A ship digital twin for safe and sustainable ship operations
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Zhang, Mingyang | en_US |
| dc.contributor.author | Hirdaris, Spyros | en_US |
| dc.contributor.author | Tsoulakos, Nikolaos | en_US |
| dc.contributor.department | Department of Energy and Mechanical Engineering | en |
| dc.contributor.groupauthor | Marine and Arctic Technology | en |
| dc.contributor.organization | Laskaridis Shipping Co. Ltd. | en_US |
| dc.date.accessioned | 2023-12-11T09:44:50Z | |
| dc.date.available | 2023-12-11T09:44:50Z | |
| dc.date.issued | 2023-10-20 | en_US |
| dc.description.abstract | This paper presents a novel digital twin that can predict ship motions and fuel consumption in real operational conditions. The analysis is based on two optimal Deep Learning Models (DLM) namely (a) a transformer neural network used for the analysis of ship motions and (b) a Long Short-Term Memory (LSTM) network for the prediction of ship fuel consumption. Comparisons of results against sea trial data suggest that subject to further testing and validation DLM could be used as part of a digital twin framework for safe and sustainable ship operations. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 5 | |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Zhang, M, Hirdaris, S & Tsoulakos, N 2023, 'A ship digital twin for safe and sustainable ship operations', BUILding a DIgital Twin, Rome, Italy, 19/10/2023 - 20/10/2023 pp. 71 - 74. | en |
| dc.identifier.other | PURE UUID: 91a4109f-9610-4ce1-b536-217ba70160e0 | en_US |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/91a4109f-9610-4ce1-b536-217ba70160e0 | en_US |
| dc.identifier.other | PURE LINK: http://inm.cnr.it/buildit2023/ | en_US |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/129006751/23_zhang.pdf | en_US |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/124855 | |
| dc.identifier.urn | URN:NBN:fi:aalto-202312117223 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | BUILding a DIgital Twin: BUILding a DIgital Twin | en |
| dc.relation.ispartof | pp. 71 - 74 | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | digital twins | en_US |
| dc.subject.keyword | ship motions | en_US |
| dc.subject.keyword | ship fuel consumption | en_US |
| dc.subject.keyword | big data science | en_US |
| dc.subject.keyword | deep learning | en_US |
| dc.title | A ship digital twin for safe and sustainable ship operations | en |
| dc.type | Abstract | fi |
| dc.type.version | publishedVersion |