Tavakoli, Sasan, Singh, Mansi, Hosseinzadeh, Saeed, Hu, Zhengyu, Shao, Yaniln, Wang, Shan, Huang, Luofeng, Grammatikopoulos, Apostolos, Li, Yuzhu Pearl, Khojasteh, Danial, Liu, Jin, Dolatshah, Azam, Cheng, Hui and Hirdaris, Spyros (2025) A review of flexible fluid-structure interactions in the ocean: progress, challenges, and future directions. Ocean Engineering, 342 (Pt. 1), [122545]. (doi:10.1016/j.oceaneng.2025.122545).
Abstract
Flexible Fluid-Structure Interaction (FFSI) has emerged as an important, but challenging research direction in modern ocean engineering. This line of research gradually evolved in response to the pressing need to model the dynamic responses of ships and marine structures to sea loads; to predict the performance of flexible marine propellers, energy converters, and coastal protection systems; and to understand the mutual interactions between sea ice, marine vegetation, and mud with oceanic and coastal processes occurring near the surface and seabed. This review presents the state of knowledge and art of modelling of FFSI in the maritime environment, tracing research progress from early physical tests to high-fidelity computational ones emerged recently. Flexible wave–structure interaction, global ship hydroelasticity, hydroelastic slamming, flexible marine propellers, vegetation dynamics, and wave–mud interactions are covered. Limitations and strengths of existing models, and the challenges that remain are discussed in-depth, and it is concluded that FFSI-based research in ocean engineering has very well grown, though some gaps are still open. In specific, hydroelastic effects are still overlooked in the design practices and classification rules do not fully incorporate them, and there are still concerns regarding uncertainties related to FFSI modelling of flexible slamming, dynamic of flexible marine vegetation, and wave-mud interactions. Hence, future research must bridge computational modelling with real-world applications, expand benchmarking coverage for marine engineering problem, and incorporate AI-based methods for modelling FFSI problems, predicting related dynamic responses, or accelerating simulations.
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