Eco-friendly and customer-driven Sail plan optimisation service

Informazioni relative al progetto

EcoSail

ID dell’accordo di sovvenzione: 820593

DOI

10.3030/820593

Progetto chiuso

Data della firma CE 25 Giugno 2018

Data di avvio 1 Novembre 2018

Data di completamento 31 Ottobre 2021

Finanziato da

• Horizon 2020 Framework Programme

Costo totale

€ 3 903 142,50

Contributo UE

€ 2 986 937,25

Coordinato da

O.M. OFFSHORE MONITORING LIMITED
Cyprus

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Websites, patent fillings, videos etc. (3)

4 EcoSail service information/demo videos; final

4 EcoSail service information/demo videos; final (M26)

Report on EcoSail ‘Training Portal’
4 EcoSail service information/demo videos; interim

4 EcoSail service information/demo videos; interim (M14)

Demonstrators, pilots, prototypes (2)

Sail plan training programme for ship operators
EcoSail project webpage

Pubblicazioni

Thesis and dissertations (2)

Development of Speed-power Performance Models for Ship Voyage Optimization

Autori: Xiao Lang
Pubblicato in: Licentiate Thesis, Numero 2021, January, 2021, Pagina/e 120
Editore: Chalmers University of Technology

Development of voyage optimization algorithms for sustainable shipping and their impact to ship design

Autori: Wang, Helong
Pubblicato in: Book (dissertation), Numero 1, 2020, Pagina/e 68, ISBN 978-91-7905-259-1
Editore: Chalmers University of Technology

Peer reviewed articles (5)

Voyage optimization combining genetic algorithm and dynamic programming for fuel/emissions reduction

Autori: Helong Wang, Xiao Lang, and Wengang Mao
Pubblicato in: Transportation Research Part D: Transport and Environment, 2021, Pagina/e 102670, ISSN 1361-9209
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.trd.2020.102670

Effectiveness of 2D optimization algorithms considering voluntary speed reduction under uncertain metocean conditions

Autori: Helong Wang, Xiao Lang, Wengang Mao, Di Zhang, Gaute Storhaug
Pubblicato in: Ocean Engineering, Numero 200, 2020, Pagina/e 107063, ISSN 0029-8018
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2020.107063

Efficiency of a Voluntary Speed Reduction Algorithm for a Ship’s Great Circle Sailing

Autori: Helong Wang, Wengang Mao, Leif Eriksson
Pubblicato in: TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Numero 14/2, 2020, Pagina/e 301-308, ISSN 2083-6473
Editore: TransNav, Faculty of Navigation
DOI: 10.12716/1001.14.02.04

A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements

Autori: Xiao Lang, Wengang Mao
Pubblicato in: Ocean Engineering, Numero 209, 2020, Pagina/e 107494, ISSN 0029-8018
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2020.107494

Impact of ship operations aided by voyage optimization on a ship’s fatigue assessment

Autori: Xiao Lang, Helong Wang, Wengang Mao, Naoki Osawa
Pubblicato in: Journal of Marine Science and Technology, 2020, ISSN 0948-4280
Editore: Springer Verlag
DOI: 10.1007/s00773-020-00769-8

Conference proceedings (4)

A Simplified Ship Wave Induced Added Resistance Calculation Method and Full-scale Measurements Validation in Head Sea

Autori: Xiao Lang, Wengang Mao
Pubblicato in: Proceedings of the International Offshore and Polar Engineering Conference, 2020
Editore: The International Society of Offshore and Polar Engineers

Multimodel Superensembling Using Convolutional Neural Networks

Autori: Erik Larsson (1), Andrea Littardi (2, 3), Mihalis Nicolaou (2), Leif E. B. Eriksson (1), and Waqas Qazi (3) 1 Chalmers University of Technology, Sweden 2 The Cyprus Institute 3 Offshore Monitoring Ltd.
Pubblicato in: The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS), Numero 16 - 17 June 2020, 2019
Editore: Swedish Artificial Intelligence Society (SAIS)

Multimodel Superensembling Using Convolutional Neural Networks

Autori: E Larsson, A Littardi, M Nicolaou, L Eriksson and W Qazi
Pubblicato in: SAIS, 2021, Pagina/e 1-4
Editore: Chalmers University of Technology

A Spatio-Temporal statistical model to simulate long-term wave environments along ship routes

Autori: Wengang Mao, Naoki Osawa, Shengzheng Wang, Di Zhang
Pubblicato in: . Proceeding of the annual meeting of the Japan Society of Naval Architects and Ocean Engineers, 2019
Editore: Japan Society of Naval Architects and Ocean Engineers