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

{"title":"Ultra-wideband EMP-shielded glass windows using metal mesh films for civilian and military infrastructure","authors":"Dat Tien Nguyen ,&nbsp;Keunsik No ,&nbsp;Chang Won Jung","doi":"10.1016/j.jestch.2025.102235","DOIUrl":"10.1016/j.jestch.2025.102235","url":null,"abstract":"<div><div>A hybrid window structure integrated with metal mesh films (MMFs) is proposed for electromagnetic pulse (EMP) protection in both civilian and military applications. The structure operates over an ultra-wide frequency range from 0.18 to 18 GHz. To the best of our knowledge, research on EMP-shielding windows remains limited in terms of frequency coverage, shielding effectiveness (SE), and optical transparency (OT), with most studies focusing on electromagnetic interference (EMI) shielding windows that achieve SE &lt; 80 dB. This work presents four EMP shielding window configurations, each achieving SE &gt; 80 dB and incorporating, for the first time, an asymmetric hexagonal mesh design. The metal mesh, deposited on a transparent dielectric substrate, exhibits OT of 75.5 % and a sheet resistance of 0.1 Ω/□. Compared to conventional square and symmetric hexagonal meshes, the asymmetric mesh improves SE by up to 4.7 dB at 10 GHz, with only a slight reduction in OT of about 3 %, demonstrating a superior balance between electromagnetic performance and transparency. Four window configurations are examined through both simulation and measurement, with square meshes from our previous work included for comparison. For civilian applications, double-pane glass with two MMF layers achieves average SE above 60 dB while maintaining OT over 40 %. For military applications, three-layer structures reach SE up to 90 dB with OT above 30 %. These results confirm that the proposed configurations provide broadband EMP shielding with sufficient transparency, offering a practical and scalable solution for EMP SE windows.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102235"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air side thermal and hydraulic performance assessment of skived, louver, offset honeycomb, and metal foam finned mini-channel heat exchangers","authors":"Bahadır Doğan ,&nbsp;M. Mete Ozturk ,&nbsp;Levent Turhan","doi":"10.1016/j.jestch.2025.102242","DOIUrl":"10.1016/j.jestch.2025.102242","url":null,"abstract":"<div><div>This study presents a comparative performance evaluation of mini-channel compact heat exchangers, focusing on four distinct extended surface configurations of skived plate fin, louver fin, metal foam, and offset honeycomb. The experiments are conducted in an air tunnel, where the <em>Re</em> number of the airflow ranges from 100–1400, and a constant surface temperature is maintained using a water bath. To ensure a fair comparison, all four configurations are tested under identical conditions. The performance of each configuration is assessed in terms of air-side heat transfer coefficient, pressure drop, Colburn <em>j</em>-factor, and friction factor. In addition to these conventional performance indicators, the heat exchangers are further evaluated using the heat transfer index with respect to friction power, both with and without accounting for compactness. According to the findings, although the cell-based configurations consistently outperform the plate-fin configurations under all conditions, the metal foam and offset honeycomb each demonstrate superiority in different performance metrics. While the heat transfer coefficient reaches approximately 110 W/(m²K) for the metal foam configuration, the offset honeycomb achieves up to 100 W/(m²K) within the same flow range. When evaluated based on the heat transfer index considering compactness, the performance order shifts, with offset honeycomb and metal foam providing values of 60.08 kW/(m³K) and 56.76 kW/(m³K), respectively. Under all tested conditions, the louver configuration, although the most commonly utilized extended surface in conventional applications, falls short of achieving the performance levels demonstrated by the novel designs introduced in this study.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102242"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel multi-functional and compact waveguide filter based on various meta-resonators for C-band applications","authors":"Abdullah Genc ,&nbsp;Habib Dogan","doi":"10.1016/j.jestch.2025.102213","DOIUrl":"10.1016/j.jestch.2025.102213","url":null,"abstract":"<div><div>This paper presents the design, fabrication, and performance analysis of a novel, multi-functional WGF for C-band (4.90–7.05 GHz) applications. The novel WR-159 WG structure is precisely fabricated from Al6065 material by the CNC milling method, and also <em>meta</em>-resonator structures designed in different sizes and geometries are fabricated from low-loss Duroid/RT 5880 substrate material with help of LPKF. Thanks to the designed single WR-159 WG structure, the WGF with four different functions is experimentally realized using a set of <em>meta</em>-resonators that can be mounted and dismounted. These four functions are bandpass/bandstop, narrow/medium/wide bandwidth, shifting operating frequency (5.5, 6, and 6.5 GHz), and filter order (n = 1–7). Filter performance has been verified through simulations and measurements with a vector network analyzer (VNA). For each WGF designed for different functions, simulated and measured performance results, such as for center frequency (f₀), return loss (RL), insertion loss (IL), fractional bandwidth (FBW), and quality factor (Q) are compared, and they have good agreement with each other. The proposed modular structure offers a low-cost and versatile alternative that can replace commercial filters.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102213"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of structural behavior in natural and synthetic fiber reinforced multilayer composites based on resin and additives","authors":"Samet Karabulut,&nbsp;Kaan Kaysadı,&nbsp;Faruk Gümüş","doi":"10.1016/j.jestch.2025.102240","DOIUrl":"10.1016/j.jestch.2025.102240","url":null,"abstract":"<div><div>In our study, jute natural fiber and glass fiber were preferred as reinforcing elements. Epoxy, vinyl ester, and polyester were selected as matrix materials, and sodium hydroxide and magnesium hydroxide were used as additives. When the results were analyzed, the samples produced with epoxy yielded good results in tensile and elongation tests compared to the others, while the average values remained the same in bending and impact tests. The presence of additives was also observed in the SEM images and EDS results.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102240"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear Doherty power amplifier design based on adaptive input signal power control","authors":"Zhiqing Liu ,&nbsp;Ye Zhong ,&nbsp;Zhijiang Dai","doi":"10.1016/j.jestch.2025.102247","DOIUrl":"10.1016/j.jestch.2025.102247","url":null,"abstract":"<div><div>In modern wireless communication systems, the trade-off between efficiency and linearity in Doherty power amplifiers (DPAs) remains a critical challenge. To address this, a novel Schottky diode-based input matching network is presented for peaking power amplifiers (PAs) which leverages a dual optimization mechanism: (1) adaptive gate DC bias adjustment via Schottky rectification, enabling Class B operation in high-power regions to enhance DPA linearity; and (2) nonlinear impedance modulation to suppress premature peaking PA activation in low-power regions, thereby improving back-off efficiency. Based on this structure, an asymmetric DPA is designed and manufactured, with continuous wave test results showing that saturation efficiency is higher than 50%, saturated output power reaches more than 43.4 dBm, and the back-off efficiency of 9 dB is 41.2%–55.6% in the operating band of 0.75–1.25 GHz. The adjacent channel power ratio (ACPR) of the PA is tested using a 20 MHz quadrature amplitude modulation (QAM) signal with a PAPR of 9 dB, and the test results show that, in the band of 1.0–1.3 GHz, the ACPR is −38.1 to −34.0 dBc and average efficiency is from 34.2%–45.5% at an average output power of 36 dBm, which verifies that the designed DPA has good linearity performance.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102247"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of pin-fin arrangement in traction inverter cooling systems: A framework based on CFD simulations, deep neural networks and evolutionary algorithms","authors":"Luca Donetti ,&nbsp;Gaetano Patti ,&nbsp;Stefano Mauro ,&nbsp;Gaetano Sequenzia ,&nbsp;Michele Calabretta","doi":"10.1016/j.jestch.2025.102238","DOIUrl":"10.1016/j.jestch.2025.102238","url":null,"abstract":"<div><div>Efficient thermal management is essential for the reliability and performance of traction inverters. However, direct optimization via Computational Fluid Dynamics (CFD) is often impractical due to the high dimensionality of the design space and the high computational cost of each simulation. To overcome this limitation, a surrogate-based optimization framework is developed to enhance the thermal and hydraulic performance of an automotive traction inverter cooling system. The methodology integrates CFD, deep neural networks (DNNs), and a multi-objective evolutionary algorithm. A simplified representation of the ACEPACK^TM DRIVE power module is employed to generate an extensive dataset through automated, GPU-accelerated CFD simulations, making data generation computationally feasible while avoiding the prohibitive cost of direct optimization. A DNN surrogate model is trained to accurately predict pressure drop and heated-wall temperature, achieving mean relative errors below 3% and 1%, respectively. This surrogate model then guides a Non-Dominated Sorting Genetic Algorithm III in the optimization of key geometric parameters, including pin-fin diameter, spacing, height, wall clearance, as well as of physical parameter such as the surface roughness of the pin-fins. CFD-based validation of the Pareto-optimal designs, performed on the full inverter geometry, indicates reductions of up to 25% in pressure drop and approximately 2% in junction temperature. These results suggest that the proposed methodology promises robustness and generalizability, showing good potential for further application in data-driven thermal design optimization.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102238"},"PeriodicalIF":5.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel mixed Rayleigh distribution model using PID based search algorithm for wind energy applications","authors":"Hilmi Aygün ,&nbsp;Bayram Köse","doi":"10.1016/j.jestch.2025.102239","DOIUrl":"10.1016/j.jestch.2025.102239","url":null,"abstract":"<div><div>Accurate modeling of wind speed distributions is a critical prerequisite for reliable wind energy assessment, system optimization, and long-term performance prediction. Conventional probability distribution functions exhibit notable deviations between the observed and estimated wind speed frequency distributions, indicating their limited capability in capturing the actual variability of wind regimes. To address this gap, this study introduces, for the first time in the wind energy domain, the application of a Mixed Rayleigh distribution in combination with a PID-based metaheuristic optimization algorithm (PSA) for parameter estimation. The proposed approach was tested at three measurement stations: Karaburun, Mersinkoy, and Gelibolu, using extensive wind speed datasets. Comparative analyses were conducted between PSA based Rayleigh, Mixed Rayleigh, and Weibull models, alongside conventional Moment and Maximum Likelihood methods. The proposed model achieved the lowest Sum Square Error (SSE) (0.0016) and Root Mean Square Error (RMSE) (0.0091) in Karaburun, the lowest SSE (0.0014) and RMSE (0.0075) in Gelibolu, and consistently high determination coefficients (R² ≈ 0.9999) across all regions. Additionally, the model yielded the lowest Mean Absolute Percentage Error (MAPE) based on Wind Power Density (WPD) (4.11 %) in Mersinköy and relatively low MAPE values based on Average Wind Speed (3.74 % and 3.26 %) in Karaburun and Mersinköy, respectively. In particular, the Mixed Rayleigh model demonstrated superior flexibility, resulting in improved fitting accuracy and reduced estimation errors. Overall, the findings highlight the methodological novelty and practical potential of combining hybrid distribution functions with advanced optimization algorithms.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102239"},"PeriodicalIF":5.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DC-PFL: A dynamic clustering-based personalized federated learning method for human activity recognition","authors":"Xiaoxu Wen ,&nbsp;Yan Wang ,&nbsp;Menghao Yuan ,&nbsp;Aihui Wang ,&nbsp;Ge Zheng ,&nbsp;Hongnian Yu ,&nbsp;Lin Meng","doi":"10.1016/j.jestch.2025.102230","DOIUrl":"10.1016/j.jestch.2025.102230","url":null,"abstract":"<div><div>Human Activity Recognition (HAR) is essential in pervasive computing, healthcare, and human–computer interaction, where accurate interpretation of motion data underpins intelligent decision-making. Federated Learning (FL) enables privacy-preserving model training across distributed clients without sharing raw data, but suffers from degraded performance under Non-Independent and Identically Distributed (Non-IID) data, a common challenge in HAR due to user diversity and device heterogeneity. To address this, Personalized Federated Learning (PFL) introduces client-specific modeling, often via clustering. However, most existing approaches adopt static clustering strategies, lacking adaptability to dynamic changes in client data distributions. In this work, we propose DC-PFL, a Dynamic Clustering-based Personalized Federated Learning framework that performs round-wise client clustering using lightweight statistical features, like Average Peak Frequency (APF), percentiles, and Median Absolute Deviation (MAD) derived from local model parameters. This design ensures efficient and privacy-preserving similarity estimation across clients. By dynamically adjusting clusters during training, DC-PFL enables fine-grained personalization, better generalization, and improved robustness to Non-IID conditions. Experimental results on HAR benchmarks demonstrate that DC-PFL achieves superior performance in both accuracy and convergence speed compared to existing methods, including FedCHAR and standard FL baselines, validating its effectiveness in real-world federated HAR scenarios.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102230"},"PeriodicalIF":5.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electromagnetic Torque Prediction and Modeling of a Doubly Fed Induction Generator for Wind Energy Conversion Systems Using Machine Learning and Deep Learning Algorithms","authors":"M. Murat Tezcan ,&nbsp;Ebru Efeoğlu","doi":"10.1016/j.jestch.2025.102227","DOIUrl":"10.1016/j.jestch.2025.102227","url":null,"abstract":"<div><div>According to the 2023 Wind Energy Report published by the Global Energy Council, the total installed power of wind energy conversion systems worldwide is around 1 TW. In addition, in 2024 and the following years, an average annual increase of around 15% on this installed capacity is envisaged. This situation reveals the importance and rapid development of wind energy conversion systems (WECS) in renewable energy systems. Accordingly, during the design, modeling and production of AC generators at different power levels used in wind turbines, new generation design and modeling techniques are used in addition to classical modeling methods, and wind turbine generator R&amp;D is developing rapidly. New design and optimization methods have begun to be used in the modeling and performance analysis of Double Fed Asynchronous Generators (DFIG), which are frequently used in the field for different output powers. Modeling DFIG with classical numerical modeling and FEA-based magnetic simulation programs is a time-consuming operation, especially in transient or dynamic analysis. Depending on the performance of the computer, obtaining a transient field distribution solution may take hours or even days to obtain iteration-based field distribution solutions that use the finite difference method as a reference. Therefore, machine learning and deep learning-based iterative optimization and prediction methods stand out as a powerful alternative.</div><div>In this study, electromagnetic torque values obtained through FEA-based simulations for three different DFIGs numerically modeled at medium power levels (250 kVA) with different winding materials (copper and aluminum) were used as reference. These torque curves were estimated using deep neural network algorithms based on K Nearest Neighbors (KNN), Support Vector Regression (SVR), Extra Tree (ET), Random Forest (RF), and Long Short-Term Memory (LSTM). Thus, the FEA results were compared with the predictions obtained from these algorithms, and the predictive performance of the algorithms was evaluated. The performances of the aforementioned algorithms in trainings and cross-validations were compared using R², MAE, and RMSE metrics. The LSTM-based deep neural network outperformed the other algorithms for electromagnetic torque estimation. Using this approach, R² values of 0.990, 0.976 and 0.994 were obtained for DFIG-1, DFIG-2 and DFIG-3 in cross-validation, respectively.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102227"},"PeriodicalIF":5.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opposition learning & PID-based grey wolf optimizer with swarm intelligence for improved load forecasting","authors":"Murat Akil ,&nbsp;Ugur Yuzgec ,&nbsp;Emrah Dokur","doi":"10.1016/j.jestch.2025.102237","DOIUrl":"10.1016/j.jestch.2025.102237","url":null,"abstract":"<div><div>Electricity load forecasting helps grid operators to make informed decisions in terms of planning and managing demand response. Electric power companies utilize load forecasting to make optimal power management. Therefore, accurate forecasting of total electrical load in a region is of great importance. To overcome this problem, this paper proposes a multi-layer perceptron (MLP) hybrid model that contain Swarm Decomposition (SWD) aided Opposition Learning and proportional–integral–derivative based Grey Wolf Optimizer (OLPIDGWO) using historical electricity demand data in non-consecutive years. The dataset used for load forecasting includes loads with different characteristics. Empirical mode decomposition method and swarm decomposition are applied to the original data to decompose the data features. Then, MLP hybrid model is applied for each decomposed signal of the data as the load forecasting model. The advantages of the proposed hybrid model include a significant improvement in forecast accuracy and capture of local maxima. The advantage of the proposed hybrid model over other hybrid models and existing single forecasting models is also verified by error performance metrics. The result of the hybrid forecast model shows that the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2020 are 35 MW, 0.591MW, 0.452MW and 1.47%, respectively, and the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2022 are 22.6MW, 0.475MW, 0.367MW and 1.21%, respectively. The results reveal the SWD decomposition and GWO optimizer module of MLP improve the load prediction, and the proposed model outperforms other load prediction models.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102237"},"PeriodicalIF":5.4,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}