September 16, 2025Open Access

Data‐Driven Structural and Aerodynamic Optimization of Ship Masts Using ANN‐GA Framework Integrated With FEA and CFD Analysis

MHMohammadreza HadaviIsfahan University of Medical Sciences KAKarim AkbarivakilabadiImam Khomeini Hospital SNSaeid NikabadiImam Khomeini Hospital

Key Points

The optimized mast achieved a 50% weight reduction and improved aerodynamic efficiency.
Using artificial neural networks and genetic algorithms, designs were refined, reducing drag by stabilizing wake flow.
Finite element analysis highlighted stress distributions, leading to effective performance evaluations.
The study's methods have high accuracy, with the ANN model reaching over 97% in predictions.

Abstract

ABSTRACT The maritime industry faces increasing pressure to enhance vessel efficiency and reduce environmental impact, largely through structural weight reduction. This study presents the design and optimization of a lightweight Enclosed Mast for naval ships, using both steel and composite materials. The aim was to improve aerodynamic efficiency and reduce stress concentrations. Finite element analysis (FEA) and computational fluid dynamics (CFD) were employed to evaluate structural and aerodynamic performance. An artificial neural network (ANN), coupled with a genetic algorithm, identified optimal designs, while Monte Carlo simulations addressed uncertainties, and sensitivity analysis determined the most influential input parameters. The optimized mast achieved a 50% weight reduction and a 36% decrease in body thickness, along with improved stress distribution. Aerodynamically, it reduced low‐velocity zones and drag by stabilizing the wake and smoothing flow. The ANN model achieved over 97% accuracy and a low error of 0.05, validating the approach for future marine structure optimization.

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