Practical method for evaluating wind influence on autonomous ship operations (2nd report)

Maki, Atsuo; Maruyama, Yuuki; Dostal, Leo; Sasa, Kenji; Sawada, Ryohei; Wakita, Kouki

doi:https://doi.org/10.1007/s00773-024-01025-z

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

Maki, Atsuo; Maruyama, Yuuki; Dostal, Leo et al.

Journal of Marine Science and Technology, 2024, 29(4), 876-884

Number of Access:440（2025-08-14 04:32 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/98273

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Title	Practical method for evaluating wind influence on autonomous ship operations (2nd report)
Creator	NRID 1000050556496 1000050556496 ORCID 0000-0002-2819-1297 0000-0002-2819-1297 researchmap permalink atsuo_maki atsuo_maki Scopus Author ID 24503751100 24503751100 CreatorName Maki, Atsuo
	CreatorName Maruyama, Yuuki
	CreatorName Dostal, Leo
	NRID 1000010360330 1000010360330 CreatorName Sasa, Kenji
	NRID 1000000825911 1000000825911 CreatorName Sawada, Ryohei
	ORCID 0000-0003-0363-1396 0000-0003-0363-1396 CreatorName Wakita, Kouki
Subject	Estimation of drift and diffusion
	Liner filter
	Stochastic differential equation
	Wind speed and direction
Description	The version of record of this article, first published in Journal of Marine Science and Technology, is available online at Publisher’s website: https://doi.org/10.1007/s00773-024-01025-z.
Abstract	Recently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.
Publisher	Springer
Source Title	Journal of Marine Science and Technology
Volume (Issue)	29(4)
Page	876-884
Date of Issued	2024-08-28
Language	English
Handle URL	https://hdl.handle.net/11094/98273
PISSN	09484280
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1007/s00773-024-01025-z
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Journal of Marine Science and Technology. 2024, 29(4), p. 876-884
DC.title	Practical method for evaluating wind influence on autonomous ship operations (2nd report)
DC.creator	Maki, Atsuo
	Maruyama, Yuuki
	Dostal, Leo
	Sasa, Kenji
	Sawada, Ryohei
	Wakita, Kouki
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-08-28
DC.identifier	https://doi.org/10.1007/s00773-024-01025-z
DC.subject	Estimation of drift and diffusion
	Liner filter
	Stochastic differential equation
	Wind speed and direction
DC.description	The version of record of this article, first published in Journal of Marine Science and Technology, is available online at Publisher’s website: https://doi.org/10.1007/s00773-024-01025-z.
DCTERMS.abstract	Recently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98273
citation_title	Practical method for evaluating wind influence on autonomous ship operations (2nd report)
citation_author	Maki, Atsuo
	Maruyama, Yuuki
	Dostal, Leo
	Sasa, Kenji
	Sawada, Ryohei
	Wakita, Kouki
citation_publisher	Springer
citation_language	English
citation_date	2024-08-28
citation_journal_title	Journal of Marine Science and Technology
citation_volume	29
citation_issue	4
citation_firstpage	876
citation_lastpage	884
citation_doi	https://doi.org/10.1007/s00773-024-01025-z
citation_public_url	https://hdl.handle.net/11094/98273

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

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