Arabian Journal for Science and Engineering最新文献

Thermal energy harvesting and its applications significantly rely on thermal energy storage (TES) materials. Critical factors include the material’s ability to store and release heat with minimal temperature differences, the range of temperatures covered, and repetitive sensitivity. The short duration of heat storage limits the effectiveness of TES. Phase change materials (PCMs) are a current global research focus due to their desirable thermal properties, which improve energy performance and thermal comfort. PCMs require relatively less synthesis effort while maintaining high efficiency and enhancing cost-effectiveness. However, limited temperature range and storage capacity restrict the application of conventional PCMs. Consequently, the demand for high-energy PCM storage with enhanced thermo-physical properties is high. It is essential to explore the potential of new PCMs to improve thermal storage performance and capacity while reducing energy consumption. This review article explores the classifications and applications of PCMs, addresses the challenges in enhancing their thermo-physical properties, and outlines the selection criteria for high-heat storage applications. Additionally, it provides an in-depth analysis of recent research and developments related to PCMs.

This review paper examines the advancements in solid-state power amplifiers (SSPAs) for wireless communication technology. As mobile devices rely on efficient power amplifiers to maintain battery life and ensure clear signal transmission, fabrication technologies like complementary metal–oxide–semiconductor (CMOS) and gallium nitride (GaN) are revolutionizing power amplifier (PA) design. The choice of material depends on the working frequency, with gallium arsenide (GaAs) and GaN suitable for frequencies under 100 GHz, and indium phosphide reaching up to 500 GHz. However, cost is a crucial factor in industrial manufacturing, making CMOS technology advantageous for on-chip system integration. Millimeter-wave chips have different requirements based on their application scenarios. In the Ka-band (26.5–40 GHz), high-power GaN and GaAs chips are preferred for satellite and long-distance communication. In contrast, the 60 GHz band is suited for short-distance high-speed communication and consumer electronics, making lower-cost CMOS and germanium silicon devices the preferred choice. This paper explores critical design considerations for SSPAs, focusing on common structures like envelope tracking, Doherty amplifiers, envelope elimination and restoration, and various linearization methods. We provide a clear comparison of their strengths and weaknesses to empower readers to select the optimal SSPA structure for their needs. Our review aims to facilitate informed decisions in the development of efficient and cost-effective SSPAs for advancing wireless communication technology.

The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO₂ emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem.

The rotor of the permanent magnet synchronous motor develops localized high temperatures at high-torque or high-speed operating conditions so that the demagnetization failure phenomenon may occur. To address this problem, a rotor temperature prediction model based on long-and-short-term memory (LSTM) neural networks is proposed. In addition, the effects of several hyperparameters on the network construction are investigated. To better improve the accuracy of prediction results, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used to optimize the construction of the network parameters. The results of the study show that the LSTM model has a large error throughout the process, which ranges from − 2.66–6.64 °C. GA-LSTM has an error of − 1.71 ~ 3.91 ℃ throughout the process. The error of PSO-LSTM is − 1.78 ~ 0.96 ℃. Additionally, the proposed PSO-LSTM prediction model exhibits good accuracy and stability with RMSE of 0.7114 and MAPE of 1.22%.

Long-term accurate forecasting of the various sources for the electric energy production is challenging due to unmodelled dynamics and unexpected uncertainties. This paper develops non-parametric source models with higher-order polynomial bases to forecast the 16 sources utilized for the electric energy production. These models are optimized with the modified iterative neural networks and batch least squares, and their prediction performances are compared. In addition, for the first time in the literature, this paper quantifies the unseen uncertainties like the drought years and watery years affecting especially the hydropower and natural gas-based electric energy productions. These uncertainties are incorporated into the parametric imported-local source models whose unknown parameters are optimized with a modified constrained particle swarm optimization algorithm. These models are trained by using the real data for Türkiye, and the results are analysed extensively. Finally, 10 years ahead estimates of the 16 imported-local sources for the energy production have been obtained with the developed models.

Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes.

Physiological signals commonly suffer from contamination by various types of noise, ones of the most foremost are baseline wander (BLW) and power line interference (PLI). The removal of these interferences is a crucial in biomedical signal processing and diseases diagnosis. This paper introduces a digital fractional notch filter derived from the corresponding anti-notch one and specifically designed for the removal of BLW and PLI from physiological signals. The salient feature of the proposed filter is its capability to eliminate any frequency range only by adjusting the single parameter ν which defines the central frequency of the anti-notch filter. The performance of the filter is closely linked to the fractional order α and the number of samples L used in approximating the ideal fractional filter. Genetic algorithms were employed to determine the optimal values for these parameters (α, L). The proposed filter has been implemented on noisy ECG, EEG, and EMG signals, exhibiting its efficiency in removing unwanted noise. Comparative analysis with existing BLW and PLI removal techniques indicates that the proposed filter outperforms them based on the evaluation metrics employed.

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%.

This article addresses the challenges encountered in underwater acoustic communication (UWAC) and presents a novel approach for chirp spread spectrum (CSS) communication. CSS is recognized for its ability to adjust to multipath and Doppler dispersion in underwater conditions, despite it usually demands a large bandwidth time product to achieve optimal performance. To address this constraint and improve data rate, the paper proposes a neural network-based receiver for spectral efficient M-ary CSS communication. M-ary communication is accomplished by transmitting chirps with different start and stop frequencies. At the receiver, a multilayer perceptron (MLP) artificial neural network and a one-dimensional convolutional neural network (1D CNN) are used for supervised classification. The neural network is trained offline using a comprehensive dataset developed by the BELLHOP ray tracing algorithm, which simulates various underwater acoustic channels. The application of VTRM pre-processing equalization aims to enhance performance. The simulation results illustrate the superior performance of the proposed receiver when compared to a conventional receiver based on a matched filter. The 16-ary chirp spread spectrum 1D CNN and MLP receivers show a gain of 6 and 4 dB, respectively, in a simulated channel after undergoing VTRM pre-processing. Furthermore, the utilization of a 16-ary 1D CNN receiver results in a noticeable 6 dB enhancement in two recorded channels. However, the MLP receiver outperforms the traditional receiver in terms of bit error rate. The article emphasizes the possibility of higher data rates and enhanced performance in underwater communication systems by employing the proposed M-ary CSS neural network-based method.