Arabian Journal for Science and Engineering最新文献

Tube dimpling is known as an innovative approach, which has greatly helped to enhance the thermal performance of ordinary tubes. Although dimples mostly increase the thermal performance of ordinary tubes, they likely increase the fluid flow pressure drop and cause a reduction in its hydraulic performance. The primary goal of the present work is to introduce a new egg-shaped profile, which can considerably improve the hydrothermal efficiencies compared with the hydrothermal performances of the past extended dimple tubes, e.g., the spherical, conical, and elliptical ones. The present research is carried out using the numerical simulation approach; however, the experimental data is used to validate the numerical results. After demonstrating the accuracy of the employed numerical method, the performance evaluation criterion (PEC) parameter is used to evaluate the hydrothermal performance of the present dimpled-tubes equipped with the new egg-shaped dimple profiles. The present calculated PEC values are then compared with those of similar dimpled-tubes; however, equipped with the previously developed dimple profiles. The data conclusion is that the dimpled-tube with the present egg-shaped profile will provide at least 12.5% greater thermal performance than that of the previously developed dimpled-tube with the best practicing dimple profile. One of the key findings of this study is that the PEC value of the egg-shaped dimpled tube is double that of the corresponding smooth tube. This improvement is largely attributed to a higher turbulence mixing phenomenon caused by the egg-shaped dimples.

Detecting violence is important for preserving security and reducing crime against humans, animals, and properties. Deep learning algorithms have shown potential for detecting violent acts. Further, the reach of large and diverse datasets is critical for training and testing these algorithms. In this study, the aim is to detect violence in images using deep learning techniques to enhance safety and security measures in various applications. For that, we adopted the most utilized and accurate models, such as sequential CNN, MobileNetV2, and VGG-16 which are well known in this field to measure the performance for each classification model on a large dataset of annotated images of eight classes containing both violent and non-violent content. The techniques like data augmentation, transfer learning, and fine-tuning are utilized to improve model performance. As a result, the VGG-16 model has achieved a 71% test accuracy that outperform than Sequential CNN and MobileNetV2 with suitable hyperparameters showcasing its potential for integration into surveillance systems, social media monitoring tools, and other security applications.

This paper presents a statistical analysis and modeling of the thermophysical properties of ZnO-MWCNT/EG-water hybrid nanofluid using three artificial intelligence models, including multilayer perceptron neural network, radial basis function neural networks, and least square support vector machine (LSSVM). The thermal conductivity of the nanofluid was modeled using experimental data, and statistical parameters such as R-squared (R²), average absolute relative deviation (AARD %), root mean squared error, and standard deviation were employed to investigate the accuracy of the proposed models. The R² values of 0.9926, 0.9951, and 0.9866 and AARD% values of 0.4996%, 0.3532%, and 0.6013% show the accuracy of the models for respective MLP, RBF, and LSSVM models. Among these models, the RBF model shows the best accuracy. The study demonstrates the potential of artificial intelligence methods in predicting the thermophysical properties of nanofluids, which can help minimize experimental time and cost for future work.

Caring for elderly individuals, particularly those residing alone, is pivotal for cultivating a compassionate and inclusive society. The ageing population grapples with various challenges, necessitating additional support. A comprehensive and culturally sensitive dataset focusing on elderly individuals within Muslim communities is developed, contributing to the field of Activity of Daily Living (ADL) and fall detection. Utilising low-cost, lightweight wearable technology, the focus centres on inertial-based data for Activity of ADL classification and fall detection as a crucial research area. A culturally diverse dataset comprising 16 classes, specifically tailored for ADLs and fall detection during Muslim prayer movements, is gathered from a self-developed wearable device equipped with dual inertial measurement units (IMUs) on the waist and thigh, ensuring dependable and synchronised information. A Convolutional Neural Network (CNN) classification model is employed and rigorously tested for its effectiveness, revealing high performance with an average accuracy of 98.974% owing to the synchronised acquisition of data from the two IMUs. The acquired CNN model is adapted for deployment on a wearable embedded system, and authentic experiments are conducted, yielding precise outcomes. The results underscore the potential of wearable technology and advanced machine learning in enhancing elderly support and fall detection systems, fostering a safer and more empathetic environment for our ageing population.

This study investigated the behavior of deep beams with openings that have been reinforced with GFRP and steel bars. A total of 14 reinforced concrete deep beams having a rectangular cross-section of 150 × 500 mm and a total length of 1600 mm were constructed with or without openings and tested up to failure under a four-point bending test. The parameters studied were the opening diameter (140 and 240 mm), number and location of the openings and the shear span-to-depth ratio (a/d). These beams were divided into Group I (a/d = 0.9) and Group II (a/d = 0.5). In each group, one beam had no opening to serve as the control beam. Two beams had one opening in the shear area, two had one at the mid-span of the beam and two had two openings, one on each side of the beam. Finite element modeling with strong correlation with the laboratory results was performed. The results showed that an increase in a/d caused a decrease in the final strength of the beam. The number of openings and their locations on the load transfer path were factors that significantly reduced the ultimate load borne by the beam. Comparison of the test results with the relations provided in design regulations indicated that the ultimate strengths of the beams were higher than the values obtained from the regulations. On average, the values calculated based on ACI 318–19 and Canadian S806-2012 were 86.95 and 55.55% lower than the test results, respectively.

Sensitivity analysis (SA) methods determine and quantify how different values of dependent or independent variables affect an output under specific circumstances, such as those represented by a surrogate model. Put differently, sensitivity analyses explore how various sources of uncertainty within a mathematical model collectively impact the model’s overall uncertainty. This study addresses the influence of different parameters—namely, the W/C ratio, CNT type, CNT content, CNT length, CNT diameter, S/C ratio, dispersion method, curing days, and the compressive strength of the control sample (C0) on the compressive strength of carbon nanotube (CNT)-reinforced cementitious nanocomposites as an output. This is achieved by applying four sensitivity analysis methods, including correlation-based indices, Cotter indices, Morris indices, and Borgonovo indices. To implement these four methodologies, a Genetic Programming-based function-finding algorithm known as Gene Expression Programming (GEP) is developed. This algorithm utilizes a collected dataset comprising 326 experimental data points obtained from a comprehensive campaign. Based on the results of the four sensitivity analysis methods, the W/C ratio and the length of CNTs are identified as the most influential input variables across all methods, with CNT type identified in three methods and CNT content in two methods as significant factors affecting compressive strength. Consequently, the W/C ratio, length of CNTs, CNT type, and CNT content are highlighted as the most impactful parameters on the compressive strength of CNT-reinforced cementitious nanocomposites.

Nanocomposites consisting of graphene oxide (GO) and Fe₃O₄ nanoparticles are significant structures due to their high absorption and magnetic properties, and they are promising materials for various application areas from drug delivery to dye removal. In this study, the effect of adding iron salts with different Fe²⁺/Fe³⁺ ratios during the synthesis of magnetic graphene oxide (MNGO) nanocomposites on size distribution, magnetic properties, morphology, and adsorption–desorption behavior was investigated. Characterization results indicated that superparamagnetic iron oxide nanoparticles (SPIONs) were successfully integrated into MNGO nanocomposites, and the surface area increased when SPIONs were synthesized on GO significantly, especially with increasing Fe²⁺/Fe³⁺ ratio. MNGO nanocomposites were tested for the removal of methylene blue (MB). Moreover, the effects of initial pH, dye concentration, and temperature on the adsorption process of MB were also studied. As a result, it is shown that the Fe²⁺/Fe³⁺ ratio has a crucial effect on the adsorption–desorption behavior of MNGO nanocomposites, which are promising nanomaterials for dye removal studies.

The production of high-purity silica from natural sand is crucial as it is a primary material in the high-grade silicon industry. This paper presents a new processing method for purifying sand silica. This process is a subsequent combination of annealing thermal and acid etching. First, samples of Algerian Sahara sand were subjected to rapid thermal annealing in an infrared furnace at 900 °C for 1 h. Subsequently, the samples were etched using an aqueous solution containing hydrofluoric and hydrochloric acid. This last acid etching step is designed to eliminate any gettered impurities that may have been migrated during the annealing process. Various characterization techniques were employed to evaluate the effectiveness of this impurity removal process, such as X-ray fluorescence (XRF) analysis and scanning electron microscopy (SEM) with energy-dispersive X-ray (EDX) spectroscopy. The results show a substantial reduction in all metallic impurities in silica after two successive purification cycles, improving the purity from 94.63 to 99.87% and the removal efficiencies for critical eleven contaminants such as Fe (93.92%), Al (98.41%), and Ca (> 99.99%).

In recent decades, the challenges of traditional visual inspection methods after catastrophic events, which are time- and money-consuming, have necessitated innovative approaches. As a result, a seismic-induced damage detection method utilizing deep learning has been developed to overcome the limitations of conventional techniques. Structure health monitoring (SHM) has emerged to address the limitations of the traditional methods of visual inspections, and among the most effective automatic feature extractor methods is Deep Learning Neural Networks (DLNNs). The DLNN method has proven highly effective compared to other methods, such as traditional methods used in damage detection when used as a feature extractor for seismic-induced damage detection. This study proposes a novel deep learning-based damage detection method for automatically extracting damage features from time series data, eliminating the need for intermediate preprocessing tools. The CNNs algorithm attains a validation accuracy of 91% when applied to a 7-story frame structure by subjecting the structures to different sets of incremental dynamic loading. The study investigates real-time applications, including environmental variables such as noise and temperature effects, examining unseen datasets of different earthquake groups and validating multiple structures in synthesis datasets. The algorithm is further investigated using the IASC-ASCE Benchmark experimental dataset conducted at the University of British Columbia laboratory. A comparative analysis is also performed in terms of time and performance on different deep learning algorithms, such as LSTM, 1D CNN, 2D CNNs and DNNs, while the 1D-CNNs showed the best performance. The results reveal that the proposed method effectively quantifies damage in different structures, including 7-story story steel and concrete structures, and the IASC-ASCE Benchmark dataset, with 93% validation accuracy. The study investigates different earthquake characteristics that affect deep learning performance, such as earthquake time step, and duration, while a specific group was examined to strengthen the claim and show 94% validation accuracy.

This paper presents an innovative approach to control upper-limb rehabilitation robots for both passive and active-assistive rehabilitation therapy. In contrast to conventional model-based impedance control strategies, which may compromise controller stability and robustness due to model uncertainties, unmodeled dynamics, and external disturbances, our proposed model-free impedance control (MFIC) strategy eliminates the requirement for prior knowledge about the controlled system dynamics. MFIC is achieved by incorporating model-free control into conventional impedance control, employing time delay estimation (TDE) to estimate unknown dynamics. Numerical simulations confirm that MFIC outperforms traditional impedance control in terms of tracking performance and robustness. Furthermore, model-free variable impedance control (MFVIC) is introduced by enhancing MFIC with online impedance parameters adaptation using fuzzy logic control. The desired impedance model adapts according to motion and contact torque measurements. MFVIC employs two fuzzy systems to adjust the desired impedance model for two stages of rehabilitation: passive and active-assistive rehabilitation training. Our controller is designed for n degrees-of-freedom (DOF) robots and has been tested on a two-DOF robot model for simplicity.