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

Publikationer

2023

Active flow control of the airflow of a ship at yaw

Artikel i vetenskaplig tidskrift
2022

Drag reduction of ship airflow using steady Coanda effect

Artikel i vetenskaplig tidskrift

Mer information

Senast uppdaterat

2023-10-02