away-wall regions, which may be helpful for a better understanding of the thermo-fluid dynamics of nanofluid in boundary layer flows. The numerical results obtained from the present study have also been validated with results published in open literature.

The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.