Weather routing and safe ship handling in the future of shipping

Perera, Lokukaluge P.; Soares, C. Guedes

doi:10.1016/j.oceaneng.2016.09.007

Cited by 71 publications

(32 citation statements)

References 52 publications

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“…In this respect, Padhy et al [13] embodied the Dijkstra [14] algorithm to solve the ship routing problem, while Veneti [15] provided an improved solution based on the exact time-dependent bi-objective shortest path algorithm, to optimize two different conflicting objectives, namely, the fuel consumption and the expected risk along the route. In Reference [16], an overview of weather routing and safe ship handling approaches is presented, addressing the importance of the complementarity between both approaches in such situations and their extensive implementation to achieve optimal and safe navigation conditions in shipping. In Reference [17], some modifications are proposed to find the optimal path in ice-covered waters in order to take into account the possible involvement of an icebreaker.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a New Ship Adaptive Weather Routing Model Based on Seakeeping Analysis and Optimization

Pennino

Gaglione

Innac

et al. 2020

JMSE

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This paper provides a new adaptive weather routing model, based on the Dijkstra shortest path algorithm, aiming to select the optimal route that maximizes the ship performances in a seaway. The model is based on a set of ship motion-limiting criteria and on the weather forecast maps, providing the sea state conditions the ship is expected to encounter along the scheduled route. The new adaptive weather routing model is applied to optimize the scheduled route in the Northern Atlantic Ocean of the S175 containership, assumed as a reference vessel, based on the weather forecast data provided by the Global WAve Model (GWAM). In the analysis, both wave and combined wind/swell wave conditions are embodied to investigate the incidence on the optimum route assessment. Furthermore, the effect of the vessel speed on the optimum route detection is also investigated. Current results clearly show that it is possible to achieve appreciable improvements, up to 50% of the ship seakeeping performances, without excessively increasing the route length and the voyage duration.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a New Ship Adaptive Weather Routing Model Based on Seakeeping Analysis and Optimization

Pennino

Gaglione

Innac

et al. 2020

JMSE

View full text Add to dashboard Cite

show abstract

“…Measurable external disturbances include the wind velocity and wind direction for the system used in this paper. Although systems exist for measuring and estimating the wave spectrum parameters in the vicinity of the vessel [23], [24] and measuring the velocity and direction of the ocean current affecting the hull [25], we limit the environmental sensory equipment to sensors that are currently available in the system. A key assumption at this stage is that the velocities relative to the vessel frame are available without bias.…”

Section: Position Estimation Conceptmentioning

confidence: 99%

Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network

Skulstad

Fossen

et al. 2019

IEEE Robot. Automat. Mag.

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When a ship experiences a loss of position reference systems, the ship's navigation system typically enters a mode known as dead reckoning to maintain an estimate of the position of the ship. Commercial systems perform this task using a state estimator that includes mathematical model knowledge. Such a model is non-trivial to derive and needs tuning if the dynamic properties of the vessel change. To this end we propose to use machine learning to estimate the horizontal velocity of the vessel without the help of position, velocity or acceleration sensors. A simulation study was conducted to show the ability to maintain position estimates during a Global Navigation Satellite System outage. Comparable performance is seen relative to the established Kalman Filter model-based approach.

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“…The time-varying data structure can be used to quantify vessel operational and navigation conditions and ship system health conditions. The same information can be used for vessel energy efficiency [12] and system reliability applications [13,14], were the respective decisions supporting features should be developed (see Figure 1). That has been done by the decision supporting layer of the data handling framework supported by the same data driven models.…”

Section: Figure 1 Data Handling Frameworkmentioning

confidence: 99%

Ship performance and navigation information under high-dimensional digital models

Perera

2019

J Mar Sci Technol

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Future vessels will be facilitated by modern internet of things (IoT) to collect various ship performance and navigation information. Such information is collected as large-scale data sets, so called Big Data and that should be utilized towards digitalization of the shipping industry. However, various data handling challenges are encountered by the shipping industry during the phase of digitalization, onboard as well as onshore. Data driven models, so called digital models, to support data handling frameworks of the shipping industry are proposed by this study. Such models can overcome the respective data handling challenges in shipping, where conventional mathematical models may fail to facilitate. These models can be derived from ship performance and navigation data sets by considering the high dimensional parameter space. Such high dimensional models consist of several data clusters and each data cluster may consist of a possible unique data structure. These data clusters often relate to sub-operational conditions of vessels and ship systems. The identification of the distribution of data clusters and the structure of each data cluster in relation to ship performance and navigation conditions have been done under this study for a selected vessel as the main contribution. Furthermore, the domain knowledge in shipping (i.e. vessel operational and navigation conditions) is also considered during this analysis to interpret the meaning in such digital models.Index Terms-Digitalization, Shipping industry, big data, digital models, data analytics, machine leaning and artificial intelligence.

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Weather routing and safe ship handling in the future of shipping

Cited by 71 publications

References 52 publications

Development of a New Ship Adaptive Weather Routing Model Based on Seakeeping Analysis and Optimization

Development of a New Ship Adaptive Weather Routing Model Based on Seakeeping Analysis and Optimization

Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network

Ship performance and navigation information under high-dimensional digital models

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