Trim Ship Fuel Usage By Up To 5%
Decision-making based on collected data is helping ship owners and operators to make significant savings.
It is no longer just about increasing the fidelity in a vessel’s speed and consumption profile. Access to high-frequency data (HFD) has allowed Nautilus Labs to help clients unlock insights that are driving optimised operations.
The company’s Dynamic Speed Optimization (DSO) and machine learning techniques are informing clients’ RPM instruction to the crew. As clients begin to see the value that HFD has for their business and understanding of their vessels, they are increasingly relying on Nautilus Platform to determine how to operate their vessels in order to maximise ROI.
Determining a ship’s optimal trim, dynamically over the course of a voyage, is another such example of an optimisation derived by Nautilus Labs software.
A vessel’s trim refers to its floating position in length, which can impact the water resistance it experiences along a voyage and therefore the amount of energy it needs to consume to progress forward.
Trim optimisation refers to identifying the exact trim the vessel needs to adjust to in order to minimise the vessel’s propulsive power demand under given speed, draft, and weather conditions.
When tackling a challenge such as trim optimisation, it is important to recognise that the nature of a vessel sailing in narrow ranges of operation leads to clustered data that needs to be properly deciphered.
How does one go about defining the relationship between trim, power, fuel consumptions, speed, draft and weather, given such clustered data points?
Statistical machine learning approaches have become some of the go-to methodologies used to tackle this question. A fundamental advantage of machine learning is that it doesn’t require any underlying assumption about the intrinsic relationship between data points in order to produce a relationship between them.
With sample data from a vessel on the platform, Nautilus Labs ran a machine learning methodology for given draft and weather conditions, and identified the relationship.
Without any scientific context that governs the physical relationship of the variables in question, the machine learning approach will result in a model with low error metrics but one that overfits for the available data. Thus, while it will appear to be accurately portraying the relationship between trim and power.
It will be nominally overfitting to the available data, not taking into account the principles of naval architecture that govern the closed-form relationship among variables such as trim, power and speed for given draft and weather conditions.
A tempting alternative would be solely relying on principles of naval architecture to develop a closed-form expression that captures the essence of the problem.
This solution, while scientifically sound, would fail to capture the intricacies and particularities of the specific vessel on its unique voyage under unique conditions.
This solution would lead a ship operator to believe that operating with a marginally positive trim, or going by the bow, is always a good approach, regardless of the ship; neglecting the common-sense rule of thumb that ships are designed to sail with an even keel. Thus, it is expected that, under design conditions, the optimum trim shouldn’t be far off the even keel trim.
The Nautilus Labs solution combines the best of both approaches. Principles of naval architecture are used to build a scaffolding for the model to preserve the intrinsic relationships between the fundamental parameters of the ship. Then this scaffolding is overlayed with statistical machine learning to fine-tune the model to the data on hand, thus accounting for the particularities of the ship.
The surface of the solution corresponds to a specific set of draft and weather conditions. For visualisation purposes, the raw data points correspond to an interval centered in the target conditions.
For any given speed along the surface, there is a trim that minimises the engine’s power demand. Using a combined machine learning and naval architecture approach, Nautilus identifies the optimal trim that minimises the engine’s power demand and, ultimately, fuel consumption.
Graphically, visualising the optimal trim shows how power increases as the vessel’s trim deviates from optimal.
Nautilus has identified an accurate optimal trim for clients that equates to over 5% in fuel savings along each vessel’s respective voyage.
Now more than ever, it is becoming important to look under the hood of data collection and analysis processes and embrace the use of high-frequency data to conduct smart optimisations using the latest technologies and methodologies available.
The solution at Nautilus Labs helps clients to optimise for variables such as trim in order to reduce the demand of energy for propulsion during sailing. Nautilus uses proprietary machine learning models combined with naval architecture expertise to provide accurate and real-time insights that help clients make data-driven decisions to enhance the performance and efficiency of their fleet and to maximise ROI.