| Url |
https://cimne.com/sgp/rtd/Project.aspx?id=926 |
|
| Acronym |
MLAMAR |
| Project title |
Desarrollo de una estrategia de aprendizaje máquina para el análisis hidroelástico de barcos Development of a machine learning strategy for hydroelastic analysis of ships |
| Reference |
PID2021-126561OB-C31 |
| Principal investigator |
Borja SERVÁN CAMAS - bservan@cimne.upc.edu
|
| Start date |
01/09/2022 |
End date |
31/08/2025 |
| Coordinator |
CIMNE |
| Consortium members |
|
| Program |
P.E. para Impulsar la Investigación Científico-Técnica y su Transferencia |
Call |
Proyectos Generación de Conocimiento 2021 |
| Subprogram |
Subprograma Estatal de Generación de Conocimiento |
Category |
Nacional |
| Funding body(ies) |
MICINN |
Grant |
$121,000.00 |
| Abstract |
The constantly rising demands of ship owners regarding the efficiency and sustainability of ship operation and maintenance of their vessels together with upcoming environmental rules and regulations foster the need of a holistic life-cycle management tools. Ship owners can identify potential opportunities for improved resource efficiency, damage prevention, and CO2 emissions reduction using computer-based life-cycle solutions. We propose the development and implementation of innovative and intelligent numerical tools for the life-cycle management assessment at the ship-design and -operation stages. A prediction tool based on Machine
Learning techniques will be developed in order to evaluate added resistance of ships in waves and improve the hull design in early stages of design . Furthermore, machine learning techniques will be used to develop a trustworthy numerical model for route optimization considering the uncertainty of the sea state, as well as seakeeping and added resistance in waves. And a novel hydroelastic solver based on a structural reduced order model will be implemented for the structural evaluation in the detailed design stage , and for structural health monitoring during the operation stages. All numerical tools will be part of a life-cycle management assessment tool. |
|
Proyecto PID2021-126561OB-C31 financiado por MICIU/AEI/10.13039/501100011033/ y por FEDER, UE
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