Engineering Science and Technology-An International Journal-Jestech最新文献

This study critically investigates analysis and control of non-isolated boost three-port DC to DC converter (TPC) for standalone PV system. The converter is controlled such that regulates flow of power from PV array to load and batteries as storage devices. According to PV power, there are four operating modes of operation of converter. Simple reduced-order dynamic models of the converter in various modes of operation are obtained using state-space averaging and small-signal techniques. In this work, the controllers of the closed-loop scheme are designed and only two controllers are used to achieve output voltage regulation, and to extract maximum power from PV array under different operating conditions. Closed-loop system simulation is studied to verify the operation of converter with the designed controllers in different modes. Transition between modes is presented with solar radiation variation and change of connected loads. Experimental verification of control systems at different modes of operation is investigated. The experimental results show that the controllers can regulate the power flow between TPC three ports and regulate output voltage under PV irradiance variation and load changing. The controller shows good performance measures such as maximum settling time of 0.06 s, maximum steady-state ripples of ±0.8 %, and 99.3 % power efficiency.

In this study, fluid flow predictions using three different methods were compared: DeepCFD, an artificial intelligence code; computational fluid dynamics (CFD) using Ansys Fluent and OpenFOAM; and two-dimensional, two-component particle image velocimetry (PIV) measurements. The airfoils under investigation were the NACA 0012 with a 10° angle of attack and the NACA 6412 with a 0° angle of attack. To train DeepCFD, 763, 2585, and 6283 OpenFOAM simulations based on primitives were utilized. The investigation was conducted at a free stream velocity of 10 m/s and a Reynolds number of 82000. Results show that once the DeepCFD network is trained, prediction times are negligible, enabling real-time optimization of airfoils. The mean absolute error between CFD and DeepCFD, with 6283 trained primitives, for NACA 0012 predictions resulted in velocity components $U_x$ = 1.08 m/s, $U_y$ = 0.43 m/s, and static pressure p = 4.57 Pa. For NACA 6412, the corresponding mean absolute errors are $U_x$ = 0.81 m/s, $U_y$ = 0.59 m/s, and p = 7.5 Pa. Qualitative agreement was observed between PIV measurements, DeepCFD, and CFD. Results are promising that artificial intelligence has the potential for real-time fluid flow optimization of NACA airfoils in the future. The main goal was not just to train a network specifically for airfoils, but also for variant shapes. Airfoils are used since they are highly sophisticated in fluid dynamics and experimental data was available.

Shrink-fit, wire-winding, and autofrettage processes are commonly utilized to enhance fatigue strength and durability of thick-walled cylinders across various mechanical applications. In this study, a novel practical design optimization methodology has been developed to determine the optimal configuration of a thick-walled cylinder, incorporating different combinations of shrink-fit, wire-winding, and autofrettage techniques. The objective is to identify the optimal layer thickness, shrink-fit interference, conventional autofrettage pressure, and reverse autofrettage pressure, if applicable, to maximize the compressive residual stress and minimize the tensile residual stress, thereby extending fatigue lifetime of the cylinder. First, different configurations of thick-walled cylinders, subjected to various combinations of reinforcement processes, are identified. A dataset of residual hoop stress profiles through the cylinder thickness is subsequently generated for these configurations based on the same manufacturing process. Neural network regression is effectively utilized to construct a single fitting function for the residual hoop stress profiles. A parametric study is performed to determine the optimal training functions, activation functions, and hyperparameters, achieving a remarkable agreement with the dataset, indicated by a coefficient of determination of over 0.97. A combination of Genetic Algorithm and Sequential Quadratic Programming algorithms is utilized to determine the accurate optimal values. Fatigue life analysis is subsequently conducted to estimate the fatigue lifetime of the optimal configuration. Results suggest that the optimal configuration, involving conventional autofrettage of the inner layer followed by shrink-fitting with a virgin layer and wire-winding the entire assembly, achieves a maximum fatigue life of 88 × 10⁶ cycles under cyclic pressure load of 300 MPa.

In response to the escalating global CO2 emissions and the urgent need to reduce dependence on fossil fuels, this study diverges from prior research that predominantly focuses on intentions or attitudes towards renewable energy. It investigates the actual uptake of residential solar photovoltaic (PV) systems in regions rich in solar radiation, where, despite the potential, renewables remain a minor part of the energy mix. Incorporating psychological and functional factors and employing the innovation resistance theory (IRT), the study comprehensively examines solar PV technology’s resistance aspects. Utilizing a robust methodological framework that uses partial least squares-structural equation modeling (PLS-SEM) with fuzzy set qualitative comparative analysis (fsQCA), the research evaluates responses from a comprehensive questionnaire survey of 758 households. The advantages of this method lie in its ability to capture both symmetric and asymmetric relationships, thereby offering a richer and more detailed analysis compared to traditional single-method approaches. PLS-SEM results identify significant barriers: image barriers (β = −0.131, t = 3.418, p < 0.001), traditional barriers (β = −0.084, t = 2.143, p < 0.05), and risk barriers (β = −0.124, t = 4.172, p < 0.001). Positive influences include environmental benefits (β = 0.166, t = 3.108, p < 0.001), environmental concern (β = 0.364, t = 6.341, p < 0.001), and government incentives (β = 0.159, t = 2.767, p < 0.01). Conversely, usage barriers and value barriers appeared non-influential. Conversely, fsQCA revealed that all factors may have a role in the uptake of residential solar PV systems. The novelty of this research is evident in its application of IRT to the context of solar PV adoption and the use of a hybrid analytical method, which together provide new insights into consumer behavior and policy implications. These findings offer actionable recommendations for policymakers and practitioners to promote the adoption of residential solar PV systems.

There are some researches in the literature on the mechanical characteristics of dual-phase (DP) steels used in the automotive industry, but there is no comprehensive research on the corrosion behavior of these steels. In this work, the corrosion behavior of DP steels (DP440, DP590, DP980) exposed to two cycles of accelerated corrosion testing in accordance with Ford CETP 00.00-L-467 was observed. Raman and X-ray diffraction (XRD) techniques were used to classify the corrosion products, and the morphology of the samples was studied using a scanning electron microscope (SEM). Goethite and haematite were the primary chemical compounds determined. In high-mechanical strength DP steels, akaganeite was also identified in corroded specimens. The compounds formed due to corrosion were revealed by SEM images. In this work, according to the results of Raman spectroscopy, which was employed for the first time to reveal corrosion products in high-strength dual-phase steels, it was discovered that corrosion products increased with increasing mechanical strength due to an increasing martensite phase volume percentage. Polarization tests were carried out to support the electrochemical data reported by the Raman analysis. Similarly, an increase in the amount of martensite phase in the microstructure led to a decrease in the material’s corrosion resistance. Polarization experiments were carried out to support the electrochemical data interpreted by Raman analysis. In addition, an increase in the amount of martensite phase in the microstructure led to a decrease in the corrosion resistance of the material. In addition, information regarding the material’s electrochemical performance was obtained through Raman analysis. As shown by Raman, XRD, and polarization tests, the increase in corrosion products formed due to the increase in the amount of martensite led to a decrease in corrosion resistance.

In Turkey, one of the most important earthquake belts in Europe, two major earthquakes (Mw: 7.7 and 7.6) occurred about 8 h apart in Kahramanmaraş province on February 6, 2023. Located on the South Anatolian Fault Zone (EAFZ), Kahramanmaraş province and 10 surrounding provinces were affected by the earthquake, which killed approximately 50,000 people and injured approximately 250,000 people. When the cause of this effect was analyzed, it was determined that the building stock of the region was old, as well as the peak ground acceleration (PGA) values that occurred on the surface and to which the structures were exposed. In addition to structural problems, it has been determined that there are significant ground problems such as ground amplification and ground liquefaction in the geotechnical field. For this reason, it is necessary to accurately determine the spectral acceleration values, which show important parameters in the design of buildings, and to take necessary precautions by identifying problems such as soil bearing capacity, settlement problem, liquefaction, and soil amplification in advance. Turkey Building Earthquake Code (TEC 2019) entered into force in 2019. In this study, surface acceleration records exposed to the structures in the region were analyzed. Earthquake acceleration records were obtained from the stations closest to the epicenters of the earthquakes that occurred in Elbistan and Pazarcık in Kahramanmaraş province, and soil amplification analysis and liquefaction potential risk analysis were performed at 3 different points in Kahramanmaraş province. In addition, 11 earthquake acceleration records were selected from different parts of the world reflecting the earthquake characteristics of the region and the analysis was repeated and the design spectrum was compared with the data obtained from the field. Site-specific soil behavior analyses were performed in the study where the existing conditions of the structures in the region were also examined. The data obtained show that the regulation does not fully reflect the field in cases where the structures are exposed to high acceleration values on the surface due to the soil structure, at the same time, liquefaction problems are high and the structures have not undergone the necessary ground improvement processes.

It is well known that road safety is a major problem in cities, resulting in a large number of accidents with significant injuries and loss of life. Much of this problem occurs when vehicles interact with pedestrians. To try to minimize this problem to a large extent, a combined system using resins and a photoluminescent additive was proposed. To confirm the goodness of this material, a characterisation was carried out covering luminance, vibroacoustic and mechanical properties and a study of its photogrammetry under real conditions of use. A luminance of 68 mcd/m² at 20 min was confirmed, which would allow, by a wide margin, a pedestrian crossing to be observed in a vehicle more than 100 m away. The acoustic vibration test confirmed that the proposed system would provide a very efficient audible warning to pedestrians and would reduce the average vehicle speed by about 37 % overall, while in cases where vehicles have to stop for pedestrians, this reduction would be about 28 %. With the mechanical characterisation, it was possible to determine a vertical displacement of always less than 2 mm in vehicles with a wheel load of 12.5 kN, reaching a compressive and tensile strength of more than 56 MPa. The results obtained confirm a potential reduction in mortality of close to 110 %, and injuries by approximately 55 %, as a consequence of the reduction in vehicle speed. In addition, improved night-time visibility of pedestrian crossings would reduce deaths by 35 % and injuries by 26 %, while in the most favourable situations, these values would be 14 % and 10 % for deaths and injuries respectively. All this confirms the great advantage of the system for improving road safety in urban environments.

Experts conduct damage assessments throughout the city in earthquake-prone areas to evaluate the destruction caused by the earthquake. Based on the ATC-20 Building Safety Values, the buildings impacted by the earthquake are categorized as “Inspected, Restricted Use, Unsafe”. Visual imagery captured both inside and outside the buildings is utilized to document the expedited identification of structural deficiencies and their underlying causes. Nevertheless, architects and engineers find the documentation, reporting, and decision-making process to be a time-consuming task. In the past ten years, extensive research has been carried out to reduce the duration of these procedures, specifically in the fields of construction and machine learning. This study investigates the application of machine learning in decision support systems, drawing on research on post-earthquake damage assessment. Post-earthquake damage assessment reports utilized CNN damage assessment algorithms to classify exterior images of buildings exhibiting “Inspected, Restricted Use, Unsafe” damage. The accuracy and loss values of various algorithms, including different AlexNet algorithms, the VGG19 algorithm, and the Resnet50 algorithm, were compared.

Optimizing speed and propulsive efficiency are the most crucial survival skills for biomimetic robots. This paper investigates a swimming mode controller inspired by the black Knifefish to govern the fast-swimming gait with high propulsive efficiency for an elongated undulating fin robot. The proposed swimming mode controller is composed of a couple of Hopf oscillator-based central pattern generators (CPG) to generate the moving gait of robotic fish and a novel variant of Reinforcement Learning (RL) known as Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) for optimizing the propulsive efficiency. The proposed swimming controller facilitates the autonomous optimization of the oscillatory amplitude of the robotic fish to improve its propulsive efficiency. The proposed MA-DDPG demonstrates an aptitude for functioning within mixed cooperative-competitive environments. Furthermore, it effectively mitigates the drawback of zero amplitude in the updating process of conventional reinforcement learning (RL) methodologies. These findings highlight the potential utility of the MA-DDPG in optimizing the performance of multi-agent systems in dynamic, real-world scenarios. The simulation results show that the undulating fin robot reaches a maximum thrust of 0.9 N with a propulsive efficiency of 12.48 %, which is higher than that of traditional reinforcement learning methods.

This paper proposed two evaluation metrics of the tamper coincidence in a block map design for image watermarking. These evaluation metrics are called Tamper Coincidence Block Ratio (TCBR) and Tamper Coincidence Block Density (TCBD). A tamper coincidence occurred in image authentication and self-recovery when the recovery data and the original block location were tampered with simultaneously. A high tamper coincidence limits image inpainting’s capability to recover the region, leading to an imprecise recovered image. The ratio and density of the tamper coincidence may significantly affect the final recovered image quality. Previously, researchers mentioned the tamper coincidence in their experiment but did not evaluate it with any metrics. They evaluated the robustness of their technique based on the final recovered image quality using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Tamper coincidences are primarily affected by the block map design implemented by the researcher. Thus, TCBR and TCBD provide valuable insight into the block map design’s effectiveness in preventing tamper coincidence. The experimental result shows that the TCBR and TCBD values are inversely proportional to the recovered image quality. A high TCBR and TCBD value leads to low recovered image quality. Therefore, this paper will help the researchers design an effective block map by minimizing the TCBR and TCBD values to obtain the highest recovered image quality.