Aeronautical, Marine, Automotive and Energy Engineering
Marine and Offshore Engineering
Principal Investigator
Borja Serván
Overview
Research
Staff
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CIMNE’s Naval and Marine Engineering group develops advanced simulation tools for ship and offshore structure design, including AI-driven seakeeping prediction, digital twins, and hydroelastic analysis, with strong expertise in FSI, composites, and IoT platforms.
The Naval and Marine Engineering research group at CIMNE specializes in the development and application of advanced computational methods for the design, analysis, and optimization of marine structures and vessels.
With decades of experience in research and technological development (RTD) projects, the group addresses key challenges in hydrodynamic analysis, ship structure design—including composite materials and fluid-structure interaction—and offshore engineering.
Their multidisciplinary expertise also extends to environmental modelling, artificial intelligence applications, and digital twin technologies.
Current research lines include the development of AI tools for real-time seakeeping prediction, integration of IoT platforms for shipbuilding processes, and hydroelastic solvers for floating wind turbines and marine structures.
The group is actively involved in creating decision support systems and digital solutions to enhance the performance, safety, and sustainability of naval and offshore systems.
Research areas
Hydroelasticity of ships and offshore structures
The numerical simulation of a ship’s structural response is typically addressed by modelling the structure with simplified beam models. And the main reason can be found in the computational cost of the structural solver when solving the fully coupled hydro-elastic problems. We use a reduce order method based on modal matrix reduction is applied to reduce the computational cost of the structural solver. The main idea is to largely reduce the number of degrees of freedom of the structural system by retaining only those modes with significant energy.
Semi-Lagrangian Particle Finite Element (SL-PFEM) Method
Development and application of advanced an efficient computational tools for the fast hydrodynamic analysis of ships and offshore structures. The SL-PFEM combines in an innovative way the best particulars of the finite element method and Lagrangian particles to obtain a numerical method capable of dealing with complex hydrodynamics.
Artificial intelligence for seakeeping of ships
Development of computational tools based on Artificial Intelligence for the ultra-fast prediction of seakeeping of ships. By training artificial neural networks (ANNs) with synthetic data, a set of ANNs have been developed capable of predicting the seakeeping coefficients for conventional monohull ships and with similar accuracy of tradition boundary element codes. The main objective is to provide a computational tool capable of performing massive analysis to be applied for seakeeping optimization during the early stages of design.
Ongoing projects
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