Zhihua Xiong, Xiaoling Liu^*, Yinfeng Wang

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.074619

Abstract Autonomous truck platooning, as a novel transportation mode, has attracted significant attention due to its potential to improve transportation efficiency, reduce energy consumption, and lower operational costs. However, this emerging mode poses new challenges to the dynamic performance of long-span bridges. This study aims to investigate the impact of autonomous truck platoons on the dynamic performance of continuous beam bridges. Using finite element software to construct accurate vehicle-bridge interaction models, we simulated the dynamic response between vehicle platoons and bridge structures. The study systematically evaluated the effects of four key factors—vehicle speed, number of formations,… More >

Muhammad Waheed Azam^1,*, Uzair Sajjad^2,*, Faisal Maqbool³, Giovani Sempirini⁴

Frontiers in Heat and Mass Transfer, DOI:10.32604/fhmt.2025.074690

Abstract Heat exchangers play a crucial role in thermal energy systems, with their performance directly impacting efficiency, cost, and environmental impact. A powerful technique for performance improvement can be given by passive enhancement strategies, which are characterized by their dependability and minimal external power requirements. This comprehensive review critically assesses recent advancements in such passive methods to evaluate their heat transfer mechanisms, performance characteristics, and practical implementation challenges. Our methodology involves a systematic and comprehensive analysis of various heat transfer enhancement techniques, including surface modifications, extended surfaces, swirl flow devices, and tube inserts. This approach synthesizes… More >

Yanzhao Yang¹, Xiaowen Song², Zhiying Deng^2,*, Jianyang Yu³

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2026.074345

Abstract In this study, a Gaussian Process Regression (GPR) surrogate model coupled with a Bayesian optimization algorithm was employed for the single-objective design optimization of fan-shaped film cooling holes on a concave wall. Fan-shaped holes, commonly used in gas turbines and aerospace applications, flare toward the exit to form a protective cooling film over hot surfaces, enhancing thermal protection compared to cylindrical holes. An initial hole configuration was used to improve adiabatic cooling efficiency. Design variables included the hole injection angle, forward expansion angle, lateral expansion angle, and aperture ratio, while the objective function was the More >

Lizhong Lu¹, Guangze Li², Zheng Yang^2,*, Yunjing Liu^1,2, Xiaozhuo Guan¹, Tiebin Guo¹

Energy Engineering, DOI:10.32604/ee.2025.075585

Abstract With the substantial advancement of the manufacturing industry, a large number of new types of loads are being connected to the distribution network via power electronic devices, exacerbating the deterioration of power quality at the Point of Common Coupling and making harmonic problems increasingly severe. Currently, most researchers focus on improving harmonic suppression performance from the perspective of the controller, but they overlook the fact that excessive performance enhancement may lead to issues such as resonance in the distribution network. To address this, this paper proposes an adaptive harmonic compensation strategy for scenarios with limited… More >

Jiyuan Liao¹, Jiangyan Zhao^2,*, Xin Li³, Changmao Liu¹, Banghong Tang¹, Zihan Ling³

Energy Engineering, DOI:10.32604/ee.2025.074667

Abstract To address the uncertainty and volatility of renewable energy while meeting the requirements of low-carbon economic operation, this paper proposes a two-stage robust optimal dispatch model for an integrated wind-solar-hydro-thermal-storage energy system with coupled power-to-gas (P2G) and carbon capture system (CCS). First, a mathematical model of the integrated wind-solar-hydro-thermal-storage energy system with P2G-CCS coupling is developed to promote internal carbon cycling and enhance the capability to accommodate renewable energy. Second, the scheduling problem is formulated as a two-stage robust optimization model. A cardinality-based uncertainty set is adopted to model deviations in renewable energy output, and… More >

Peng Wang^1,2, Zhiying Gao^1,2,*, Yongyan Chen¹, Rina Su^1,2, Yefei Bai², Jianlong Ma^1,2, Tianhao Zhang¹

Energy Engineering, DOI:10.32604/ee.2025.073862

Abstract We propose a novel procedure, Time-Domain De-Dopplerized Orthogonal Matching Pursuit deconvolution approach for the mapping of acoustic sources (TD-OMP-DAMAS), for separating aerodynamic noise sources distributed across wind turbine blades (WTB), a task that is typically hindered by mutual interference and spatial mixing. The proposed procedure is a two-stage, hybrid de-Doppler/sparse-reconstruction algorithm based on time-domain de-Doppler (TD, Stage 1) and an orthogonal matching pursuit (OMP)-based deconvolution scheme (Stage 2), enabling sparse-reconstruction techniques to be effectively applied in rotating-source scenarios. The method is validated using both simulated rotating-source data and wind-tunnel measurements, and its performance is systematically… More >

Hussain H. Al-Kayiem^1,*, Safaa M. Ali², Sundus S. Al-Azawiey³, Raed A. Jessam³

Energy Engineering, DOI:10.32604/ee.2025.073303

Abstract Gas Turbines are among the most important energy systems for aviation and thermal-based power generation. The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation, reversal flow, and pressure distortion, mainly in aggressive S-shaped diffusers. Several methods, including vortex generators and energy promoters, have been proposed and investigated both experimentally and numerically. This paper compiles a review of experimental investigations that have been performed and reported to mitigate flow separation and restore system performance. The operational principles, classifications, design geometries, and performance parameters of S-shaped diffusers are presented to facilitate the… More >

Marwa M. Eid^1,2,*, Amel Ali Alhussan³, Ebrahim A. Mattar⁴, Nima Khodadadi^5,*, El-Sayed M. El-Kenawy^6,7

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073555

Abstract Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs, improving material utilization, and ensuring structural safety in modern construction. Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents, especially with the growing use of supplementary cementitious materials and recycled aggregates. This study presents an integrated machine learning framework for concrete strength prediction, combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms, with a particular focus on the Somersaulting Spider Optimizer (SSO). A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,… More >

Divya Ramesh, Sriram Venkatachalam^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069854

Abstract A hybrid model combining Fully Non-Linear Potential Flow Theory (FNPT) based on the Finite Element Method (FEM) and the Unified Navier-Stokes equation, using the 3D Improved Meshless Local Petrov Galerkin method with Rankine Source (IMLPG_R), is developed to study wave interactions with a porous layer. In previous studies, the above formulations are applied to wave interaction with fixed cylindrical structures. The present study extends this framework by integrating a unified governing equation within the hybrid modeling approach to capture the dynamics of wave interaction with porous media. The porous layers are employed to replicate the… More >

Tran Minh Bao¹, Kumar Shashvat², Nguyen Gia Nhu^3,*, Dac-Nhuong Le⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.075161

Abstract The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the… More >