Better fuel savings with an integrated, onboard trim solution

There are various solutions for trim optimisation on the market. As a ship operator or fleet manager, you should be aware of which method best fits your needs.

Trim optimisation

It is well known that optimised trim leads to reduced fuel consumption. This solution is cost-effective and easy to implement, cutting fuel costs while ensuring continued smooth operations.

However, with the growing number of trim optimization tools on the market, how do you know which one is the best for your vessel and provides the most accurate results?

They all feature a graphical user interface and consider key operational parameters such as speed and draught. However, they differ in their approach to generating the fundamental hydrodynamic knowledge base, which determines the performance of the trim optimisation tool.

The solution can be based on one of two systems:

Model or CFD system

Before the ship is sent to sea, model tests or simulations using CFD (computational fluid dynamics) are performed to create a knowledge base. Model tests are costlier and more time-consuming than CFD. Furthermore, they use a scaled-down version of the ship, which can lead to inaccurate results. CFD , however, can mimic the conditions of the real ship.

Machine-learning system

This approach puts a curve through scattered data. It required extensive information from sensors on board the vessel. Data acquisition systems on the ship have to handle changing parameters on account of uncertain ambient conditions. As a result, until a large amount of data has been “learnt” by the system, the trim optimisation advice will most likely not be very accurate.


The hydrodynamic system based on CFD has the edge over the machine-learning approach when it comes to trim optimisation, as it provides more accurate results in shorter time at affordable price. ECO Assistant from DNV GL, for instance, is a trim optimisation software based on CFD that can be coupled with other functionalities in a single comprehensive tool. Because the machine-learning model involves so many parameters, it is best for ships that experience fewer changes in operation such as cruise ships or ferries.

Related links and downloads:

ECO Assistant

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Trim Optimisation – Don’t blind me with science!

2-page article from the Naval Architect magazine by DNV´s Volker Bertram (PDF, 1MB)

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