Ship speed loss estimation using wave spectrum of encounter

Haruka Nakano; Kentaro Kuroki; Yoshiko Sato; Satoshi Koshita; Masahiro Maeda; Kuniaki Matsuura

doi:10.1109/OCEANSKOBE.2018.8559122

Ship speed loss estimation using wave spectrum of encounter

Haruka Nakano, Kentaro Kuroki, Yoshiko Sato, Satoshi Koshita, Masahiro Maeda, Kuniaki Matsuura

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

An accurate estimation of ship speed loss is required to verify ship propulsion performance in actual sea conditions. In this study, ship speed loss was estimated using a neural network. Firstly, the directional spectra were extracted from the ocean wave hindcast database of Japan Weather Association. Then, the spectra were converted into wave spectra of encounter. Secondly, ship speed through water was modeled using ship monitoring data and spectra with a neural network. Thirdly, speed-power curves were estimated using the model. Effects of the wind and waves on ship speed were checked by considering the sensitivity of the model’s outputs. Finally, ship speed loss was calculated using this model. The estimated ship speed loss was consistent with the ship speed loss obtained using ship monitoring data.

Original language	English
Title of host publication	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538616543
DOIs	https://doi.org/10.1109/OCEANSKOBE.2018.8559122
Publication status	Published - Dec 4 2018
Externally published	Yes
Event	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 - Kobe, Japan Duration: May 28 2018 → May 31 2018

Publication series

Name	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018

Other

Other	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Country/Territory	Japan
City	Kobe
Period	5/28/18 → 5/31/18

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Oceanography
Space and Planetary Science
Energy Engineering and Power Technology
Ocean Engineering
Acoustics and Ultrasonics
Instrumentation

Access to Document

10.1109/OCEANSKOBE.2018.8559122

Cite this

Nakano, H., Kuroki, K., Sato, Y., Koshita, S., Maeda, M., & Matsuura, K. (2018). Ship speed loss estimation using wave spectrum of encounter. In 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 Article 8559122 (2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OCEANSKOBE.2018.8559122

Ship speed loss estimation using wave spectrum of encounter. / Nakano, Haruka; Kuroki, Kentaro; Sato, Yoshiko et al.
2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8559122 (2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nakano, H, Kuroki, K, Sato, Y, Koshita, S, Maeda, M & Matsuura, K 2018, Ship speed loss estimation using wave spectrum of encounter. in 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018., 8559122, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018, Institute of Electrical and Electronics Engineers Inc., 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018, Kobe, Japan, 5/28/18. https://doi.org/10.1109/OCEANSKOBE.2018.8559122

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title = "Ship speed loss estimation using wave spectrum of encounter",

abstract = "An accurate estimation of ship speed loss is required to verify ship propulsion performance in actual sea conditions. In this study, ship speed loss was estimated using a neural network. Firstly, the directional spectra were extracted from the ocean wave hindcast database of Japan Weather Association. Then, the spectra were converted into wave spectra of encounter. Secondly, ship speed through water was modeled using ship monitoring data and spectra with a neural network. Thirdly, speed-power curves were estimated using the model. Effects of the wind and waves on ship speed were checked by considering the sensitivity of the model{\textquoteright}s outputs. Finally, ship speed loss was calculated using this model. The estimated ship speed loss was consistent with the ship speed loss obtained using ship monitoring data.",

author = "Haruka Nakano and Kentaro Kuroki and Yoshiko Sato and Satoshi Koshita and Masahiro Maeda and Kuniaki Matsuura",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 ; Conference date: 28-05-2018 Through 31-05-2018",

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doi = "10.1109/OCEANSKOBE.2018.8559122",

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AU - Nakano, Haruka

AU - Kuroki, Kentaro

AU - Sato, Yoshiko

AU - Koshita, Satoshi

AU - Maeda, Masahiro

AU - Matsuura, Kuniaki

PY - 2018/12/4

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N2 - An accurate estimation of ship speed loss is required to verify ship propulsion performance in actual sea conditions. In this study, ship speed loss was estimated using a neural network. Firstly, the directional spectra were extracted from the ocean wave hindcast database of Japan Weather Association. Then, the spectra were converted into wave spectra of encounter. Secondly, ship speed through water was modeled using ship monitoring data and spectra with a neural network. Thirdly, speed-power curves were estimated using the model. Effects of the wind and waves on ship speed were checked by considering the sensitivity of the model’s outputs. Finally, ship speed loss was calculated using this model. The estimated ship speed loss was consistent with the ship speed loss obtained using ship monitoring data.

AB - An accurate estimation of ship speed loss is required to verify ship propulsion performance in actual sea conditions. In this study, ship speed loss was estimated using a neural network. Firstly, the directional spectra were extracted from the ocean wave hindcast database of Japan Weather Association. Then, the spectra were converted into wave spectra of encounter. Secondly, ship speed through water was modeled using ship monitoring data and spectra with a neural network. Thirdly, speed-power curves were estimated using the model. Effects of the wind and waves on ship speed were checked by considering the sensitivity of the model’s outputs. Finally, ship speed loss was calculated using this model. The estimated ship speed loss was consistent with the ship speed loss obtained using ship monitoring data.

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M3 - Conference contribution

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Y2 - 28 May 2018 through 31 May 2018

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Ship speed loss estimation using wave spectrum of encounter

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