Arabian Journal for Science and Engineering最新文献

The present investigation explores the potential of alkali-activated slag as a novel method for stabilizing and enhancing the mechanical properties of loose sandy soils. To achieve this, unconfined compression tests were performed on samples with varying slag content, activator solution parameters, and curing conditions. A predictive model was developed to estimate UCS based on these factors. The microstructural analyses using field emission scanning electron microscopy and energy-dispersive X-ray spectroscopy elucidated the development of gels contributing to improved mechanical properties of the treated samples. Additionally, UCS tests demonstrated that increased slag content, activator concentration, and curing time significantly increase strength, stiffness, and brittleness. Notably, the findings show that samples treated with alkali-activated slag achieved substantially higher strength than those treated with ordinary Portland cement. These findings highlight the superior efficiency of this method in soil stabilization.

In any machining, process parameters are optimized to obtain a better output with optimum input. Tool flank wear is one of the machinability parameters that significantly affect the cost of production and surface quality. In this study, turning procedure is carried out on Inconel 718, utilizing a micro-hole pattern on the rake face of the insert coupled with tungsten disulfide (WS₂) solid lubricant as coolant. Process parameters are optimized using the Taguchi method to minimize flank wear. The result of optimization revealed that a cutting speed of 140 m/min, feed rate of 0.1 mm/rev, and depth of cut of 1.5 mm were ideal. Tool flank wear is often obtained using microscopic and SEM analysis methods. For this study, image processing steps are used to evaluate flank wear. The image of the patterned insert is the input, and a sequence of operations is carried out with MAT lab to obtain the flank wear. To validate the result, the optimized flank wear value obtained from Image processing is compared through optical microscope and noticed that the error is minimum. The result revealed that image processing is a reliable method to measure tool flank wear accurately and the optimized value using Taguchi leads to effective machining performance.

With the increasing demand for lightweight and high-strength materials, aluminium alloy composites have shown promising properties for structural applications. This work investigates the fabrication of AA6063/Si₃N₄ nanocomposites through the friction stir processing (FSP) route. The study evaluates the effect of the volume fraction of Si₃N₄ on the mechanical properties, fretting wear, and corrosion behaviour of the aluminium composite. The microstructure, distribution of the reinforcement, and phase changes were characterized using field emission scanning electron microscopy images and X-ray diffraction. The results indicate that the FSP route is an effective method for producing AA6063/Si₃N₄ nanocomposites which exhibit improved mechanical properties and wear resistance compared to the base material. Additionally, the corrosion resistance of the composite was found to be enhanced to that of the base material. In comparison with the base material, a significant improvement of 81% in hardness and 47.2% in tensile strength was observed in the 10% Si₃N₄ AMC. These findings demonstrate the potential for using FSP to produce high-performance nanocomposites for various industrial applications.

The use of coiled tubing drilling in drilling operations has the problems of large sliding friction and serious backing pressure. The use of small-sized vortex hydraulic oscillators can effectively improve the drilling pressure transmission efficiency. In this paper, a multi-vortex circumferential hydraulic oscillator model is established to systematically study the influence of structural parameters and vortex number on its working performance under gravity effect. The results show that the oscillation performance of the hydraulic oscillator under gravity effect is highly sensitive to the structural parameters, the number of vortices and other factors. The comparative analysis shows that the oscillation process of the three vortex circumferentially placed oscillator is more stable, the oscillation efficiency is higher, and the oscillation performance is better. The change of the number of vortexes in the multi-vortex hydraulic oscillator will cause the change of the oscillation performance of the hydraulic oscillator. The average pressure drop and the maximum oscillation amplitude of the tool decrease with the increase of the number of vortexes. The research results provide theoretical support for the structural optimization design and application of multi-vortex hydraulic oscillator.

Evacuated U-tube solar collectors (ESC) are highly efficient devices for converting solar energy into heat. In this study, a mathematical model was developed for the dynamic thermal analysis of ESCs designed for low and medium-temperature applications. Carbon dioxide (CO₂), chosen as the working fluid in solar collectors, possesses several superior properties: excellent heat transfer capabilities, non-flammability, non-explosiveness, environmental friendliness, and low critical temperature and pressure. The developed mathematical model was validated against experimental results, demonstrating a deviation of 6.3% between theoretical predictions and experimental measurements. For the dynamic analyses, the seasonal and annual performances of the collector were assessed using meteorological data of Isparta, Türkiye. The calculated CO₂ exit temperatures from the collector on specific dates (15 January, 15 April, 15 July, and 15 October) were 129.19 °C, 149 °C, 205 °C, and 170 °C, respectively. The maximum CO₂ temperature was observed in July and June, whereas the minimum temperature occurred in January and December based on monthly average meteorological data. The analyses indicated that fluid temperatures could reach approximately 250 °C. Furthermore, temporal variations in temperature across the collector's layers were studied.

The study focused on the synthesis of hydrogels using polyvinyl alcohol (PVA) and polyethylene glycol (PEG) as primary components and containing various proportions of carbon nanotubes (CNTs). The findings revealed that the hydrogels containing CNTs exhibited an increase in hydrophobic properties, with the average contact angle increasing from 14.77% (%PVA/PEG/DOX) to 73.73% (%PVA/PEG/CNT/DOX) and the swelling degree decreasing from 95 to 81%. Measurements using FTIR analysis and the observation of a decrease in surface free energy confirmed these findings. In addition, the hydrogels were observed to exhibit bioactivity. Drug release analysis was performed using ultrasound (US) at a frequency of 40 kHz and a temperature of 22 °C for periods of 15, 30, 45, and 60 min, excluding the use of ultrasound. The drug release assays revealed that even with the US effect, drug release decreased by approximately 6.71% as the CNT content increased. The results suggest that CNT-containing hydrogels have the potential to be valuable in drug delivery systems and could be a highly effective approach for drug delivery in the US.

Solenoid valves enable flow and motion control functions in the fluid power systems. Even today, on-line diagnosis of fluid power systems still remains a challenging task due to the computational cost and availability of machine operation data sets. For the prior, rapid fault diagnosis of the solenoid fault is of great economic values to the reduction in downtime maintenance. For the latter, currently the data for training networks are the major obstacles, as some of the rare faults are simply unavailable from the usual maintenance data. Facing the challenges, this paper presents a new way of quantifying the spool stiction severeness, a common fault in the solenoid on–off valves, using a proposed coupled physical model, where only temporal features from the solenoid coil driving current were extracted and applied for rapid diagnosis, without the need of spool displacement information. A test system was constructed in laboratory and different settings of valve spool stiction from normal to completely jammed were realized on the hardware. The developed coupled model is validated experimentally and demonstrates the capabilities in capturing the stiction effects. The quantitative diagnosis model based on temporal feature vectors was also tested and compared to the true stiction level, and the proposed sigmoid weightings have shown high prediction accuracy. The initial results have shown that the proposed model can quantify the spool stiction degree with accuracy at least 90% and with computation time less than 500 ms with a CPU at lower than 1.3 GHz.

The purpose of this study was to bioprospect the volatile organic compounds (VOCs) of various Trichoderma harzianum strains to control black rot of postharvest snake fruit, an important fruit commodity in Southeast Asia, caused by the fungus Thielaviopsis paradoxa. Trough an indirect confrontation assay, T. harzianum InaCC F88 was found as the most suppressing strain among others. The strain inhibited T. paradoxa with growth relative to control (GRC) of 71.14%. A volatolomic analysis using Headspace GC–MS of this strain showed the most abundant VOC was isoamyl alcohol (36.06%), followed by 2-methyl-1-propanol (21.92%) and 2-cyclopentenone (10.72%). Isoamyl alcohol as the major compound inhibited T. paradoxa with GRC of 71.44, 28.88, and 2.86% after the addition of 10, 20, and 30 µL of the vapor of pure compound, respectively. Moreover, in a 1.5-L close-container assay, the addition of 300 µL isoamyl alcohol vapor was also able to reduce lesion tissue in the pre-infected fruit up to 29.15% after 7 days of storage in room temperature compared to 58.97% in the absence of the pure compound. In conclusion, T. harzianum InaCC F88 through its VOCs was potential to biocontrol black rot in snake fruit, thus extend its storage time.

In recent years, with the indepth research on driverless technology, model predictive control theory was extensively applied in the field of vehicle control. In order to improve the accurate tracking of reference trajectories by driverless vehicles, a model predictive control trajectory tracking controller for driverless vehicles optimized by an improved sparrow search algorithm is proposed. Firstly, an objective function with constraints is added to the model predictive control trajectory tracking controller by establishing the vehicle dynamics model; Secondly, the improved sparrow search algorithm is enhanced to speed up convergence and expand the program's search capabilities; Then, in order to discover the best value, the model predictive control trajectory tracking controller's prediction time domain and control time domain are optimized using the improved sparrow search algorithm; Finally, to confirm the method's viability, collaborative simulations in Simulink/Carsim were completed. The simulation results show that the lateral errors generated by the improved sparrow search algorithm-based optimized model predictive control trajectory tracking controller are reduced by 53.53% and 65.44%, respectively, when the vehicle speed is 36 km/h, compared with the traditional model predictive control trajectory tracking controller. When the vehicle speed is 54 km/h, the lateral deviations are reduced by 81.08% and 86.76%, respectively. In addition, the optimized model predictive control trajectory tracking controller improves the accuracy and at the same time, the driving stability of the control vehicle is significantly improved.

Turn-over intention recognition of patient is crucial for the advancement of the intelligent nursing field. In this paper, a novel turn-over intention method is proposed based on array air spring mattress. For this method, the turn-over intention of a lying patient can be recognized by identifying the internal pressure distribution of array air springs. To begin with, the samples of turn-over intention are created experimentally, and then input into a model combining Variational Auto-Encoder and Generative Adversarial Network for the sample augmentation to address issues related to low accuracy and poor generalization caused by sample imbalance. Besides, the augmented dataset is conveyed into the Convolutional Neural Network model, for the detection of three states: left/right turn-over intentions and no intention. The research demonstrates that, the similarity of the left and right turn-over intention samples generated by VAE-GAN model is 90.13% and 91.01%, respectively. This increases the diversity of samples and is helpful for intention recognition. The recognition accuracy of the CNN model with sample augmentation is 98.04%, which is 13.4% higher than without sample augmentation. The proposed method is effective to turn-over intention recognition, by identifying the internal pressure distribution of array air spring mattress. The efficiency of intelligent nursing systems can be substantially improved, thus ensuring better patient care and safety.