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CONTENTS
Volume 15, Number 2, June 2025
 

Abstract
In general, for an autonomous underwater vehicle (AUVs), its shape is one of an important parameter influencing its performance. AUVs can be propelled using conventional propellers or by undulating (flapping), its body like aquatic animals. Most of the existing AUVs that propels by undulating its body are multi-segmented and rectangular. However, research studies have often focussed on NACA hydrofoils, despite the prevalence of rectangular designs in AUVs. This paper aims to fill the gap in the current research by conducting a comparative study of the thrust force generation and efficiency of rectangular fin and hydrofoil. Besides, a merged body shape (MBS) has been proposed, combining the advantages of hydrofoil and fin. A comprehensive analysis has been made by comparing the performance of an undulating NACA 0012 foil, a rectangular fin with the proposed MBS at St = 0.2 – 1, for non-dimensional wavelengths (l*= 0.8 and 1.0) at Re = 1000. Increase in thrust forces are observed with increase in St and l*. The efficiency at l*=0.8 is higher than that at l*=1.0, indicating optimal wavelength for high efficiency. The MBS generates a mean thrust coefficient comparable to the hydrofoil, offering a balance of thrust and modular adaptability.

Key Words
anguilliform; autonomous underwater vehicles; biomimetic; hydrodynamics; undulatory motion

Address
B. Narendhiran and K. Narendran: Department of Ocean Engineering, Indian Institute of Technology (IIT) Madras,
Chennai, 600036, Tamilnadu, India

Abstract
Maritime Natural Caves (MNCs) are real and natural representations of shoreline Oscillating Water Column (OWC) devices. Recently, one particular MNC located in Cidade Velha on Santiago Island, Cabo Verde, has been the focus of several studies aimed at analyzing its behavior and energy performance under different wave climate conditions. This study investigates the operation of this MNC, focusing on the impact of airflow damping caused by its power take-off mechanism, represented here by orifices (ORs) with different cross-section areas and Wells turbines with various rotor blade stagger angles (B) on its energy extraction and production capacity. Our study showed that the power available from the MNC is linked to the flow damping characterized by the area contraction coefficient and the linear damping coefficient for turbines. The optimum flow contraction coefficient was found to be 𝐶􀯖 = 0.269, which maximizes both the average and peak power available from the cave, reaching 439.5 𝑊 and 4237.9 𝑊, respectively. When using turbines, the average power available ranged from 38.3 W to 80.5 W, which was considerably lower than the range observed with orifices (132.2 W – 454.1 W). This suggests that the damping capacity of the turbines could be improved. The turbine with the highest damping coefficient (B=15) produced the highest values of both available and converted power. However, it showed limitations in effectively converting the energy made availabThe turbines with moderatele by the MNC. The turbines with moderate damping, between 2.52 𝑐𝑚𝐻 m3/s (turbine with B=0) and 2.84 𝑐𝑚𝐻 m3/s (B=10), demonstrated better energy conversion performance with average efficiency of 23.6% and 20.7%, respectively. The remaining turbines exhibited lower average efficiency: 16.6% (B=15) and 13.3% (B=5). However, all turbines showed losses of efficiency when the MNC operated with high airflow rates. The turbine with B=0 had a contraction coefficient of 0.545, significantly higher than the optimal found for the orifices. The ideal contraction coefficient could be achieved by increasing both the turbine blades and hub by 60%.

Key Words
airflow damping; maritime natural caves; ocean energy; turbine geometry; wells turbines; orifices damping coefficient

Address
Wilson Madaleno Léger Monteiro, Jakson Augusto Léger Monteiro, Bruno Roberto Semedo,
Arider Barbosa Carvalho and Tomás Tavares Furtado: Faculty of Science and Technology, University of Cabo Verde,
P. O. Box: C.P. 379-C, 7943-010, Praia, Cabo Verde
António José Nunes de Almeida Sarmento: WavEC - Offshore Renewables, Edifício Diogo Cão Doca de Alcântara Norte 1350-352 Lisbon, Portugal

Abstract
This study presents a comprehensive data-driven approach for the design and optimization of multi-chamber oscillating water column (OWC) wave energy converters by integrating high-fidelity computational fluid dynamics (CFD) simulations with machine learning (ML) techniques. The CFD model was rigorously validated against experimental data from literature results, with good agreement observed in both hydrodynamic efficiency and power output. Further, a large input data has been generated with distinct simulation cases, spanning single-, double-, and triple-chamber chamber configurations under various wave conditions with kh ranging from 2.0 s to 5.5 s, were conducted. The CFD-generated dataset was employed to train several ML models—polynomial regression, decision trees, random forest, XGBoost, support vector regression, and multilayer perceptron. XGBoost demonstrated better performance compared to the other machine learning models evaluated. Furthermore, to identify the optimal design configuration, Latin Hypercube Sampling was employed to randomly generate 1,000 distinct OWC configurations, which were then evaluated using the XGBoost model. The top ten configurations were identified, with the highest predicted power output of 36.40 W obtained from the dual-chamber OWC configuration. These findings confirm the potential of ML-driven models to significantly reduce computational cost and accelerate the design of efficient wave energy systems.

Key Words
CFD; design optimization; ML models; multi-chamber OWC; XGBoost

Address
S.Prasanna and Poguluri Sunny Kumar: Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Yoon Hyeok Bae: Department of Mechanical and System Design Engineering, Hongik University,
Seoul 04066, Republic of Korea


Abstract
This paper provides a novel numerical approach in order to simulate ocean wave propagation, integrating o-transformation with the finite difference schemes. The governing equations are derived from viscous flow theory, specifically the incompressible Navier-Stokes equations under the assumption of negligible viscosity (Euler's equations). The continuity equation represents the conservation of mass and is a fundamental part of fluid dynamics, applicable to both potential flow theory and viscous flow theory, and momentum equations represent the conservation of momentum in the horizontal and vertical directions, respectively. The method enhances accuracy and stability in modeling wave dynamics in deep and transitional waters while effectively handling complex geometries and boundary conditions. Numerical experiments indicate high precision and the capability to capture nonlinear wave behavior, particularly in comparisons with linear and second-order Stokes theory. Stability analyses confirm that the framework maintains reliable results across varying time steps with minimal error growth. This research provides a powerful tool for ocean wave simulation, holding significant implications for marine engineering, environmental studies, and coastal management.

Key Words
finite difference method; numerical solution; numerical stability and accuracy; ocean wave propagation; o-transformation

Address
Nasim Madah Shariati: Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Namjoo Street, Rasht, 41938-33697, Iran

Abstract
For safe navigation of ships, it is essential to accurately detect and continuously track surrounding obstacles. The horizon, serving as the boundary between the sky and the sea, plays a crucial role in enabling ships to maintain precise routes and effectively assess the positions of obstacles. Reliable horizon detection is, therefore, highly significant. Conventional horizon detection methods, such as the Hough transform and edge-based approaches, have shown good performance in relatively simple environments. However, their accuracy significantly decreases in realistic maritime environments, which involve complex factors such as terrain, obstacles, and reflections. To address these limitations, this study proposes a novel horizon detection method that fine-tunes the SAM (Segment Anything Model), a deep learning model specifically designed for maritime images. An efficient adapter-based fine-tuning technique was implemented on the SAM' mask decoder, enabling the model to effectively learn the distinct visual and structural characteristics of maritime environments. Experimental evaluations demonstrated that the method combining the SAM fine-tuning with the vertical edge response approach achieved superior performance, significantly reducing height error and slope error by an average of 75.58% and 70.17%, respectively, even in highly complex environments. These findings highlight the superior accuracy and robustness of the proposed method, indicating its substantial potential for practical applications in autonomous navigation systems and enhanced maritime safety.

Key Words
autonomous ship; fine-tuning; horizon detection; maritime obstacle detection; SAM (Segment Anything Model)

Address
Yun-Sik Kim, Ha-Yun Kim and In-Chang Yeo: Department of Naval Architecture and Ocean Engineering, Seoul National University, Republic of Korea
Myung-Il Roh: Department of Naval Architecture and Ocean Engineering, and Research Institute of Marine Systems Engineering, Seoul National University, Republic of Korea


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