Arabian Journal for Science and Engineering最新文献

Concrete-filled steel tube (CFST) as the high-tech composite members utilized as a main load-carrying element in high-rise buildings’ construction. CFST element load capacity is considered one of the most crucial and challenging engineering parameters for designing columns structurally and economically for steel–concrete composite. Because of the designing complexity of theoretically simulation and serviceability limits, this paper attempted to overcome the engineering problem using a machine learning (ML) methods. To do so, numerous efficient ML modeling called multivariate adaptive regression spline (MARS), M5p model tree (M5p), extreme learning machine (ELM), and random forest (RF) are implemented to propose a new auto-estimated and interpretable model. Through extensive literature, including 1305 (circular column) and 1003 (rectangular column) subjected to concentric axial force, data-intelligence models are developed. The developed models were compared with corresponding values computed by design code provisions, including Eurocode 4, LRFD, AISC 360–16, AS5100, ACI 318–14, and empirical equations extracted. The statistical metrics present that the proposed MARS models (r = 0.990, RMSE = 361.32 KN, WI = 0.995, and PMARE = 14.078% (circular column)) and (r = 0.974, RMSE = 494.94 KN, WI = 0.984, and PMARE = 11.238% (rectangular column)) boosted the performance of the simulation of the CFTS column compare to other models and design codes. In addition, global sensitivity analysis was performed using SOBOL methods to evaluate effective parameters. The explicit simulation model of the CFST columns is satisfied with the parametric study and shows the ability to perform the modeling and the cost-effective benefits of the information approach.

Motor current signature analysis (MCSA) offers a non-invasive approach to early detect different faults in squirrel-cage induction motors (SCIMs). Every fault normally adds some specific harmonics to the motor current and the MCSA typically proposes the fault diagnosis by detecting these harmonics. Using the rotor–stator mutual-inductance curve, this paper proposes an analytical approach to determine broad sets of harmonics that are presenting in the healthy SCIM current or are adding to the current by broken rotor bar (BRB) fault, mixed eccentricity (ME) fault and combined BRB-ME fault. The broad harmonic sets are attained due to applying exact form of the inverse of the air gap function, using exact form of the stator and rotor turn functions and taking every integer harmonic of the stator current into account. The extensive harmonic sets give higher degrees of freedom to attain the most appropriate harmonics to establish fault diagnosis techniques. Further study shows that many BRB-related harmonics are also present in the healthy state with lower amplitudes and that the ME fault magnifies some well-known BRB-related harmonics as well as the 3rd harmonic. In addition, the combined BRB-ME fault produces harmonics that are sidebands around the harmonics produced by the single ME or BRB fault. Simulation results based on the finite elements method and corresponding experimental test results confirm the analytically achieved results.

In this paper, a single-switch hybrid dual diode-capacitor (HDDC) boost converter with less stress over all devices for high voltage gain applications is proposed. It combines a voltage boost cell with two back-to-back diode-capacitor cells for providing high voltage gain. The current spikes across the switching devices, occurring due to the diode-capacitor circuit, are effectively truncated by an inductor that is used at the input side. With a single inductor and a single MOSFET, the proposed HDDC converter provides continuous input current, a common ground (C.g) structure and keeps the device voltage stress (V(_textrm{stress})) and current stress under check. This allows the use of lower-rating devices and is helpful in restricting switching losses, thus improving the comprehensive efficiency of the converter. For integrating RES with micro-grid, the proposed HDDC converter provides all the desirable features. A MATLAB/Simulink model is employed for testing purposes of the proposed HDDC. Additionally, a hardware prototype of the HDDC, with a power rating of 280 W and voltage output of 200 V, is subjected to laboratory testing at a frequency of 33 kHz. The findings from both the simulation and hardware testing are then compared to validate the performance of the proposed HDDC. At near-rated load, the converter operates at an efficiency of around 95.4%.

The THz spectrum excited by an external electron beam is studied in semiconductor systems. The beam electrons interact with the medium particles to excite a wave at the cyclotron frequency. The dispersion relation of the THz spectrum is obtained by employing the quantum magneto-hydrodynamic (QMHD) model for the semiconductor species, which includes quantum features like Landau quantization of Fermi statistical pressure. It is noticed that the dispersion relation verifies the excitation of THz electron cyclotron waves (ECWs) at a typical set of real-time parameters of GaAs semiconductor plasmas. The features of the THz spectrum vary with varying angles of propagation (theta ) that exist between the wave vector k of the spectrum and the ambient magnetic field (B_0), the streaming speed of the electron beam (u_{0}) directed into the plasma system parallel to wave vector k, the thermal effects of beam electrons, and the gyro frequency dependent on (B_0) rooted in the expression of Landau quantization. As for the application, this study is helpful to understand semiconductor device technology. The semiconductors are used to generate the THz range by continuous waves or pulse waves [1] although they face a lot of technical difficulties in the laboratory [2]. A theoretical model is presented here for the excitation of continuous plasma waves [3], employing the data of GaAS semiconductors for the THz range [4]. The authors believe that this study may increase our theoretical understanding to meet the growing demand for THz bandwidth for experimental purposes.

Heat transfer capabilities of the heat exchangers require enhancements to save energy and decrease their size. For this purpose, the swirl generators have been widely preferred. However, the swirler inserts have not reached their optimum shape. Thus, this study experimentally and numerically investigates the impact of novel 3D-printed swirler inserts with varying twist angles in the range of 0°–450° on the thermo-hydraulic performance of solar absorber tube heat exchangers under laminar flow (Re = 513–2054) condition. Friction factor, Nusselt number, and performance evaluation criterion (PEC) were used to assess heat exchanger performance, and related correlations are provided. Tangential velocity components were also used to explore fluid flow characteristics in local analysis. Numerical investigation was done by using computational fluid dynamics adopting Finite Volume Method in ANSYS Fluent. Results show that 3D-printed swirlers considerably increase heat transfer compared to plain tube. The swirler with a twist angle of 450° led to the maximum enhancements of nearly 217% in average Nusselt number and around 1630% in friction factor at Reynolds number of 2054. Overall, increasing Reynolds number enhanced Nusselt number. The highest PEC of 1.15 was observed at a Reynolds number of 1031 using the swirler with 150° twist angle. Flow near the swirler has higher tangential velocities, hence contributing to local Nusselt number enhancement up to 453.8% compared to plain tube when swirler with twist angle of 450° utilized. It is anticipated that findings of this study can guide further related research and increase the usage of swirlers in heat exchangers.

A complex and fascinating field of study with a wide range of applications can be created by combining natural convection, hybrid nanofluid, thermal radiation, magnetohydrodynamics (MHD), Laplace transform, heat generation/absorption, and forward/backward moving vertical plates. The novelty of this research lies in its comprehensive exploration of heat transport in a specific hybrid nanofluid with a focus on multi-physics interactions, plate motion scenarios, and the application of mathematical techniques. These factors make the study a valuable contribution to heat transfer and fluid dynamics, with potential applications in various engineering and industrial settings. Therefore this work deals with the analysis of an unsteady, electrically conducting, water-based hybrid nanofluid across a forward and backward moving vertical porous plate. Using Laplace transform techniques, the temperature and velocity distributions of a hybrid nanofluid ((hbox {Cu})–(hbox {Al}_2hbox {O}_3)–(hbox {H}_2hbox {O})) at different fluid parameters, such as MHD, radiation, porosity, and the heat generation/absorption values at the moment of the plate moving forward and backward, are obtained. The brightly overlaid graphical representation conveys the velocity and temperature distributions.

Buckling-restrained braced frames (BRBFs) present a kind of lateral bracing system characterized by their remarkable high-energy dissipation capacity. This study focuses on two BRBFs within 2- and 6-story structures. The frames are meticulously modeled within the OpenSees software. The investigation employs the multi-objective particle swarm optimization (MOPSO) algorithm to ascertain the optimal stiffness modification factor for the braces. This factor is influenced by diverse aspects, including brace length and cross-sectional area—key components in synthesizing the brace structure. The objective of brace optimization lies in minimizing building repair time and cost, necessitating a comprehensive risk assessment. Throughout the optimization procedure, performance evaluation is conducted using the methodology outlined in FEMA P-58. Each optimization stage involves an analysis of the braces utilizing Incremental Dynamic Analysis (IDA) across 22 earthquake records to assess their performance. The optimization outcomes unveil a distinct trend: for a 2-story building, lower values of the stiffness modification factor engender an optimal risk profile concerning repair time and cost. Conversely, a 6-story building tends toward higher values of the stiffness modification factor to achieve an optimal balance between repair time and cost.

Acoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

Diabetic retinopathy (DR) stands as the most prevalent diabetic eye ailment and constitutes one of the primary causes of blindness worldwide. Detecting and classifying retinal images can be laborious and demands specialized expertise. In this study, a convolutional neural network (CNN) was trained using stained retinal fundus images to identify DR and categorize its stages. The deep learning models chosen for this research encompassed InceptionResnetV2, VGG16, VGG19, DenseNet121, MobileNetV2, and EfficientNet2L. To enhance the resilience of the models and mitigate overfitting issues, data augmentation approaches were implemented. Each network underwent two levels of training. The initial level involved a feature extraction network with a customized classifier head, followed by fine-tuning the resulting network from the previous step through the unfreezing of certain layers. The efficacy of the proposed strategy was assessed through qualitative and quantitative evaluations using Kaggle’s diabetic retinopathy detection dataset. The obtained results demonstrated that our proposed methods, particularly those based on the refined InceptionResnetV2, achieved exceptional accuracy values, reaching 96.61%.

The WNiFeCo, WNiFeMo, and WNiFeCoMo compositional complex alloys (CCAs) were prepared by powder metallurgy technique. The thermodynamic investigations of the CCAs proved that WNiFeCo, and WNiFeMo, are medium entropy alloys (MEAs), whereas WNiFeCoMo is a high entropy alloy (HEA). The density of the prepared specimens was estimated. The sintered CCAs were characterized by investigating their microstructures and elemental distribution using SEM and EDX analysis. The crystal structure of the different phases was identified utilizing X-ray diffraction (XRD). From XRD results, W, Fe₇W_6, and FeNi were observed in all CCAs, whereas Co₇W₆, MoNi₄, and Co₇Mo₆ phases were found in WNiFeCoMo HEA. WNiFeCo MEA contained a Co₇W₆ phase, while the MoNi₄ phase was observed in WNiFeCo MEA. The A₇B₆ phases are formed in the CCAs which have good characteristics. The hardness, Young’s modulus, and corrosion behavior were evaluated. Among the investigated CCAs, WNiFeMo MEA showed the highest relative density percentage (95%), Young’s modulus (190 GPa), hardness (451 HV), and lowest corrosion rate in 3.5% NaCl (0.22 mm/y). The surface morphology of the WNiFeCo, WNiFeMo, and WNiFeCoMo alloys displayed uniform corrosion, galvanic corrosion, and localized corrosion.