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Abstract
One critical issue in the tracking systems based on photovoltaic (PV) is how to harvest highest power of the photovoltaic array; particularly when the system is operating in partially shaded conditions (PSCs) or varying irradiances. This study proposes particle swarm optimization (PSO) hybridization and cuckoo search algorithm (CSA) methods for maximum power point tracking (MPPT). The effectiveness of the proposed algorithm is validated and examined under various irradiance patterns. A comparison study in performance has been conducted between the proposed hybrid CSA-PSO method with the conventional P&O and PSO techniques. Several tests have been performed based on numerical simulations utilizing the programming software MATLAB/Simulink. The results demonstrated that the suggested hybrid technique yields smaller tracking time, higher power and greater efficiency than those of other traditional algorithms.
Abstract
Composite materials are vulnerable to impacts that may occur during their use. Such transverse loads represent a significant threat to these materials because they can cause damage that is difficult to detect. Thus, understanding the mechanical behavior of composite materials during impacts is crucial for improving their damage resistance. Therefore, this study investigates the response of two commonly used composite panels in maritime transportation—a PVC core sandwich composite and a laminated GFRP composite—under quasi-static indentation (QSI). Using numerical simulations with Abaqus/Explicit, this investigation aims to anticipate mechanical characteristics and damage patterns during low-velocity impact. Results show a strong correlation between numerical and experimental data. The force-displacement curves aid in understanding damage sequences, with predicted maximum loads at 1.43% and 6.45% accuracy for laminated and sandwich composites. Both exhibit significant damage, including permanent indentation, matrix cracks, fiber fractures, and prevalent delamination around the impact point.
Abstract
This study evaluates metallic yield dampers, specifically slit steel dampers, for protecting steel beam-to-column connections during seismic events. Finite element model simulations were conducted for the damper and its connection. Analysis of circular parameters, like the radius slot, showed that appropriately sized slit dampers exhibit advantageous seismic behavior. Moment-rotation, hysteresis curves, and plastic stresses comparisons indicate efficient energy absorption. The maximum moment was 25% lower than conventional samples. The slit steel damper model with a ductility factor of 3.5 allows significant plastic deformation before potential failure. Results emphasize the slit damper's potential for optimal performance in steel frames, suggesting its use for efficient energy absorption.
Abstract
Geopolymer concrete (GPC) is a rising eco-conscious substitute for traditional cement-based concrete, bringing the construction industry closer to sustainability. Self-compacting geopolymer concrete (SCGC) enhances the concrete flowability and fills the congested reinforced areas without vibrators in concrete structures such as bridges, tunnels and canals. This study aims to analyze the impact of silicon dioxide nanoparticles (NS) on the rheological and mechanical properties of SCGC to optimize the dosage of NS in SCGC. For this purpose, NS (0–6%) blended in partially distributed binders of fly ash and ground granulated blast furnace slag (50:50) with 0.5 alkaline binder ratio, 2% superplasticizers (9 kg m−3) (MasterGlenium SKY 8233) and 12% extra water (54 kg m−3). Sodium silicate solution and sodium hydroxide ratio of 2.5 was used for this study. It is observed that SCGC with 3% NS replacement complied with the guidelines of EFNARC. According to the T50cm slump flow test, V-funnel test, and L-box test results meet the guidelines of up to 4% NS replacement, and 3% NS addition offers excellent mechanical properties in SCGC. This study concluded that the replacement of 3% of NS improved the fresh and hardened properties of SCGC, which can apply to construction.
Abstract
Today, the role of humans is changing rapidly in both industrial production activities and services. Mediocre, easy-to-learn activities can be performed more efficiently by machines; mediocre knowledge is being devalued while the importance of high-level skills is increasing. As a result, in all sectors of the economy, and especially in engineering, new approaches to expert training are needed; people must learn to hand over certain decision-making roles and to control the processes supported by AI rather than compete with it. STEM education has a responsibility to achieve these goals and must develop appropriate tools for engineering education. This paper presents a complex didactic methodology for competency-based education in engineering bachelor programs. An important element is the mathematical competency map, which shows the importance and place of mathematical and algorithmic (coding) knowledge in engineering topics. Another element is the systematic testing of mathematical knowledge in non-mathematical contexts in engineering courses. We provide an overview of our achievements in applying the developed toolset and improving the efficiency of mathematics teaching in engineering bachelor programs.
Abstract
Using finite element methods, residual stresses were estimated in pipe welds. Experiments were also conducted to verify the numerical results. An alternative to a three-dimensional model was used to simplify the numerical calculation for residual stresses investigation. Model predictions were validated by measuring residual stresses using X-ray diffraction. As compared to measured residual stress distributions, the computational approaches developed in this study can accurately predict welding residual stress distributions. The focused welding parameters have a significant impact on residual stresses even when all the other parameters are the same.
Abstract
The predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults.
Abstract
The integration of Autonomous Vehicles (AVs) into our modern society hinges on gaining widespread acceptance from potential road users. To indicate the preparedness of these road users and elucidate their perspectives regarding the use of AVs in future, it is imperative to conduct surveys gauging public acceptance and satisfaction with this emerging mode of transportation. This paper reports the results of a comprehensive questionnaire study involving 1,000 individuals in Gyor City. The survey's primary objective was to assess participants' attitudes and willingness to embrace autonomous vehicles within the city's road networks. The study delved into various socio-demographic factors, such as age, gender, and employment status, while also exploring participants' prior knowledge and opinions regarding the advantages and limitations of AVs. The findings reveal a generally favorable disposition among the public toward the inclusion of AVs in urban traffic, paving the way for the acceptance of mixed traffic patterns. Notably, respondents in younger age groups exhibit greater enthusiasm for incorporating AVs into their daily transportation, whereas individuals aged 65 and above express more reservations, displaying a conservative outlook. Furthermore, participants with prior knowledge and a deeper understanding of AVs exhibit a markedly more positive inclination toward this emerging technology compared to those lacking such familiarity.
Abstract
Design and testing of real materials is a costly process and usually requires some specific equipment. To alleviate this task numerical methods can be leveraged. In this work we show possible modelling technique for closed-cell material structure using Weaire–Phelan unit cells. As an example existing aluminum structures were used and modelled parametrically, allowing to establish different geometrical models for different applications. Numerical simulations for compression was also done on the developed models to reveal the material response. The influence on the cell wall thickness and the friction between the material and the compression plate was investigated. It was found that the friction coefficient has no significant effect on the material response, except in the case where bonded connection was assumed. It was also demonstrated that material response and the porosity controlled by cell wall thickness have an approximately linear relationship with each other. This method proved to be a flexible and alternative solution of real laboratory tests and targeted to reduce costs of material design.
