Weather Routing Research Articles

An Improved Ship Weather Routing Framework for CII Reduction Accounting for Wind-Assisted Rotors

With the increasingly strict regulations for the energy-saving and emission-reduction technology of ships, minimizing fuel cost and thus reducing the carbon intensity index (CII) is one of the most critical issues in the design and operation of merchant ships. More recently, many wind-assisted devices, such as rotors, wind sails, etc., have been investigated and designed to utilize renewable wind energy. With the equipment of wind-assisted rotors, the optimization of ship routes becomes more important because the effect of this wind-assisted device highly depends on the local wind field along the shipping route. In this paper, an improved ship weather routing framework based on the A* algorithm has been proposed to determine the optimal ship route and ship operations with wind-assisted rotors. The proposed framework effectively utilizes different sources of data, including ship design, weather forecasting and historical sailing information, to produce a better estimation of fuel consumption under the effect of sea states. Several improvements on the classic A* algorithm, including directed searching and three-dimensional extension, are proposed to improve the routing effect and efficiency. Finally, the proposed method was applied to test cases of a VLCC operating from China to the Middle East and the results show that the total fuel consumption could be reduced compared to the minimum distance route.

Towards the development of smart weather routing systems for leisure planing boats

Weather routing (WR) systems are widely adopted in the maritime transport since safety of goods and saving of fuel are crucial for shipping companies. However, the need of protecting the local coasts and reducing CO2 emission is making WR attractive even for the market of leisure boats, especially if comfort and safety are also accounted. In the present study, a prototype of a WR system is presented. The developed tool implements the Dijkstra’s algorithm to find the fuel-optimal path while ensuring comfort and safety. A computationally efficient digital twin of a planing boat, based on a 2D+t model and a powertrain mapped model, has been implemented for the estimation of the fuel consumption and the evaluation of vertical accelerations. The methodology for the acquisition of online weather data is presented, together with the strategy for the assessment of comfort and habitability. The application of the WR tool in a typical path of the Tuscan Archipelago shows improvements in fuel usage and comfort even in moderate weather conditions. The influence of each weather variable on fuel efficiency is assessed, highlighting the requirement of an accurate dynamic modelling. Criteria for the graph design are also outlined, showing a wrong estimation of comfort and fuel usage in case of low-definition graphs. The paper proves the potential and the effectiveness of the developed tool, moving toward a greener and more comfortable navigation in local seas.

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

The International Maritime Organization (IMO) had made effort to reduce the ship’s energy consumption and carbon emission by optimizing the ship’s operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship’s heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report.

Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions

Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions

Local assimilation of wave model predictions for weather routing systems

This work adopts an assimilation scheme to improve the local wave predictions based on measured wave data at a specific location. This is a typical situation for waves measured onboard a ship during its transit are the only source of measurements and therefore can be assimilated in weather forecasts for the ship route. The wave modelling system is based on the WAVEWATCH III model, which is run for a 7-year period between 2010 and 2016. The method is tested by using buoy data of the Azores islands as a proxy for ship measurements. This is chosen because at Azores the weather is representative of the Atlantic Ocean and the buoy is well exposed to storm conditions, without the sheltering effect of other islands. The local assimilation of buoy data is applied to the significant wave height as a post-processing analysis in time. Three methods are considered: The Systematic Error method (SE), the Linear Regression method (LR) and the Kalman Filter (KF). The assimilation techniques show a reduction of the errors in the predicted significant wave height close to the buoy location, indicating that this could be used to assimilate weather forecasts used in weather routing systems.

AIS-Enabled Weather Routing for Cargo Loss Prevention

The operation of any vessel includes risks, such as mechanical failure, collision, property loss, cargo loss, or damage. For modern container ships, safe navigation is challenging as the rate of innovation regarding design, speed profiles, and carrying capacity has experienced exponential growth over the past few years. Prevention of cargo loss in container ship liners is of high importance for the Maritime industry and the waterborne sector as it can lead to potentially disastrous, harmful, or even life-threatening outcomes for the crew, the shipping company, the marine environment, and aqua-culture. With the installment of onboard decision support system(s) (DSS) that will provide the required operational guidance to the vessel’s master, we aim to prevent and overcome such events. This paper explores cargo losses in container ships by employing a novel weather routing optimization DS framework that aims to identify excessive motions and accelerations caused by bad weather at specific times and locations; it also suggests alternative routes and, thus, ultimately prevents cargo loss and damage.

Ship routing optimisation based on forecasted weather data and considering safety criteria

AbstractA weather routing tool is presented based on forecasted weather data along the route and considering safety aspects. The tool aims to determine the optimal path for the minimisation of the fuel oil consumption, ensuring a safe passage. It is developed in MATLAB and considers detailed ship characteristics. Specifically, ship's motions and fuel oil consumption of the main engine during a potential path are estimated. For the latter, a physics-based model for a specific vessel is developed where tools of different level of detail are utilised to calculate the various resistance components. A speed management strategy along the route is specified as well as safety criteria representing acceptable limits of ship's responses. When the set criteria and constraints have been set, a genetic algorithm is used to find the optimal route by means of ship's heading or by considering both heading and ship's power settings as variables to minimise the fuel oil consumption. The search space of the algorithm lies within a predefined envelop, but still the evolutionary optimisation approach used has no pre-assigned values to any possible candidate waypoint.

Distribution of energy efficiency design index for tankers in Indonesia

Greenhouse gas emissions resulting from shipping activities increased by 9% in 2018. Tankers are one of the contributors to total ship emissions. To reduce these emissions, the International Maritime Organization adopted the Green House Gas Strategy by issuing a regulation in the form of the Energy Efficiency Design Index (EEDI). This study aims to obtain the distribution of EEDI tankers in Indonesia. The EEDI calculation uses empirical equations that consider the main engine power, auxiliary engine power, and reference speed. The ship data used are 638 units of Indonesian-flagged tankers, including ship particulars from the classification agency. The results of this study indicate that compliance with the EEDI regulation is 26%. Through the application of engine power limitation technology and innovative technology in the form of weather routing, waste heat recovery, solar panels, and electronically controlled engines, EEDI fulfillment increased to 78%.

VOYAGE OPTIMIZATION WITH WIND PROPULSION

Wind propulsion has been proven to produce significant fuel savings on commercial vessels. The typical savings potential of wind propulsion on conventional cargo ships is 5-25%. On the other hand, voyage optimization and weather routing has been shown to produce similarly large fuel savings in the order of 15%. Wind is an intermittent energy source, and it often occurs together with waves, making ships installed with wind propulsion especially interesting from weather routing point of view. This means that a holistic approach is needed when conducting weather routing for ships with wind propulsion. Using detailed simulations, this paper studies how the fuel savings potential of wind propulsion can be further improved by coupling it with state-of-the-art voyage optimization software. Via case study examples, it is shown that the savings potential of wind propulsion can be more than doubled by using voyage optimization.

Multi-Objective Weather Routing Algorithm for Ships: The Perspective of Shipping Company’s Navigation Strategy

Ship weather routing has always been an important issue in the research field of navigation, and many scholars have been devoted to this research for a long time. To study the route strategies of different shipping companies, this paper proposes an improved multi-objective ant colony optimization (IMACO) algorithm based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It can comprehensively consider ship navigation risk and fuel consumption cost under complex sea conditions. First, the grid method is used to model the marine environment. Then, we calculate the fuel consumption and the ship navigation risk of each grid and use the TOPSIS method to evaluate these two indicators for each grid. The results show that due to the different strictness of navigation management requirements of different companies, different routes are selected in the same sea area at the same time. Compared with the single-objective ant colony optimization (SACO) algorithm, the algorithm proposed in this paper can more comprehensively and effectively solve the problem of route strategy selection of shipping companies, which has great practical significance for ship operations management.

A Metaheuristic Algorithm for Ship Weather Routing

A Metaheuristic Algorithm for Ship Weather Routing

Data fusion and machine learning for ship fuel efficiency modeling: Part I – Voyage report data and meteorological data

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R2 values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Ship route optimization using hybrid physics-guided machine learning

This paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. The first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control.

Energy saving method for ship weather routing optimization

In this paper, an improved three-dimensional dynamic programming algorithm for ship route optimization is proposed. As a consequence of the weather conditions and constraints, the ship course and speed are planned, and the optimal solution of ship route planning is generated. Compared with the actual navigation data of the VLCC oil tanker, the calculation accuracy of the method is verified by referring to the great circle route. Under the objective function of minimum FOC (fuel oil consumption) and ETA (estimated time of arrival), the economic benefits of the VLCC oil tanker per voyage are increased by 1.59% and 1.33% respectively, and ETA is shortened by 14.63h and 17.79h. In addition, the method also has the ability of other objective function optimization, weather updating and route updating, which can provide sufficient support for ship operators in route planning.

Weather Routing System Architecture Using Onboard Data Collection and Route Optimisation

Abstract This paper describes the architecture of a weather routing system consisting of two key elements: onboard monitoring and route optimiser sub-systems. The former is responsible for collecting various onboard measurements, such as current ship position or ship motion variables. These data, when gathered and processed, are then used for fine-tuning a ship model. The model, together with weather forecasts, is utilised by a multi-objective route optimiser to estimate forecasted ship responses during the voyage. The route optimiser has been developed in a client-server architecture to reallocate all necessary high-tech resources to the server side and keep the client software as simple and light as possible. The system also includes a module responsible for optimising transmission costs, to reduce onboard transmission during the voyage. The entire solution has been deployed onboard the demonstrator ship ‘Monte da Guia’ and tested during its operations at sea.

A Data Processing Framework for Polar Performance Diagrams

Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new framework for scientific work with such diagrams, which we make available as an open source Python package. It contains a model for the creation of polar performance diagrams from measurement data and supports different representations of polar performance diagrams for different tasks. The framework also includes several methods for the visualisation of polar performance diagrams, for example for scientific publications. Additionally, the presented framework solves basic tasks for the future development of weather-routing algorithms in a far more general manner than other methods did previously: it provides the calculation of costs of a sailing trip using custom cost functions, suggestions of optimal steering using convex hull calculations and a more flexible calculation of isochrone points, using custom weather models. Altogether, the presented framework allows future researchers to more easily handle polar performance diagrams. The corresponding Python package is compatible with various established file formats.

Ship emissions reduction using weather ship routing optimisation

A significant proportion of global carbon dioxide emissions are attributed to ocean-sailing ships and shipping emissions are predicted to double in less than 30 years. This paper investigates the benefit of using weather ship routing optimisation, assessing the ship emissions for minimum distance routes and optimised routes. The present contribution merges the estimation of shipping pollutants and their mitigation through weather routing optimisation; two lines of research widely analysed separately but seldom discussed together. A previously developed open software of weather ship routing is used to obtain the minimum cost (i.e. optimised route) in terms of sailing time, using high-resolution wave forecasting. The assessment of fuel consumption and ship emissions calculations were inspired by the STEAM2 bottom-up approach, in conjunction with the estimation of the power increase needed to overcome speed decrement due to waves. Several scenarios covering the Western Mediterranean Short Sea Shipping routes (from 24 to 600 nautical miles and using a real Ro-Pax vessel) are compared in terms of emissions between the minimum distance route and the optimum. The ship routing optimisation reveals a reduction up to 30% of ship emissions during severe storms on longer routes. Nevertheless, all the cases studied show emissions mitigation when ship routing optimisation is used. The expected increase of extreme weather events, in terms of frequency, intensity and duration due to climate change, suggests a gradual gain of implementing weather ship routing optimisation in all types of routes, regardless of the distance.

Study of Hybrid Propulsion Systems for Lower Emissions and Fuel Saving on Merchant Ship during Voyage

The hybrid propulsion system is a vehicle propulsion system with two or more sources of propulsion such as diesel, batteries, and other renewable energy. The use of hybrid propulsion systems is not new, and they have been adopted worldwide. There are many applications for hybrid propulsion systems, from powering land-based vehicles to marine vessels. With the success of hybrid propulsion systems, many large vessels still rely on two-stroke engines for power generation. In this paper, the benefits and feasibility of using battery systems or hybridizing existing conventional two-stroke engines onboard large intercontinental vessels is studied and simulated. The merchant ship, a bulk carrier with 82,000 deadweight tonnage capacity, is used. The model vessel travels along a simulated route of up to 4000 nautical miles using weather routing software to generate data for estimating emissions with the help of widely adopted empirical formulas. A reduction of up to 3.4 tonnes of emissions and fuel consumption of not more than 1 tonne per kWh from peak shaving and boosting applications for a 12-day voyage were observed.

Ship weather routing optimization based on improved fractional order particle swarm optimization

Based on the original FOPSO (fractional order particle swarm optimization) algorithm, this paper improves new coefficients of the fractional order velocity update formula to avoid falling into local optimization and premature problem. The coefficients change adaptively with the swarm evolution state to make the fractional order velocity occur some disturbances to jump out of the local optimal solution and the search space more comprehensively. In addition, by comparing with other PSO (particle swarm optimization) algorithms for solving typical benchmark functions, the superiority of the improved FOPSO algorithm in solving multi-dimensional nonlinear problems is confirmed. Then, taking a VLCC oil tanker as a research example, it is analyzed that under the objective function of minimum fuel oil consumption (FOC) and Estimated time of arrival (ETA), the maximum profit calculated by the proposed improved algorithm is 9.83 $/nm and 4.17 $/nm higher than that of the actual navigation, respectively. Meanwhile, when the weather forecast data is updated or the ship deviates from the voyage, the improved FOPSO algorithm will update the optimized route according to the current ship's position and sea conditions. Consequently, the improved FOPSO algorithm is quite suitable to solve the ship weather routing optimization problem with constraints.

Multi-Objective Weather Routing Algorithm for Ships Based on Hybrid Particle Swarm Optimization

Multi-Objective Weather Routing Algorithm for Ships Based on Hybrid Particle Swarm Optimization