Journal of Engineering Research最新文献

The objective of this study is to present the electromagnetic and the thermal multi-field coupling model of the induction heating process for numerical simulation based on the finite element method. One of the most important difficulties encountered in the induction heating processes is to ensure homogeneous temperature distribution throughout the part. To improve the uniform temperature distribution in the sheet, the induction heating system is modelled with the ANSYS software taking into account some operational and geometrical parameters including current density and coupling distance between induction coil and sheet. Induction heating simulations were performed for all simulations at 20 kHz frequency ANSYS Maxwell. The numerical model has been verified by the conducted experiments for Ti6Al4V at the current of 50 A, 125 A, and 200 A, and the 1 mm and 3 mm gap distances. The relative error of the maximum temperature between the experiment and simulation was found around 14 % recorded at 25 s measurements. In addition, the effects of the current and the frequencies on the induction heating were evaluated by the verified numerical model for epoxy/carbon fiber (UD prepreg) and epoxy/carbon fiber (Woven prepreg) plates. The results show that the induction heating model is suitable and efficient to determine the temperature distribution within the thin plates by the finite element method.

Power prediction in solar powered electric vehicle (EV) charging stations is very essential for smooth and uninterrupted operations due to the high oscillatory output of renewables and their dependence on various atmospheric factors. The need for early prediction helps EV stations improve their power performance and utilize available power by designing intelligent charge scheduling algorithms. This study introduces a novel design approach for an off-grid photovoltaic (PV)-powered EV charging station, which involves three main stages: evaluating and analyzing different solar irradiance prediction models (theoretical, empirical, and artificial neural network (ANN) models), forecasting day-ahead solar power profiles, and optimizing charge scheduling for pre-booked vehicles using energy storage systems (ESS). The effectiveness of various solar irradiance prediction models is assessed to identify the best-performing model. The proposed approach employs a novel algorithmic procedure to fine-tune the selected model using a basic dataset. Power prediction simulations are conducted using MATLAB, while Python is utilized for model development. The feed forward neural network (FFNN) model for irradiance prediction has a 0.88 R² score; the anisotropic general regression neural network (AGRNN), isotropic GRNN both have 0.94 and 0.95 R² values for direct PV current prediction, providing a strong base for reliable forecasting models. The significance of ESS backup for effective charging stations is clearly demonstrated by a remarkable 20 kW peak shaving.

Natural fiber has earned great attention for its discovery as a green material in dielectric composites. Their excellent dielectric properties have granted them the great capability to be used in high dielectric composites. Hence, this study attempts to determine the most influential factor contributing to the permittivity of pineapple leaf fibers and to examine the morphological structure of the developed fibers. The two-level factorial analysis was applied to determine the significant, influential factors and the best conditions contributing to the permittivity value of fiber. The factors include the pineapple leaf-to-soda ratio (1:5 and 1:10), soda concentration (5–10 wt%), temperature (60–100 ℃), and pulping time (45–75 min). The fiber was extracted from the pineapple leaf through the soda pulping method, and the content was analyzed by the Kurschner-Hanack method. Based on the analysis, the pineapple leaf-to-soda ratio was observed as the most significant factor contributing to the permittivity value of fiber, with an 8.86% contribution. The best conditions were suggested at a 1:10 pineapple leaf-to-soda ratio, 5 wt% soda concentration, 100 ℃ temperature, and 45 min of pulping time, contributing to the 1.85 permittivity value of pineapple leaf fiber. The scanning electron microscope images of the material under test indicate that the morphological structures play a crucial part in determining the permittivity value of fiber. Therefore, with suitable processing factors, pineapple leaf fiber can be a great dielectric material used in many engineering applications.

This study evaluated the durability of concrete incorporating different silica fume (SF) content and also computed the service life of this concrete in marine environments. The specimens were prepared with three different SF content (i.e. 8%, 10%, and 12% by mass of cement replaced with SF). Three water/binder ratios (0.25, 0.30, and 0.35) were used for preparing concrete specimens. The mechanical properties were assessed via compression and splitting tensile tests. The rapid chloride penetration and chloride migration experiments were employed to evaluate the durability of concrete. The results showed that the mixture with 10% SF replacement showed the best performance. The inclusion of SF not only reduced the total porosity but also refined the volume fraction of harmless and less harmful pores (< 200 nm). Consequently, the chloride ion penetration resistance of concrete was improved, which in turn reduced the potential corrosion rate of reinforcement. From the results of service life prediction using Life-365 software, it is indicated that the utilization of SF for cement replacement effectively improved the corrosion resistance of steel bars in marine reinforced concrete. The service life against salt damage can be remarkably extended by substituting cement with an appropriate SF level.

A regenerative plant is examined with respect to cost and thermodynamic optimization using the power-quantity ratio (PQR) and the classical thermal efficiency. The PQR is compared with the thermal efficiency index in adaptive response intended for low carbon footprint in power production. The Dataq PLC is employed for smart embedded PC control. The ranges of values of the vaporizer pressure and temperature are 5–20 MPa and 350 – 580 ^oC, respectively. By specifying the values of the thermodynamic variables in the Engineering Equation Solver (EES) software environment which was used to computerize the thermodynamic relations, the heat input and work output as well as the classical efficiency index are calculated.

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.

In mass production, welding flaw detection in existing surface mount technology (SMT) has certain constraints, including its high costs, heavy workloads, and time-consuming processes. However, image classification technology using computer vision demonstrates high detection speeds and considerably reduced detection costs in flaw detection. Nevertheless, the increased integration of chip components on printed circuit boards (PCBs) and reduced component sizes pose challenges for flaw detection technology. Therefore, in this paper, an SMT welding image flaw classification model—that is, the ResNet-34-ECA model—based on an improved ResNet model, is proposed. Initially, the dataset is amplified using data amplification methods, such as stochastic rotation, increased data diversity, and enhanced model robustness. The ResNet34 model is then optimized using the light quantization efficient channel attention (ECA) module, resulting in higher classification accuracy. The experimental data in this study were collected using automated optical inspection (AOI) equipment, following the manual creation and amplification of the dataset. The experimental results showed that the baseline model accuracy increased by 0.22 in the augmented dataset, reaching 97.2%. Moreover, the ResNet-34-ECA model proposed in this paper could realize the classification of SMT welding image defects successfully; the overall classification accuracy of the improved ResNet image classification model was 0.01 higher than that of the baseline model, reaching 98.2%. Consequently, the proposed model proves to be better than other models in defect classification on this dataset, providing an accurate classification of SMT welding image defects.

The radiation (beta particles (β), protons (¹H), α-particles (He²⁺), carbon ions (C⁶⁺), fast neutrons (FN), and thermal neutrons (TN)) absorption parameters of 75TeO₂–10MoO₃–10BaO–(5-x)Sm2O3–xBi₂O₃ glass system were evaluated and presented along with their optical parameters in this study. The parameters were analyzed with the objective of evaluating the role of Sm and Bi atoms in altering the optical and radiation characteristics of the glass system. Starting with high-purity chemicals, namely BaCO₃, Bi₂O₃, MoO3, TeO₂, and Sm₂O₃, and following the melt-and-quench procedure, 75TeO₂–10MoO₃–10BaO–(5-x)Sm₂O₃–xBi₂O₃ glasses were prepared as TeMoBa-Sm/Bi1, TeMoBa-Sm/Bi2, TeMoBa-Sm/Bi3, TeMoBa-Sm/Bi4, and TeMoBa-Sm/Bi5 for Bi₂O₃ molar concentration of 0, 2, 4, 4.5, and 5 mol %, respectively. The optical parameters were computed theoretically from measured absorption spectra. The stopping powers and ranges of β-particles, protons, and α-particles were estimated using the ESTAR, PSTAR, and ASTAR software, respectively, while the SRIM code was used to compute for heavy carbon ions. In addition, the cross-sections of fast (FN) and thermal (TN) neutrons were computed using the standard expressions. There was a positive correlation between Bi₂O₃ content and the molar volume of the glasses. The refractive indices vary slightly between 2.53 and 2.56. The molar refractivity ($R_m$) values for TeMoBa-Sm/Bi1, TeMoBa-Sm/Bi2, TeMoBa-Sm/Bi3, TeMoBa-Sm/Bi4, and TeMoBa-Sm/Bi5 are 19.769 cm³/mol, 19.7³0 cm³/mol, 19.689 cm³/mol, 19.656 cm³/mol, and 19.754 cm³/mol, respectively. The values of $S_p$ for proton are highest at 0.09 MeV with values of 284.047, 281.041, 278.135, 277.434 and 276.633 MeV cm²/g for TeMoBa-Sm/Bi1, TeMoBa-Sm/Bi2, TeMoBa-Sm/Bi3, TeMoBa-Sm/Bi4, and TeMoBa-Sm/Bi5, respectively. The Bi₂O₃ content thus improved the CR shielding ability of the glass system slightly. The optimum Bi₂O₃ concentration for FN shielding is 4 mol %. In addition, Bi₂O₃ compromised the TN absorption ability of the TeMoBa-Sm/Bi glass system.

In order to improve the quality of physiological signals, a combined study of blind source separation and wavelet thresholding methods was conducted, resulting in the proposal of a multispectral adaptive wavelet denoising (MAWD) method. This method was employed in conjunction with an improved unsupervised source counting algorithm (USCA). To evaluate the effectiveness of the proposed approach, three methods were used to calculate signal-to-noise ratio (SNR) and root mean square error (RMSE): soft thresholding, hard thresholding, and adaptive thresholding. The results demonstrated that the proposed method exhibited strong applicability under soft thresholding. Specifically, compared to hard thresholding, the enhanced signal using soft thresholding showed an approximately 44.2% increase in SNR and a 28.8% decrease in RMSE, along with a 1.4% reduction in processing time. Moreover, when compared to adaptive thresholding, soft thresholding exhibited approximately 706% improvement in SNR, a 16.7% decrease in RMSE, and a 3.0% reduction in processing time. Multiple experiments were conducted to determine the optimal peak detection threshold range for USCA, which was found to be within the interval [0.001, 0.0001]. This range facilitated the separation of more sources, thereby enhancing the separation effectiveness and accuracy. To substantiate the effectiveness of the USCA method, tests were conducted on publicly available datasets of EMG, ECG, and EEG signals, all of which consistently demonstrated the advantages of this approach.

Data Availability

The authors do not have permission to share data.

Background

Chaotic oscillations within the power system give rise to instability. While these oscillations may not have an immediate impact on the synchrony of the machine, they stimulate one of the oscillation modes, ultimately leading to voltage collapse and a loss of synchronism.

Objective

This paper introduces a modified Whale Optimization Algorithm (WOA)-based Battery-STATCOM (Static Synchronous Compensator) as a solution to mitigate chaotic oscillations within a Single Machine Infinite Bus (SMIB) system.

Methodology

An adaptable controller is implemented to manage the gate signal within the Battery-STATCOM. The AC-DC currents of this controller are optimally governed by two distinct WOA-tuned Proportional-Integral (PI) controllers. The battery storage unit serves as a robust voltage source, with the intelligent controller maintaining the DC-link voltage at the desired level.

Test Cases

Additional disturbances, such as gradual variations in reference voltage and electromagnetic torque, are introduced to exacerbate chaotic oscillations. This is done to assess the controller's real-world performance under adverse conditions.

Results and Conclusion

Under zero damping conditions, rotor parameters, including rise time, settling time, peak time, and overshoot, initially remain undefined due to uncontrolled oscillations. However, once the Battery-STATCOM is applied, these parameters are defined and achieve values (in seconds) of 0.90, 6.21, 1.71, and 21.10, respectively. After further optimization through the proposed modified WOA optimizer, the parameters reach values of 0.25, 1.01, 0.89, and 1.78, respectively. These results underscore the effectiveness of the proposed metaheuristic controller in suppressing overall chaotic oscillations within the power system.