Journal of Engineering
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Acceptance rate10%
Submission to final decision100 days
Acceptance to publication17 days
CiteScore3.600
Journal Citation Indicator0.430
Impact Factor2.7

Effects of Heat Transfer Characteristics of R32 and R1234yf with Al2O3 Nanoparticle through U-Bend Tube Evaporator

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Journal of Engineering publishes research in several areas of engineering, including chemical engineering, civil engineering, computer engineering, electrical engineering, industrial engineering and mechanical engineering.

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Chief Editor, Professor Wang, is the Vice Deputy Dean of the School of Aerospace Engineering at Tsinghua University.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Investigation of the Combined Effects of Ultrasonic Vibration-Assisted Machining and Minimum Quantity Lubrication on Al7075-T6

The aluminum alloy Al7075-T6 finds extensive application in the aviation and automotive industries, where machining plays a pivotal role. Emerging techniques such as Ultrasonic Vibration-Assisted Machining (UVAM) and Minimum Quantity Lubrication (MQL) hold promise for enhancing machining efficiency. In this study, the combined use of UVAM and MQL for slot milling of Al7075-T6 was investigated. The results demonstrate that UVAM reduced cutting forces by an average of 10.87% in MQL and 8.31% in Conventional Cutting Fluid (CCF) conditions when compared to Conventional Machining (CM). In addition, UVAM yielded significantly improved surface finishes, characterized by an average reduction in surface roughness of 41.86% in MQL and 32.11% in CCF conditions relative to CM. Furthermore, surfaces subjected to UVAM exhibited fewer instances of burn marks and tool-induced markings, reduced chip splashing, and more uniform surface integrity compared to those manufactured with CM. Lastly, chips generated through UVAM exhibited distinct characteristics, notably shorter length, curvier shape, and a distinctive half-turn morphology when compared with the irregular chips produced through CM. In conclusion, our findings underscore the potential of UVAM in synergy with MQL to augment the machining of Al7075-T6 alloy, thereby yielding superior-quality machined components with enhanced operational efficiency.

Research Article

Effects of Front Total Toe-In Angle on Tire Wear and Emissions for a Light-Duty Vehicle

An experimental investigation is carried out in this study to investigate the effect of wheel alignment, particularly the front total toe-in angle, on tire wear and emissions for a light-duty vehicle. Such investigations reveal that there is a substantial correlation among rolling resistance, energy consumption, tire wear, tire travel life, and the total toe-in angle of the front wheel. It is observed that the rate of loss in tire travel life with regard to a condition without misalignment is up to 98.33% when the front total toe-in angle is out of alignment (ranging from 0.00° to 4.20°). It is found that rolling resistance increases by about 128.86%, while CO2, CO, and NOx emissions rise by nearly 36.67%, 26.83%, and 31.25%, respectively, as the front total toe-in angle increases from 0.00° to 4.20°. The experimental results also reveal that tire circumferential groove wear is observed at 0.04 mm after the vehicle’s travelling distance of 500 km, where the front total toe-in angle is 0.00°, and the tire travelling life is 92250 km. In addition, the tire circumferential groove wear is investigated as 2.40 mm after the vehicle’s travelling distance and tire travel life are recorded to be 3,500 km and 1537.50 km, respectively, due to the occurrence of misalignment (the front total toe-in angle is 4.20°). Finally, a regression model is proposed using the test data. Such a model would be useful to explain the relationship between the related factors and determine the rate of tire wear and emissions. It is noteworthy that the wheels should always remain aligned in accordance with the manufacturer’s specifications in order to ensure optimal performance and longevity of the tires

Research Article

CFD Simulation of an Industrial Dust Cyclone Separator: A Comparison with Empirical Models: The Influence of the Inlet Velocity and the Particle Size on Performance Factors in Situation of High Concentration of Particles

The present work is dedicated to the study of multiphase turbulent and three-dimensional rotational flow in dust cyclones, a contribution to air pollution control. Cyclones are widely used devices for the separation of constituents from solid-gas mixtures in industry. In order to improve the filtration efficiency of cyclones, and to reduce the pressure drop, parametric numerical simulation studies using the Fluent code have been conducted to characterise the effects of the parameters affecting the operation of these devices through their performance indicators. In this work, the effect of inlet velocity and the particle size on the turbulent flow air in the cyclone is presented. Numerical simulation of the flow by Fluent code using three numerical models: the first based on the dissipation of kinetic energy by viscosity (RNG) K-epsilon and standard K-epsilon as well as the last based on the solution of Reynolds stress equations (RSM), combined with the multiphase mixing model, gave interesting results in terms of the pressure and flow field in the separator, the variation of inlet velocity, and the variation of particle size. Validation with experimental and empirical results showed the advantage of the Reynolds stress turbulence model (RSM) over the standard K-epsilon and RNG K-epsilon. The RSM model better captures physical phenomena in an intense vortex flow in the presence of walls. But it is characterised by a very long calculation time and requires large machine resources. An alternative to this model is RNG K-epsilon model, which offers a reasonable calculation time with acceptable results (maximum deviation of 5 ) for speed values below 10 m/s. In the absence of numerical resources, certain empirical models such as those of First (for the evaluation of pressure drop) and Iozia and Leith (for evaluation of efficiency) may well be useful for the dimensioning of the cyclone.

Review Article

Hydrogen as Fuel for ICEs: State of Art and Technological Challenges

The climate change, as the main consequence of the polluting emissions due to anthropic activities, is nowadays a well-known threat to human health as well as for the environment safety. Several plans and strategies have been announced by the governments to detach from fossil fuel-based energy and gradually reduce the carbon footprint of their economies. In this scenario, the road transport sector is turning out to be the main “technological gym” to test and improve new powertrain solutions to achieve as soon as possible the goal of the net-zero carbon emissions, at least in the tank-to-wheel (TTW) context. In view of this, the hydrogen as fuel is gaining ever more attention from the scientific community, both as the middle-term solution to achieve the abovementioned goal and as a prominent future energy carrier. In this review, the performance and main characteristics of the hydrogen-internal combustion engines (H2-ICEs) are discussed based on the most recent studies available in literature. A comprehensive overview of various topics is offered, from the production stage to the combustion anomalies, mixture formation strategies, and the challenge of reducing the nitrogen oxides (NOx) emissions under high load conditions.

Research Article

Assessment of Effects of Turbidity Variation on Water Temperature and Evaporation of Gilgel Gibe I Reservoir, Omo-Gibe River Basin, Ethiopia

Turbidity has a significant impact on reservoir water by raising the temperature and evaporation rates. This study provided clear and concise information about the effects of turbidity alteration on reservoir water. The main objective of this study was to assess the effects of turbidity variation on reservoir water temperature and evaporation. To determine these effects, the samples were taken from the reservoir by stratifying it randomly along the reservoir course. To evaluate the relationship between turbidity and water temperature and also to measure the vertical alteration of water temperature, ten pools were burrowed, and they were filled with turbid water. Two class A pans were installed in the field to determine the effect of turbidity on reservoir evaporation. The data were analyzed using SPSS software and MS Excel. The results depicted that turbidity has a direct, solid positive relationship with water temperature at 9:00 and 13:00 and a vigorous negative relationship at 17:00, and water temperature decreased vertically from the top to the bottom layer. There was a greater extinction of sunlight in most turbid water. The differences in water temperature between the top and bottom layers were 9.78°C and 1.53°C for most and least turbid water at 13:00 observation hour, respectively. Turbidity has a direct and strong positive relationship with reservoir evaporation. The relation was analyzed using Spearman’s ranked correlation coefficient, and the vertical alteration of water temperatures was analyzed using a box and whisker plot. The tested results were statistically significant. The study concluded that an increment in reservoir turbidity immensely heightens both reservoir water temperature and evaporation.

Research Article

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

Journal of Engineering
 Journal metrics
See full report
Acceptance rate10%
Submission to final decision100 days
Acceptance to publication17 days
CiteScore3.600
Journal Citation Indicator0.430
Impact Factor2.7
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