Data-driven modelling of ship propulsion and the effect of data pre-processing on the prediction of ship fuel consumption and speed loss

The paper examines data-driven techniques for the modeling of ship propulsion that could support a strategy for the reduction of emissions and be utilized for the optimization of a fleet’s operations. A large, high-frequency and automated collected data set is exploited for producing models that estimate the required shaft power or main engine’s fuel consumption of a container ship sailing under arbitrary conditions. A variety of statistical calculations and algorithms for data processing are implemented and state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. Emphasis is given in the pre-processing of the data and the results indicate that with a proper filtering and preparation stage it is possible to significantly increase the model’s accuracy. Thus, increase our prediction ability and our awareness regarding the ship's hull and propeller actual condition.

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Data-Driven Prediction Method for Truck Fuel Consumption Based on Car Networking

CICTP 2020 ◽

10.1061/9780784483053.055 ◽

2020 ◽

Author(s):

Keke Long ◽

Guanqun Wang ◽

Zhigang Xu ◽

Xiaoguang Yang

Keyword(s):

Fuel Consumption ◽

Prediction Method ◽

Data Driven

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A Data-Driven Fuel Consumption Estimation Model for Airspace Redesign Analysis

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) ◽

10.1109/dasc.2018.8569564 ◽

2018 ◽

Author(s):

Ning Hong ◽

Lishuai Li

Keyword(s):

Fuel Consumption ◽

Data Driven ◽

Estimation Model

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Data driven analysis and forecasting of medium and heavy truck fuel consumption

Enterprise Information Systems ◽

10.1080/17517575.2020.1856417 ◽

2020 ◽

pp. 1-22

Author(s):

Thomas Bousonville ◽

David Cheubou Kamga ◽

Thilo Krüger ◽

Martin Dirichs

Keyword(s):

Fuel Consumption ◽

Data Driven ◽

Heavy Truck

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Fingerprinting the ship propulsion system: low hanging fruit or mission impossible?

10.24868/issn.2631-8741.2018.013 ◽

2018 ◽

Author(s):

A Vrijdag ◽

Y Sang

Keyword(s):

Transfer Functions ◽

Full Scale ◽

Propulsion System ◽

Data Driven ◽

Parameter Estimates ◽

System Parameters ◽

Ship Propulsion ◽

Wide Range ◽

System Input ◽

Ship Operation

In this paper the concept of ship propulsion system “fingerprinting” is explored as an alternative for data driven models that require extensive measured datasets collected over long periods of ship operation. As a first exploratory step a model of a ship in bollard pull conditions is linearised and its transfer functions are determined. Subsequently limited experimental data, involving sinusoidal excitation of the system input at a wide range of frequencies, is used to determine the system parameters. The resulting parameter estimates compare well against previously determined values. Although the developed ideas are far from ready to be used on full scale, the authors believe that the approach is promising enough to be developed further towards full scale application.

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Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons

10.36501/0197-9191/21-036 ◽

2021 ◽

Author(s):

Hadi Meidani ◽

◽

Amir Kazemi ◽

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Fuel Consumption ◽

Data Driven ◽

Dynamics Model ◽

Drag Forces ◽

Consumption Reduction ◽

Energy Economy ◽

Computational Fluid Dynamics Cfd ◽

Large Systems

Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.

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Wind Assisted Ship PropulsionTechnologies – Can they Help in Emissions Reduction?

Naše more ◽

10.17818/nm/2021/2.6 ◽

2021 ◽

Vol 68 (2) ◽

pp. 102-109

Keyword(s):

Fuel Consumption ◽

Real Life ◽

Ship Design ◽

International Maritime Organization ◽

Emissions Reduction ◽

Reduce Fuel Consumption ◽

Shipping Industry ◽

Ship Propulsion ◽

Fuel Savings ◽

The Future

According to International Maritime Organization, emissions coming from global shipping are expected to increase 50% to 250% by the year 2050. This concern led to the introduction of various regulations that aims to encourage ship owners and builders to explore innovative renewable technologies. The main focus of this article is on wind-assisted ship propulsion technologies, as a complement to ship propulsion, such as rigid sail, soft sail, wing sail, kite, and Flettner rotor. These technologies are not widely accepted because ship owners have doubts due to the lack of real-life results and their implementation and efficiency greatly depends on ship design and purpose. This article shows the progress in the field of wind-assisted ship propulsion in the last 15 years which proved the concept as they have the potential to reduce fuel consumption, thus emissions, by double digits. The conclusion is drawn, from fuel savings percentages, that rotor and soft sails technologies have great potential in the future of the shipping industry.

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The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Energies ◽

10.3390/en14164827 ◽

2021 ◽

Vol 14 (16) ◽

pp. 4827

Author(s):

Tomasz Cepowski ◽

Paweł Chorab

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fuel Consumption ◽

Design Stage ◽

Accurate Estimation ◽

Ship Propulsion ◽

Fuel Prices ◽

Bulk Carriers ◽

Artificial Neural ◽

Engine Power

The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

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