Eco-friendly and customer-driven Sail plan optimisation service

Información del proyecto

EcoSail

Identificador del acuerdo de subvención: 820593

DOI

10.3030/820593

Proyecto cerrado

Fecha de la firma de la CE 25 Junio 2018

Fecha de inicio 1 Noviembre 2018

Fecha de finalización 31 Octubre 2021

Financiado con arreglo a

• Horizon 2020 Framework Programme

Coste total

€ 3 903 142,50

Aportación de la UE

€ 2 986 937,25

Coordinado por

O.M. OFFSHORE MONITORING LIMITED
Cyprus

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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

Publicaciones

Thesis and dissertations (2)

Development of Speed-power Performance Models for Ship Voyage Optimization

Autores: Xiao Lang
Publicado en: Licentiate Thesis, Edición 2021, January, 2021, Página(s) 120
Editor: Chalmers University of Technology

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

Autores: Wang, Helong
Publicado en: Book (dissertation), Edición 1, 2020, Página(s) 68, ISBN 978-91-7905-259-1
Editor: Chalmers University of Technology

Peer reviewed articles (5)

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

Autores: Helong Wang, Xiao Lang, and Wengang Mao
Publicado en: Transportation Research Part D: Transport and Environment, 2021, Página(s) 102670, ISSN 1361-9209
Editor: Pergamon Press Ltd.
DOI: 10.1016/j.trd.2020.102670

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

Autores: Helong Wang, Xiao Lang, Wengang Mao, Di Zhang, Gaute Storhaug
Publicado en: Ocean Engineering, Edición 200, 2020, Página(s) 107063, ISSN 0029-8018
Editor: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2020.107063

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

Autores: Helong Wang, Wengang Mao, Leif Eriksson
Publicado en: TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Edición 14/2, 2020, Página(s) 301-308, ISSN 2083-6473
Editor: 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

Autores: Xiao Lang, Wengang Mao
Publicado en: Ocean Engineering, Edición 209, 2020, Página(s) 107494, ISSN 0029-8018
Editor: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2020.107494

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

Autores: Xiao Lang, Helong Wang, Wengang Mao, Naoki Osawa
Publicado en: Journal of Marine Science and Technology, 2020, ISSN 0948-4280
Editor: 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

Autores: Xiao Lang, Wengang Mao
Publicado en: Proceedings of the International Offshore and Polar Engineering Conference, 2020
Editor: The International Society of Offshore and Polar Engineers

Multimodel Superensembling Using Convolutional Neural Networks

Autores: 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.
Publicado en: The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS), Edición 16 - 17 June 2020, 2019
Editor: Swedish Artificial Intelligence Society (SAIS)

Multimodel Superensembling Using Convolutional Neural Networks

Autores: E Larsson, A Littardi, M Nicolaou, L Eriksson and W Qazi
Publicado en: SAIS, 2021, Página(s) 1-4
Editor: Chalmers University of Technology

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

Autores: Wengang Mao, Naoki Osawa, Shengzheng Wang, Di Zhang
Publicado en: . Proceeding of the annual meeting of the Japan Society of Naval Architects and Ocean Engineers, 2019
Editor: Japan Society of Naval Architects and Ocean Engineers