The project aims at developing new methodologies for the generation of
digital twin models to support the design and operation of ships and
offshore structures (in particular, large offshore wind turbines). The
selected candidate will investigate the application of model order reduction
and deep learning techniques for the prediction of aspects such as
seakeeping, power optimization including added resistance effects or
structural response in waves. The methodology to be developed will be
conceived to be applied throughout the whole lifecycle of the ship or
platform. The project outcome will contribute to the research activities in
the CP related to the design of ships and marine structures with improved
performance and environmental characteristics.
The validation and demonstration of the methodologies to be developed will
also be a main objective of the project. For this purpose, the work will
include the active collaboration with different companies as well as various
research groups within CIMNE and worldwide.
References
Chinesta, F., Ladeveze, P. & Cueto, E. A Short Review on Model Order
Reduction Based on Proper Generalized Decomposition. Arch Computat Methods
Eng 18, 395 (2011). https://doi.org/10.1007/s11831-011-9064-7
Fonseca, I. A. & Gaspar, H. M., Challenges when creating a cohesive digital
twin ship: a data modelling perspective. Ship Technology Research, 1-14
(2020). https://doi.org/10.1080/09377255.2020.1815140
Coraddu, L. Oneto, F. Baldi, F. Cipollini, M. Atlar & S. Savio, Data-driven
ship digital twin for estimating the speed loss caused by the marine
fouling, Ocean Engineering, 186, 106063 (2019).
https://doi.org/10.1016/j.oceaneng.2019.05.045.
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