Aktiv strömningskontroll genom maskininlärning för minskad energiförbrukning hos fartyg
Forskningsprojekt, 2021
– 2023
This project is for the first time exploring the usage of machine learning to optimize the performance of ships and vessels.
The project is aiming to address the investigation and the control of the air flow surrounding longhaul ships. The external flow has an impact on the total ship resistance and it is also responsible for natural instabilities, created for example over aft frigate deck regions, that influence safety features, such helicopter landing pads, and environmental aspects, such as avoiding smoke intake to the HVAC systems. The main tangible goals are therefore to decrease drag (resistance of motion) by 5% and develop a system which have control over the natural flow instabilities. This will be achieved with a machine learning driven design approach to drive an active flow control system
Deltagare
Rickard Bensow (kontakt)
Chalmers, Mekanik och maritima vetenskaper, Marin teknik
Sinisa Krajnovic
Chalmers, Mekanik och maritima vetenskaper
Kewei Xu
Chalmers, Mekanik och maritima vetenskaper, Marin teknik
Finansiering
Chalmers styrkeområde Transport
Finansierar Chalmers deltagande under 2021–2023
Relaterade styrkeområden och infrastruktur
Hållbar utveckling
Drivkrafter
Transport
Styrkeområden
Energi
Styrkeområden
C3SE (Chalmers Centre for Computational Science and Engineering)
Infrastruktur
Innovation och entreprenörskap
Drivkrafter