Arabian Journal for Science and Engineering最新文献

Plasma technology is based on a simple physical principle. When more energy enters the gas, it ionizes and becomes the fourth state of matter, the energy-dense plasma. The studies carried out within the scope of this study were designed to create microchannels on lamellar glass using an improved redesign of the current plasma arc device, which is the main subject of the paper. The created microchannel is examined at the microscale. Experimental analysis was conducted considering the effect of plasma on the effect of microchannel quality. We performed an experimental design study to determine the optimal parameter levels for improving microchannel quality. The predicted results have been validated with the experimental results. An experimental design study provides useful results, such as information about the distance between the probes, pulse duration, and material temperature, which enhances the channel dimensions. The improved device can be utilized effectively to establish microchannel processing in practice.

Lithium-ion (Li-ion) battery cells are used as the major power source for every electric vehicle (EV) industry because of their properties like density and voltage. Their optimal operating temperature ranges between 15 and 45 °C. The charge mobility and chemical reaction in Li-ion batteries cause excessive heat generation leading to thermal runaway and ultimately their capacity diminishes over the life cycle. The main idea of the present study is to control the thermal runaway of the Li-ion batteries using nano-enhanced phase change materials (Ne-PCM). Hence, there is a need for the development of a battery thermal management system (BTMS) using either air, liquid, or phase change material (PCM). An 18650 battery cell (normal capacity: 2700 mAh; rated capacity: 2600 mAh; normal voltage: 3.7 V; rated power: 9.62 Wh; anode material: lithiated graphite (LiC₆); cathode material: lithium-nickel-manganese-cobalt-oxide (LiNiMnCoO₂); electrolyte material: lithium hexafluorophosphate (LiPF₆)) along with a complete battery pack (4 cells) is considered in the present study. Transient numerical simulations (using both MATLAB R2022a and ANSYS 2020 R2) are carried out with and without using the paraffin wax. Three different nanoparticles (copper oxide (CuO), aluminum oxide (Al₂O₃), and titanium oxide (TiO₂)) at various concentrations (0%, 3%, 7%, and 10%) are added to the paraffin wax to enhance their thermal conductivity value. However, the experiments are conducted only with and without using the paraffin wax, but not with the nano-enhanced paraffin wax. Hence, this (Ne-PCM case) acts only as a support to the numerical study. For both the numerical and experimental analysis, the temperature and voltage characteristics of the battery packs are measured for a specific time to understand their charging and discharging characteristics. It is found that paraffin wax is a better candidate for maintaining the battery temperature in an optimal range when the battery generates excess heat. Paraffin wax gives a 41% increase in battery life compared to air cooling. The hybrid cooling (combination of paraffin wax and air) technique reduces the battery temperature rise by 4 °C compared to only paraffin wax and by 8 °C compared to only air cooling.

The increasing manufacturing of tires, driven by the automotive sector, has led to various sustainability and waste management issues. Consequently, it is necessary to explore alternatives for the reuse of these wastes. Rubber has demonstrated properties that could make it an attractive material for use as a granular aggregate. The aim of this study is to evaluate the influence of crumb rubber (CR) on the mechanical behavior of a granular subbase (GSB) by replacing fine CR at 10% and 15%. An environmental analysis of the proposal was conducted using the SimaPro tool, and a series of laboratory tests were performed to assess physical and mechanical characteristics. The strength results indicate a decreasing trend as the CR content in the GSB increases. Specifically, in abrasion tests, the natural material showed an average result of 30.86%, while the mixes with 10% and 15% CR exceeded the maximum limit (50%), achieving results of 59.24% and 53.98%, respectively. In terms of the California Bearing Ratio (CBR), only the natural samples compacted at maximum energy exceeded the minimum requirements for low and medium traffic levels (30%). The highest CBR value for samples containing CR was 5%, whereas the natural GSB reached a maximum CBR of 33%. A similar trend is observed in environmental outcomes, with increases of up to 17% in abiotic depletion for the sample with 15% CR. Overall, the environmental analysis suggests that incorporating CR into the GSB could lead to an increase in various environmental impacts.

Bug reports (BRs) play a major role in the software maintenance process; they alert developers about the bugs discovered by the end-users. Software applications utilize bug tracking systems (BTS) to manage submitted bug reports. Recent studies showed that the majority of BRs in BTS belong to the default severity category, which does not represent their actual severity. In this paper, we propose an approach that can automatically classify default bug reports into severe or non-severe categories. We curated a dataset based on the history of bug report logs. After that, we used the Support Vector Machine algorithm and Term Frequency-Inverse Document Frequency feature extraction method to classify default bug reports into severe or non-severe categories. The results show that building customized models for default severity bug reports provides better and more reliable results than training one model for all severity. Overall, the proposed Log model outperformed the three models (approaches) from the literature; it achieved an improvement of up to ~ 4% f-measure compared to others, and in some projects, it achieved an improvement of 11.2% f-measure. Moreover, we investigated the impact of sentiment analysis on default bug severity prediction; the results show no noticeable influence.

In the realm of lubrication, nanoparticles play a pivotal role in enhancing the tribological efficacy of lubricating oils. Unveiling a critical need, the research underscores the necessity for a predictive model capable of anticipating these performance characteristics. This research endeavors to fill this gap by introducing an artificial neural network (ANN) tailored specifically for predicting the behavior of nanolubricants. The optimized neural network structure, at 5 × 8 × 2, attains a remarkable minimum mean square error of 0.00046667, with R-values hovering at impressive proximity to unity (0.99828). During the confirmation phase, the neural network's predictions demonstrate a deviation of 7.51% (negative) and 2.87% (negative) for COF, alongside 0.50% and 1.80% for WSD, further affirming its predictive capacity in assessing lubricant performance characteristics.

The objective of this experimental study was to investigate the impact of different earth precursors, partially substituted with ground-granulated blast furnace slag (GGBFS), at varying replacement levels of 0–25% with 5% increments, on abrasion resistance, SEM analysis, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) tests after 90 days and compressive strength with dry density test at 28 days curing age. The precursors derived from waste aluminosilicate sources, such as metakaolin (MK), pumice powder (PP), waste ceramic powder (C), and bentonite (B), were utilized to produce GPMs. A total of 21 different combinations from four distinct series were produced. Depending on the results, it was found that all earth materials used had a positive effect on all properties at various replacement ratios. After 28 days, the mix containing 5% B reached its maximum strength of 64.15 MPa. The maximum values for abrasion resistance and compressive strength were obtained when the replacement level was 10% for all precursors, except bentonite, which achieved the best results at a replacement level of 5%. At a 25% replacement level, pumice powder showed superior performance on all properties compared to other precursors. Furthermore, the impact of the replacement level and precursor types was statistically evaluated using the two-way analysis of variance (MINITAB-ANOVA) technique. The statistical study showed that all variables had a substantial impact on the characteristics of the geopolymer mortar. The proposed geopolymer materials possess inherent stability, making them viable and sustainable substitutes for conventional construction materials.

Nanostructured duplex stainless steel (DSS) was prepared using mechanical alloying (MA) and spark plasma sintering (SPS). The ball milling was performed under nitrogen atmosphere. X-ray diffraction (XRD) and scanning electron microscope (SEM) were used to identify the phases and carry out morphological investigations, respectively. XRD spectra revealed that ferrite phase formation dominated during the initial stages and austenite phase emerged after several hours of ball milling. Lattice parameter calculations showed a decrease in lattice parameter up to 5 h and later increased at 10 h of milling. A decrease in crystallite size was observed up to 10 h of milling. SPS was performed in vacuum at an optimized temp. of 1000 °C for a fixed holding time of 10 min, heating rate of 100 °C/min, cooling rate of 200 °C/min and under an applied pressure of 50 MPa. The electrochemical performance of DSS alloys was examined using potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). The results showed that alloy Fe-18Cr-3Mn-1Mo-1Si-0, 22N-5Ni (wt.%), has the highest corrosion resistance among the designed alloys.

The present article investigates the performance of powder-mixed die sink electrical discharge machining (PMEDM) under the influence of micro-flakes graphite (Gr) powder additive in dielectric (1.25 and 2.5 g/l of concentrations) during machining two workpiece materials of D2 steel and mild steel that are extensively employed alloys in the die and mold industry. Micro-flakes are chosen due to the necessity for fewer particles to fill the gap in the machining process, resulting in reduced pollution within the gap. The assessment of the PMEDM procedure’s results is conducted based on several parameters, including the material removal rate (MRR), electrode wear ratio (EWR), average surface roughness (Ra), morphology, machined surface elemental content and recast layer. The results indicated that Gr PMEDM process offers higher machining performance in terms of MRR, EWR and the machined surface quality compared to die sink EDM process in pure dielectric. The optimum concentration was found to be 2.5 g/l Gr powder mixed dielectric for both workpiece materials. Furthermore, it was specified that the chemical composition and mechanical properties of the workpiece considerably affect the performance of die sink EDM and PMEDM. Unlike what has been discussed previously in the literature, present study revealed that formation of micro-crack and its density as well as micro-defects on the machined surface is highly dependent on workpiece elemental composition and mechanical properties. The concentration of cracks was particularly higher in areas with higher Cr atomic concentration, which increased the crack density in D2 steel.