Three-dimensional Analysis of Wave Transmission and Sheltered Area of Floating Breakwaters

Abstract

The growing demand for renewable energy has driven advancements in ocean exploration technologies involving floating structures. These structures are exposed to waves, requiring careful design to ensure safety. To enhance protection, the present research focuses on floating breakwaters designed to mitigate wave action. When considering regular waves, the wave transmission coefficient (K_t) for two-dimensional (2D) box-type floating breakwaters is well-defined in the literature. However, the wave transmission coefficient computation for three-dimensional (3D) floating breakwaters still requires a standardized method. In this context, this study conducts a parametric analysis of a box-type floating breakwater, using numerical models based on potential flow theory to determine the 3D wave transmission coefficient. Results reveal that, unlike 2D cases, different K_t levels emerge for a given 3D scenario. These findings suggest that breakwaters featuring high relative breakwater beam ratios are likely to present convergent mean wave transmission coefficients. Furthermore, the research demonstrates that the wave-shelter area is highly dependent on the breakwater beam ratio, with larger ratios leading to lower K_t levels. The sheltered area changes exponentially with the K_t levels. In addition, a practical application is introduced, leveraging machine learning techniques to predict the wave-shelter area and optimize breakwater dimensions. The proposed design minimizes construction costs while ensuring effective wave attenuation for a one megawatt-peak floating solar photovoltaic system. These findings enhance understanding of the wave transmission coefficient for 3D floating breakwaters, highlighting that variations in breakwater dimensions and wave conditions significantly influence the sheltered area, which impacts the protection of offshore structures.

Keywords

Breakwater / Wave transmission coefficient / Machine learning / Offshore structure protection / Renewable energy

References

[1] Ancellin M, Dias F. Capytaine: a python-based linear potential flow solver. Journal of Open Source Software, 2019, 4(36): 1341.

[2] Blank J, Deb K. Pymoo: multi-objective optimization in python. IEEE Access, 2020, 8: 89497-89509.

[3] Breiman L, Friedman JH, Olshen RA, Stone CJClassification And Regression Trees (1), 2017.

[4] Bureau VeritasHydrostar for experts, user manual (7.3), 2024

[5] Coppersmith D, Hong SJ, Hosking JRM. Partitioning nominal attributes in decision trees. Data Mining and Knowledge Discovery, 1999, 3(2): 197-217.

[6] Costa DDO, Fernandes AC, Sales Junior JS. Optimization of system of oscillating water columns to mitigate pitch response of fpso platforms submitted to head waves. Ocean Engineering, 2023, 286: 115729.

[7] Dai J, Wang CM, Utsunomiya T, Duan W. Review of recent research and developments on floating breakwaters. Ocean Engineering, 2018, 158: 132-151.

[8] De Oliveira Costa D, Sales Junior JS, Fernandes AC. Oscillating water column motion inside circular cylindrical structures. Volume 10: Ocean Renewable Energy, 2019, 10: V010T09A040

[9] Diamantoulaki I, Angelides DC. Analysis of performance of hinged floating breakwaters. Engineering Structures, 2010, 32(8): 2407-2423.

[10] Dong G, Zheng YN, Li Y, Teng B, Teng B, Teng B, Guan C, Lin DF. Experiments on wave transmission coefficients of floating breakwaters. Ocean Engineering, 2008

[11] Drimer N, Agnon Y, Stiassnie M. A simplified analytical model for a floating breakwater in water of finite depth. Applied Ocean Research, 1992, 14(1): 33-41.

[12] Elchahal G, Younes R, Lafon P. Parametrical and motion analysis of a moored rectangular floating breakwater. Journal of Offshore Mechanics and Arctic Engineering-Transactions of The Asme, 2009

[13] Ferreira R d S, Lima JVP d, Caprace J-D. Comparative analysis of machine learning prediction models of container ships propulsion power. Ocean Engineering, 2022, 255: 111439.

[14] The Annals of Statistics, 2001, 29(5

[15] Guo W, Zou J, He M, Mao H, Liu Y. Comparison of hydrodynamic performance of floating breakwater with taut, slack, and hybrid mooring systems: an sph-based preliminary investigation. Ocean Engineering, 2022, 258: 111818.

[16] Kramer O. Scikit-learn. Machine Learning for Evolution Strategies, 2016, 20: 45-53

[17] Kurnia R, Ducrozet G. NEMOH: open-source boundary element solver for computation of first- and second-order hydrodynamic loads in the frequency domain. Computer Physics Communications, 2023, 292: 108885.

[18] Leite B, Costa AOSD, Costa Junior EFD. Multi-objective optimization of adiabatic styrene reactors using generalized differential evolution 3 (gde3). Chemical Engineering Science, 2023, 265: 118196.

[19] Liang J, Liu Y, Chen Y, Li A. Experimental study on hydrodynamic characteristics of the box-type floating breakwater with different mooring configurations. Ocean Engineering, 2022, 254: 111296.

[20] Loukogeorgaki E, Angelides DC. Stiffness of mooring lines and performance of floating breakwater in three dimensions. Applied Ocean Research, 2005, 27(4–5): 187-208.

[21] Loukogeorgaki E, Yagci O, Sedat Kabdasli M. 3D experimental investigation of the structural response and the effectiveness of a moored floating breakwater with flexibly connected modules. Coastal Engineering, 2014, 91: 164-180.

[22] Macagno EOExperimental study of the effects of the passage of a wave beneath an obstacle, 1953

[23] Mei CC, Black JL. Scattering of surface waves by rectangular obstacles in waters of finite depth. Journal of Fluid Mechanics, 1969, 38(3): 499-511.

[24] Mohapatra SC, Da Silva Bispo IB, Guo Y, Guedes Soares C. Analysis of wave-induced forces on a floating rectangular box with analytical and numerical approaches. Journal of Marine Science and Application, 2024, 23(1): 113-126.

[25] Newman JN. Propagation of water waves past long two-dimensional obstacles. Journal of Fluid Mechanics, 1965, 23(1): 23.

[26] Newman JNMarine hydrodynamics, 201740th anniversary edition

[27] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Müller A, Nothman J, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, DuchesnayScikit-learn: machine learning in python, 2012

[28] Reddy MJ, Kumar DN. Multiobjective differential evolution with application to reservoir system optimization. Journal of Computing in Civil Engineering, 2007, 21(2): 136-146.

[29] Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the rprop algorithm. IEEE International Conference on Neural Networks, 1993586-591.

[30] Sawaragi TCoastal engineering-waves, beaches, wave-structure interactions, 1995

[31] Stoker JJWater waves, the mathematical theory with applications, 1957

[32] Storn R, Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341-359.

[33] Tavakoli S, Khojasteh D, Haghani M, Hirdaris S. A review on the progress and research directions of ocean engineering. Ocean Engineering, 2023, 272: 113617.

[34] WAMIT IncWAMIT, 1999

RIGHTS & PERMISSIONS

The Author(s)