One way to improve energy efficiency is to optimize, to better plan operations, and - in a cost-efficient way - to reduce GHG emissions, lowering operational costs and possibly even improving revenue and thus the overall global competitive edge. However, for planning and optimizing operations, we would need to have current, up-to-date information of the ship’s condition, for example how well the ship is doing at the moment, the level of fouling, and whether this is affecting consumption.

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One way to get information about the ship’s technical performance is by taking onboard measurements. However, these measurements might not always be available. For example, the ship may not be equipped with adequate sensors to measure the power of the propeller, or that data is not necessary disclosed to all the stakeholders. The owner might not disclose the measurements to the charterer who might be paying for the fuel and is interested in the actual level of performance of the vessel. In this regard, a noon report is a good way to access information, and this is often used to deduce the level of performance. But the shortcomings of relying on the noon report are that the data is sparse.

We have one daily information data point on the consumption. However, the weather might change during the day quite significantly, which cannot be reported in the noon report. To overcome these challenges, our solution is to use the available big data, taking all the information from the AIS (Automatic Identification System) and the publicly available information about the ship. We then build a digital twin of the vessel, based on our knowledge and experience of naval hydrodynamics and ship design. As a result, we can overcome some of the challenges created by the lack of available data from the noon report.

The challenges lie in the availability of weather information. For instance, we can see that there is a tendency to exaggerate the wind speed, for example to report higher wind speeds when actually the wind speed is close to the limiting Beaufort range for defined good weather. Variations in the weather can be quite significant, which introduces uncertainty in relying on the noon report as a source of information.

For instance, in 800 days of operation of one dry bulk vessel, during 233 days the swell and wind-wave height varied more than 1 metre, and the wind speed varied more than 5 m/s. Whereas, in the noon report we would have just one information of the wind speed and the wave height, which may be reported or not. These weather variations have quite important influences on the fuel consumption of the vessel and in the reduction of the speed.  Within the 4 Beaufort range, which is considered as good weather, if we’re trying to maintain the same speed throughout, we need to increase fuel consumption by 50% in adverse weather. If we are operating at constant engine output, we are seeing as much as a 15% reduction in vessel speed.

To account for these challenges, we have a full model of the ship based on our generic models, which we combine with chart data and the available AIS data, linking it to the specific design of the ship. We then add global weather data based on each location of the vessel.

Combining the noon reports at this stage, as well as using automation data if it’s available, creating the detailed and accurate digital twin with the ship performance model based on hydrodynamics and naval architecture principles. Essentially, we have generic models that can cover whichever ship we choose, providing us with a base from which to begin our analysis. Because we are using the automation signals from 200 vessels, we have gained knowledge of how to enhance our models, applying data learning methods to continually improve them. With the help of the digital twin we can extract information, monitor the performance of the vessel, and conduct better, more precise planning of operations. The twin that we have is based on NAPA’s Ship Performance model, applying a hydrodynamic model which takes into account the coupling effects of wind, waves, current, and shallow water, combined with a full model of the propulsion and engine system.

We are then able to address the force balance of all these factors at the actual location of the ship, at the real operational speed, and in actual wind and wave conditions. What is also important is that we are not dependent on taking samples of days where the weather was good, instead we are able to use data from all days.

The noon report gives average or total values over the one day period and therefore cannot reflect the entire history.  The reported speed is normally an average speed, similarly for the wave height, if reported, and for wind speed. However, the fuel consumption, or in some cases the speed reduction, has a non-linear dependency on the wave height and wind speed.

With our method, we are taking into account the reference fuel consumption at each position during the vessel’s operations. We assume the reference calculated consumption will represent clean hull consumption, and the difference between the reference and noon reported value is considered as being the increased consumption due to fouling. We can then place all of the data points on the timeline, even the data from days with variations in weather, and we will then produce a curve to show the increase in consumption over time. In bringing all of this information together we can significantly improve the accuracy of the estimate over time.

If we have only three months of operational data, the uncertainty is around 3 mt/d. However, after ten months of operation and data collection, the uncertainty on the level of fouling and the level of performance is less than 1 mt/d and we are best able to get the most accurate results.

With increasing data, we get better accuracy. We have studied a fleet of dry bulk vessels with 3 years of data, and on average, after 3 months of data collection, the uncertainty of the consumption estimate is less than 7.5%. After half a year, the uncertainty is less than 5 %. With the presented method we can retain most of the noon reports, filtering out any erroneous datapoints. The accuracy of the fouling estimate improves rapidly with the amount of datapoints, for example noon reports, and this information can then be used to estimate how much the weather has effect on that particular vessel and also to know the current level of the vessel’s performance.

All of this can be used to plan vessel operation with better accuracy as we understand better the level of fouling and the effect of weather. Also we can estimate more precisely the required voyage times, the estimated consumption for the voyages, and plan better what could be the sea margin levels for those vessels at different voyages. The model also helps with planning of maintenance, and we can better assess the effect of hull maintenance.  In conclusion, with this up-to-date information on the ship’s performance, we can do more precise voyage optimization for fuel efficient operations.

Above text is an edited version of Dr. Teemu Manderbacka’s presentation during the 2019 SAFETY4SEA London Conference.

View his presentation here:

The views presented hereabove are only those of the author and not necessarily those of  SAFETY4SEA and are for information sharing and discussion  purposes only.


About Dr. Teemu Manderbacka, Senior R&D Engineer, NAPA

Teemu Manderbacka, holds a Doctor of Science in Naval Architecture. He works currently at NAPA Shipping Solutions as Senior R&D Engineer developing methods for the assessment of ship technical performance applying the naval architecture principles with statistical methods of big data processing. Prior to joining NAPA Ltd, in Jan 2016, he worked at the Aalto University, as laboratory manager of the Seakeeping and Ice Model Basin, carrying out model tests of the ice going performance of the vessels. He has obtained the doctoral degree at the Aalto University on the numerical modelling of hydrodynamics of ship flooding, including the interaction of sloshing and ship motions. His current interest is to advance Safety and Energy of Marine Transportation by combining hydrodynamics and ship design knowledge with data of the ship operations. He is actively participating to the discussion of scientific community. He has over ten years of experience in ship hydrodynamics and have authored several publications on the intact and damaged ship hydrodynamics.