Ships and Offshore Structures

Papers
(The H4-Index of Ships and Offshore Structures is 14. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-04-01 to 2025-04-01.)
ArticleCitations
Optimal design of a single-point electromechanical cable subsurface mooring39
Numerical analysis and performance assessment of trapezoidal oscillating water columns36
Non-linear response of a moored LNG ship subjected to regular waves35
Analysis of emergency response and rescue operation using fuzzy expert system27
Numerical simulation study of ship-ice interaction based on multi-fracture strength cohesive element model24
Hydrodynamic analysis of a semi-submersible aquaculture platform with mesh grouping impact23
Method for securing minimum spacing distance during auto routing on ships using bounding box method22
Characterisation and collapse analysis of composite egg-shaped pressure housings: experimental and numerical insights22
Experimental and numerical investigation of double-stepped hull and optimisation of the distance between steps in calm water via Response Surface Design Analysis (DOE method)20
Hydrodynamic evaluation and parametric analysis of stern flap impact on performance for high-speed displacement vessels with transom stern16
An autonomous location prediction model for maritime transport applications: a case study of Persian Gulf16
Numerical investigation of the hull girder ultimate strength under realistic cyclic loading derived from long-term hydroelastic analysis15
Numerical investigation of solitary waves effects on the hydrodynamic characteristics of twin-plate breakwaters15
Procedure for determining design accidental loads in liquified-natural-gas-fuelled ships under explosion using a computational-fluid-dynamics-based simulation approach14
Adaptive trajectory tracking control for safe navigation of underactuated hovercraft with state-constraints14
Vibration prediction of offshore wind turbines based on long short-term memory network14
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