Modeling of Ship Propulsion Performance

Benjamin Pjedsted Pedersen; Jan Larsen

Modeling of Ship Propulsion Performance

Benjamin Pjedsted Pedersen, Jan Larsen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature, from four different loading conditions has been used to train a neural network for prediction of propulsion power. The network was able to predict the propulsion power with accuracy between 0.8-2.8%, which is about the same accuracy as for the measurements. The methods developed are intended to support the performance monitoring system SeaTrend® developed by FORCE Technology (FORCE (2008)).

Original language	English
Title of host publication	World Maritime Technology Conference
Volume	Session 4A
Publication date	2009
Publication status	Published - 2009
Event	Modeling of Ship Propulsion Performance - Duration: 1 Jan 2009 → …

Conference

Conference	Modeling of Ship Propulsion Performance
Period	01/01/2009 → …

Keywords

propulsion
ship
performance
maritime
Neural network

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title = "Modeling of Ship Propulsion Performance",

abstract = "Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature, from four different loading conditions has been used to train a neural network for prediction of propulsion power. The network was able to predict the propulsion power with accuracy between 0.8-2.8%, which is about the same accuracy as for the measurements. The methods developed are intended to support the performance monitoring system SeaTrend{\textregistered} developed by FORCE Technology (FORCE (2008)).",

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author = "Pedersen, {Benjamin Pjedsted} and Jan Larsen",

year = "2009",

language = "English",

volume = "Session 4A",

booktitle = "World Maritime Technology Conference",

note = "Modeling of Ship Propulsion Performance ; Conference date: 01-01-2009",

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AU - Pedersen, Benjamin Pjedsted

AU - Larsen, Jan

PY - 2009

Y1 - 2009

N2 - Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature, from four different loading conditions has been used to train a neural network for prediction of propulsion power. The network was able to predict the propulsion power with accuracy between 0.8-2.8%, which is about the same accuracy as for the measurements. The methods developed are intended to support the performance monitoring system SeaTrend® developed by FORCE Technology (FORCE (2008)).

AB - Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature, from four different loading conditions has been used to train a neural network for prediction of propulsion power. The network was able to predict the propulsion power with accuracy between 0.8-2.8%, which is about the same accuracy as for the measurements. The methods developed are intended to support the performance monitoring system SeaTrend® developed by FORCE Technology (FORCE (2008)).

KW - propulsion

KW - ship

KW - performance

KW - maritime

KW - Neural network

M3 - Article in proceedings

VL - Session 4A

BT - World Maritime Technology Conference

T2 - Modeling of Ship Propulsion Performance

Y2 - 1 January 2009

ER -

Modeling of Ship Propulsion Performance

Abstract

Conference

Keywords

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