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  • Open Access

    ARTICLE

    Numerical Investigation of Wind Resistance in Inland River Low-Emission Ships

    Guang Chen1, Shiwang Dang1, Fanpeng Kong2, Lingchong Hu1, Zhiming Zhang1, Yi Guo3, Xue Pei1, Jichao Li1,4,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2721-2740, 2025, DOI:10.32604/fdmp.2025.068889 - 01 December 2025

    Abstract To enhance the navigation efficiency of inland new-energy ships and reduce energy consumption and emissions, this study investigates wind load coefficients under 13 conditions, combining a wind speed of 2.0 m/s with wind direction angles ranging from 0° to 180° in 15° increments. Using Computational Fluid Dynamics (CFD) simulations, the wind load is decomposed into along-course (CX) and transverse (CY) components, and their variation with wind direction is systematically analyzed. Results show that CX is maximal under headwind (0°), decreases approximately following a cosine trend, and reaches its most negative value under tailwind (180°). CY peaks at More >

  • Open Access

    ARTICLE

    Optimization Ensemble Weights Model for Wind Forecasting System

    Amel Ali Alhussan1, El-Sayed M. El-kenawy2,3, Hussah Nasser AlEisa1,*, M. El-SAID4,5, Sayed A. Ward6,7, Doaa Sami Khafaga1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2619-2635, 2022, DOI:10.32604/cmc.2022.030445 - 16 June 2022

    Abstract Effective technology for wind direction forecasting can be realized using the recent advances in machine learning. Consequently, the stability and safety of power systems are expected to be significantly improved. However, the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem. This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models. This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization (PSO-Guided WOA). The proposed optimized weighted ensemble predicts the wind direction given a set of input features. The… More >

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