Arabian Journal for Science and Engineering最新文献

Several novel configurations based on leaf structures are proposed for generating radiating elements. The next leaf architectures (found in nature) are assessed and studied: Phyllanthus poumensis Phyllanthaceae, Desfontainea spinosa Desfontaineaceae, Rubus mesogaeus Rosaceae, Adenia heterophylla Passifloraceae, Leepierceia preartocarpoides Proteales and Tetracera rotundifolia Dilleniaceae. Each leaf architecture generates a radiating element by processing the image of plant leaf. The contributions of this proposal are: Six new antenna designs are provided with interesting characteristics in electromagnetic radiation and frequency performance, and a comparative study is made between the performance of the proposed designs and the existing work found in the literature. Simulation and experimental results are provided to evaluate and discuss each leaf antenna. An analysis in detail is provided considering parameters such as reflection coefficient, VSWR, antenna gain, current distribution and radiation pattern. The prototype and the experimental results (of each leaf architecture) are assessed considering the impact on the design of radiating elements and some wireless technology applications.

In polymer matrix composites (PMCs), reinforced lignocellulosic fibres are one of the excellent endeavours; doing so eliminates the need for the more commonplace synthetic fibres. In this respect, the fibres from Enset ventricosum (EV), one of the underutilized which have not been studied extensively, were focused to carry out an investigation on PMCs applications using the particles of Limonia acidissima fruit shell powder (LASP) as reinforcing agent. The study set out to evaluate the adeptness of altered LASP and EV plant fibres by 4% NaOH treatment. The results from morphological, physicochemical, XRD, FTIR, and thermal aspects of alkali-treated samples of LASP and EV fibres revealed that the alkali treatment significantly improved the compatibility of biomaterial’ property to utilize the natural fillers in the epoxy–EV fibre composites. The first-degree polynomial model was fitted using the response surface analysis to optimize the impact energy, water absorption, tensile, and flexural strength of reinforced fibre with respect to composition and fibre length. Using RSM numerical model, aforementioned properties were analysed to develop the ideal epoxy–EV fibre composite for attaining a minimal water absorption, a high tensile modulus, flexural strength, and impact energy. Accordingly, 3 mm of fibre length reinforcement with 38.3 wt % of biomaterials loading reinforcement was found to be optimized for idealistic epoxy–EV fibre composite.

The present study addresses the issue of varying fault locations within a distribution system, which leads to fluctuations in short-circuit currents and requires the implementation of adaptive protection strategies for network reliability. This paper presents a novel adaptive protection scheme that specifically considers these fault location variations using directional overcurrent relays (DOCRs). Unlike previous research on adaptive protection, which does not adequately account for fault location variations, this method employs deep neural networks (DNNs) for online fault location detection. In the verification process, the effectiveness of the proposed methodologies was assessed by analyzing the time derivative of a trained machine learning model for fault identification. This approach enables the immediate detection of fault locations within the distribution system and facilitates the transmission of the setting group index to activate preset optimal coordination parameter values for the system relays. Crucially, the proposed method ensures that the coordination constraints remain intact across various adaptive settings, relying on precise fault identification through machine learning. The practical significance of this approach lies in its applicability to real-world systems because the proposed solutions and protective settings can be easily implemented using commercially available relays. To evaluate its effectiveness, the adaptive protection scheme was tested on three distribution networks: IEEE 14-Bus, 15-Bus and 30-Bus. The comparative test results highlight that the proposed method significantly improves the speed of the protection system for distribution networks when compared to existing studies, making it a valuable contribution to enhancing network reliability and performance.

Predicting wind speed accurately is essential for the efficient generation of wind power. To enhance the precision of wind speed forecasting, this paper proposes a multivariate combinatorial model based on a deep learning neural network, which incorporates not only historical wind speed data but also relevant meteorological features. Initially, the feature extraction of meteorological features related to wind speed is first performed using an autoencoder and singular value decomposition. Subsequently, the complementary ensemble empirical mode decomposition and wavelet transform method is utilized to mitigate noise in the wind speed series. Finally, this paper utilizes a gated recurrent unit (GRU) deep learning neural network for predicting the wind speed series. The optimization of the GRU’s four hyperparameters is accomplished through the implementation of the improved gray wolf algorithm. This paper evaluates and validates the predictive performance of the model using two datasets. The experimental results demonstrate that the mean absolute percentage error of the proposed model’s 1-step predictions on the two datasets is 0.7532% and 0.5263%, with corresponding root mean square error values of 0.0283 and 0.0227, respectively. These results indicate a significant improvement over those achieved by other models under comparison.

Evaluating the bearing capacity of strip footing is a classic problem in geotechnical engineering, which has been investigated by many researchers. As the advancement of technology and urbanization, less and less land area can be available for the construction of facilities, which results in that many buildings must be constructed near slopes. The bearing capacity of strip footing constructed near slopes is usually lower than that on flat land. When the soil strength of slopes is not sufficient to support the external loads, it is often necessary to backfill or reinforces the slopes to make the strength of slope meet application requirements. The discontinuity layout optimization (DLO) method is therefore adopted in this paper to investigate the effects of various factors on bearing capacity and failure mechanism of strip footing on inclined multilayered natural slopes. Two conditions, normal slope and backfilled reinforced slope, are considered in the analysis. In addition, the influence of distance between the strip footing and slope, the number of soil layers, the thickness of the interlayer soil layer and earthquake on the unreinforced slope, and the influence of geosynthetic length and burial depth on the reinforced slopes are investigated. Eventually, the results showed that different factors have different impacts on the slope bearing capacity and failure mechanism.

For a photovoltaic module coupled with thermoelectric generator, a unique wavy cooling channel is proposed, and its performance is numerically assessed by using three-dimensional computations. The cooling channel uses nanofluid of alumina–water with various shaped nanoparticles (spherical, cylindrical and brick). Numerical simulations are performed for a range of parameters for the corrugation amplitude ((0 le text {Amp} le 0.1)), wave frequency ((2 le text {Nf} le 16)), nanoparticle loading quantity ((0 le text {SVF} le 0.03)), and nanoparticle shape (spherical, brick, and cylindrical). We analyze the photovoltaic module’s average temperature and temperature uniformity for a variety of parameter variations. When nanofluid and greater channel corrugation amplitudes are utilized, the average panel surface temperature is decreased more. A wavy shape of the cooling channel at the maximum corrugation amplitude yields a cell temperature reduction of 1.88 (^text {o})C, while frequency has little impact on average cell temperature and its uniformity. The best-performing particles are those with cylindrical shapes, and the drop-in average photovoltaic temperature with solid volume fraction is essentially linear. As utilizing cylindrical-shaped particles, the average temperature of corrugated cooling channels decreases by around 1.9 (^text {o})C as compared to flat cooling channels with base fluid at the greatest solid volume fraction. As compared to un-cooled photovoltaic, cell temperature drops by around 43.2 (^text {o})C when employing thermoelectric generator. However, temperature drop value of 59.8 (^text {o})C can be obtained by using thermoelectric generator and nano-enhanced wavy cooling channel utilizing cylindrical-shaped nanoparticles. An hybrid computational strategy for the fully coupled system of photovoltaic with cooling system is provided, which reduces the computational time by a factor of 75.

Fractional calculus is an important mathematical tool that is widely used in control systems. It is established in the literature that fractional order models are more accurate and more effective in system modelling. In this study, an alternative and novel technique is proposed to identify the fractional order time-delayed model of an unknown system. The method is based on obtaining the approximate stability boundary locus (SBL) curve of the unknown system by applying three different experimental tests. Three points on the SBL curve are determined by the experimental tests and then the parameters of the fractional order time-delayed model are computed by solving the nonlinear systems of equation. The system model with double fractional order element plus a time delay is obtained using the proposed method. The proposed method is explained through simulations on a twin rotor system. The proposed method is also used in model order reduction calculation of the higher order transfer functions.

In light of the pressing need for optimizing the cost and efforts involved in geotechnical investigations, this study presents a spatial analysis and interpretation of geological and geotechnical database of Riyadh City. By consolidating available data from geotechnical investigation reports of the study area, spatial maps have been developed focusing on subsoil types and rock quality designation. The application of the geostatistical analyst extension in ArcMap highlights significant spatial variation in subsoil characteristics, leading to a more accurate zonation of geotechnical profile. It is emphasized that among several interpolation methods, the inverse distance weighting emerges as a better approach for representing these variations, enabling the creation of detailed geotechnical zonation maps. Considering its importance, the data on groundwater table depths at various locations were also retrieved and visualized illustrating the frequency of presence of groundwater in bedrock (limestones) or in the surface soils. This insight can be instrumental in strategizing groundwater pumping for water supply as well as designing dewatering systems for potential excavations in the study area. The findings of this study indicate that a GIS-based overview of subsoil profiles allows the construction engineers to plan and execute projects more effectively, resulting in considerable savings in time and financial resources associated with site investigations as well as contributing the sustainable development of infrastructure projects in the region.

The diagnostic accuracy and reliability of an unsupervised electrocardiogram (ECG) analysis system entirely depend on the response of ECG preprocessing stage. Unfortunately, ECG signal analysis faces the challenge of getting distorted by various noises and artifacts (physiological and non-physiological origin). Thus, designing a denoising technique capable of dealing with different noises in real time is challenging, so selecting a noise analysis model is particularly important. Based on the extensive survey of state-of-the-art techniques, it is noticed that all the denoising techniques are designed with an implicit assumption that noises distorting ECG signals are of Gaussian nature and therefore, are based on the Gaussian distribution noise analysis model. However, in practical scenarios, noises may not always have a Gaussian nature. Therefore, this paper puts forward a non-Gaussian (alpha)-stable distribution model for noise analysis from the perspective of ECG signal analysis. This distribution model encompasses Gaussian distribution as a special case. From rigorous simulations and analytical studies, this research offers statistical proof that the (alpha)-stable distribution noise model may effectively capture background noises corrupting ECG signals. Ultimately, the effectiveness of R-peak detection techniques and deep learning models is evaluated in the presence of two types of noise: Gaussian distribution and (alpha)-stable distribution. Finally, through intensive simulation studies, it is discovered that relying on the assumption of Gaussian background noise can be misleading when the actual noise follows a non-Gaussian (alpha)-stable nature.

The offshore regions typically experience greater wind speeds, which makes offshore Wind Turbines (WTs) more efficient. This enhanced output comes with a price, including more maintenance requirements, greater proneness to malfunctions, and difficulties with accessibility. It is challenging to gather the signatures with invasive sensors from offshore WTs for a long time due to the surrounding conditions. Sensors get displaced from their positions, and the acquired data becomes less reliable. The issues with invasive sensors in offshore locations for WTs are addressed in the study by presenting a non-invasive method for fault detection in offshore WTs using thermography. The approach is able to classify 11 different health conditions of WTs such as healthy, single-multiple phase stator faults with different shorting percentages, cooling fan faults, and rotor faults. Serial Based Feature Fusion technique is proposed in which features are extracted from seven Pre-Trained (PT) models and fused to get a feature set with advantages of individual PT model. The problem of high processing time and complexity a Hybrid Feature Selection technique is proposed in which the feature selection is done in two stages along with hyperparameter tuned Shallow Learning (SL) Classifier at the output layer. The algorithm is tested for multiple combinations of DNN and SL approaches. The highest achieved efficacy is 100%. By using feature set with best possible feature, the suggested model is more reliable. Additionally, it eliminates the necessity for segmentation and clustering, which reduces the computational burden and time required for diagnosis.