A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

Ghalib Taimuri; Mingyang Zhang; Spyros Hirdaris

doi:10.5957/SMC-2022-012

A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

Ghalib Taimuri, Mingyang Zhang, Spyros Hirdaris

Energia- ja konetekniikan laitos

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Konferenssiesitys › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

This paper presents a rapid method for the evaluation of ship grounding risk and the estimation of avoidance action in real operational conditions. The approach makes use of big data analytics from Automatic Identification System (AIS), nowcast and General Bathymetric Chart of the Oceans (GEBCO) to generate potential grounding scenarios. Following the identification of potential grounding scenarios, a Fluid Structure Interaction (FSI) model is adopted to simulate grounding avoidance actions that account for the influence of surrounding water and ship controlling devices in 6- DoF. Application for the case of a passenger ship operating under ice free conditions in the Gulf of Finland demonstrates the potential of the method for the development of improved decision support systems and operational practices.

Alkuperäiskieli	Englanti
DOI - pysyväislinkit	https://doi.org/10.5957/SMC-2022-012
Tila	Julkaistu - 21 syysk. 2022
OKM-julkaisutyyppi	Ei sovellu
Tapahtuma	SNAME Maritime Convention - Houston, Yhdysvallat Kesto: 26 syysk. 2022 → 29 syysk. 2022 https://web.cvent.com/event/742733d5-d310-4259-8003-0d1caacee4f8/summary

Conference

Conference	SNAME Maritime Convention
Lyhennettä	SMC
Maa/Alue	Yhdysvallat
Kaupunki	Houston
Ajanjakso	26/09/2022 → 29/09/2022
www-osoite	https://web.cvent.com/event/742733d5-d310-4259-8003-0d1caacee4f8/summary

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

Pääsy asiakirjaan

10.5957/SMC-2022-012

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

FLARE: FLooding Accident REsponse
Hirdaris, S. (Vastuullinen tutkija), Zhang, M. (Projektin jäsen) & Matusiak, J. (Projektin jäsen)
31/05/2019 → 30/11/2022
Projekti: EU: Framework programmes funding

Siteeraa tätä

@conference{4ae0c6903dac4002b48c5d928f3a6ac3,

title = "A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions",

abstract = "This paper presents a rapid method for the evaluation of ship grounding risk and the estimation of avoidance action in real operational conditions. The approach makes use of big data analytics from Automatic Identification System (AIS), nowcast and General Bathymetric Chart of the Oceans (GEBCO) to generate potential grounding scenarios. Following the identification of potential grounding scenarios, a Fluid Structure Interaction (FSI) model is adopted to simulate grounding avoidance actions that account for the influence of surrounding water and ship controlling devices in 6- DoF. Application for the case of a passenger ship operating under ice free conditions in the Gulf of Finland demonstrates the potential of the method for the development of improved decision support systems and operational practices.",

keywords = "6-DoF maneuvering model, big data analytics, grounding risk, Gulf of Finland, machine learning, ship safety, simplified FSI",

author = "Ghalib Taimuri and Mingyang Zhang and Spyros Hirdaris",

note = "Funding Information: All authors acknowledge funding received from the EU Horizon 2020 project on FLooding Accident REsponse (FLARE – Contract No. 814753) & Merenkulun S{\"a}{\"a}ti{\"o}, Finland (https://www.merenkulunsaatio.fi/etusivu/). Publisher Copyright: Copyright {\textcopyright} 2022 Society of Naval Architects and Marine Engineers (SNAME); SNAME Maritime Convention, SMC ; Conference date: 26-09-2022 Through 29-09-2022",

year = "2022",

month = sep,

day = "21",

doi = "10.5957/SMC-2022-012",

language = "English",

url = "https://web.cvent.com/event/742733d5-d310-4259-8003-0d1caacee4f8/summary",

}

TY - CONF

T1 - A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

AU - Taimuri, Ghalib

AU - Zhang, Mingyang

AU - Hirdaris, Spyros

N1 - Funding Information: All authors acknowledge funding received from the EU Horizon 2020 project on FLooding Accident REsponse (FLARE – Contract No. 814753) & Merenkulun Säätiö, Finland (https://www.merenkulunsaatio.fi/etusivu/). Publisher Copyright: Copyright © 2022 Society of Naval Architects and Marine Engineers (SNAME)

PY - 2022/9/21

Y1 - 2022/9/21

N2 - This paper presents a rapid method for the evaluation of ship grounding risk and the estimation of avoidance action in real operational conditions. The approach makes use of big data analytics from Automatic Identification System (AIS), nowcast and General Bathymetric Chart of the Oceans (GEBCO) to generate potential grounding scenarios. Following the identification of potential grounding scenarios, a Fluid Structure Interaction (FSI) model is adopted to simulate grounding avoidance actions that account for the influence of surrounding water and ship controlling devices in 6- DoF. Application for the case of a passenger ship operating under ice free conditions in the Gulf of Finland demonstrates the potential of the method for the development of improved decision support systems and operational practices.

AB - This paper presents a rapid method for the evaluation of ship grounding risk and the estimation of avoidance action in real operational conditions. The approach makes use of big data analytics from Automatic Identification System (AIS), nowcast and General Bathymetric Chart of the Oceans (GEBCO) to generate potential grounding scenarios. Following the identification of potential grounding scenarios, a Fluid Structure Interaction (FSI) model is adopted to simulate grounding avoidance actions that account for the influence of surrounding water and ship controlling devices in 6- DoF. Application for the case of a passenger ship operating under ice free conditions in the Gulf of Finland demonstrates the potential of the method for the development of improved decision support systems and operational practices.

KW - 6-DoF maneuvering model

KW - big data analytics

KW - grounding risk

KW - Gulf of Finland

KW - machine learning

KW - ship safety

KW - simplified FSI

UR - http://www.scopus.com/inward/record.url?scp=85133540519&partnerID=8YFLogxK

U2 - 10.5957/SMC-2022-012

DO - 10.5957/SMC-2022-012

M3 - Paper

AN - SCOPUS:85133540519

T2 - SNAME Maritime Convention

Y2 - 26 September 2022 through 29 September 2022

ER -

A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

Abstrakti

Conference

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

FLARE: FLooding Accident REsponse

Siteeraa tätä