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Physics-based shaft power prediction for large merchant ships using neural networks

Physics-based shaft power prediction for large merchant ships using neural networks
Physics-based shaft power prediction for large merchant ships using neural networks
There are currently over 100,000 merchant ships operating globally. To reduce emissions requires predicting and benchmarking the power they use. This is relatively straightforward for calm conditions but becomes almost impossible in larger waves. Design power predictions for ships in weather are typically derived by applying a ‘margin’ onto a reference ‘calm water power’. This is of questionable accuracy as the techniques available to estimate these ‘margins’ are inaccurate. To improve the accuracy and flexibility of such predictions this paper investigates the use of neural networks. For this, 27 months of continuous monitoring data are used from 3 vessels of the same design, sampled every five minutes. Multiple network sizes are considered and evaluated to determine the quantity and quality of data required for predictions. A key aspect is determining network architectures optimised not just for accuracy, but that give close relationships between the input variables and shaft power. Predictions are compared to the results of a regression, the conventional tool to determine shaft power from measured full-scale data from ships. The predictions from this network are similar in accuracy to those of standard practices, with an error less than 10%, but the scope for further improvements is large.
Machine Learning, Shaft Power Prediction, function approximation, Physics-based learning, Artificial Neural Networks
0029-8018
92-104
Parkes, Amy, Isabel
9fbc0481-7bcf-4d15-8474-4df77d4338ef
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Hudson, Dominic
3814e08b-1993-4e78-b5a4-2598c40af8e7
Parkes, Amy, Isabel
9fbc0481-7bcf-4d15-8474-4df77d4338ef
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Hudson, Dominic
3814e08b-1993-4e78-b5a4-2598c40af8e7

Parkes, Amy, Isabel, Sobey, Adam and Hudson, Dominic (2018) Physics-based shaft power prediction for large merchant ships using neural networks. Ocean Engineering, 166, 92-104. (doi:10.1016/j.oceaneng.2018.07.060).

Record type: Article

Abstract

There are currently over 100,000 merchant ships operating globally. To reduce emissions requires predicting and benchmarking the power they use. This is relatively straightforward for calm conditions but becomes almost impossible in larger waves. Design power predictions for ships in weather are typically derived by applying a ‘margin’ onto a reference ‘calm water power’. This is of questionable accuracy as the techniques available to estimate these ‘margins’ are inaccurate. To improve the accuracy and flexibility of such predictions this paper investigates the use of neural networks. For this, 27 months of continuous monitoring data are used from 3 vessels of the same design, sampled every five minutes. Multiple network sizes are considered and evaluated to determine the quantity and quality of data required for predictions. A key aspect is determining network architectures optimised not just for accuracy, but that give close relationships between the input variables and shaft power. Predictions are compared to the results of a regression, the conventional tool to determine shaft power from measured full-scale data from ships. The predictions from this network are similar in accuracy to those of standard practices, with an error less than 10%, but the scope for further improvements is large.

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Accepted/In Press date: 31 July 2018
e-pub ahead of print date: 15 August 2018
Published date: 15 October 2018
Keywords: Machine Learning, Shaft Power Prediction, function approximation, Physics-based learning, Artificial Neural Networks

Identifiers

Local EPrints ID: 423024
URI: http://eprints.soton.ac.uk/id/eprint/423024
ISSN: 0029-8018
PURE UUID: ef23ef7c-e1d5-4f5b-9302-387f1f023ecc
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338
ORCID for Dominic Hudson: ORCID iD orcid.org/0000-0002-2012-6255

Catalogue record

Date deposited: 10 Aug 2018 16:30
Last modified: 07 Oct 2020 06:12

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