Pushing the boundaries of dynamic positioning
Steven van ‘t Klooster has recently completed a one-year, Damen-sponsored project on dynamic positioning. His objective? To improve DP performance with particular reference to walk-to-work (W2W) vessels goal by enhancing the ability to calculate and so predict the maximum environmental conditions in which a vessel can continue to maintain its position while transferring personnel. This continued a theme that Steven first explored in his graduate thesis, using the Bibby Wavemaster 1 as a case study.
Bibby Wavemaster 1 gangway
In the study, Steven recognises that for the accurate calculations required by DP, between 30 to 60 different inputs are required depending on the method used. These include known factors such as the dimensions and weight of the vessel. However, variables such as wind, wave and current forces are by their nature uncertain, as are other factors such as the density of the air and water as these vary according to the temperature. Added to that are other uncertainties. For example, the actual propulsive force generated by the thrusters may be slightly different to that stated in the specifications. Frequently these values are either estimated or simply taken from fixed databases. This introduces a level of uncertainty into the end results and has a negative effect on the predicted DP capability.
Dynamic offset confidence interval
Steven’s goal for the project was to be able to state a reliability interval when calculating the DP capability for a particular design of vessel. For example, at present the DP capability of a vessel might be stated as; it is capable of maintaining its position when there is 8m/s of wind speed, 1m/s of current and 1m of wave height. What would be more useful for the vessel operator would be stating the DP performance in the form of; there is a 90% probability that the vessel can maintain its position in those same environmental conditions. Expressing a performance value in this way not only gives a truer reflection of a vessel’s capability under defined conditions, it is also useful when it comes to setting up contracts as big penalties can be incurred when a vessel does not meet its contractual requirements. Stating the DP performance in this way generates a more realistic expectation on the part of the client.
Calculating the values in this way can be applied to both static and dynamic calculations. However, it requires a lot of time and computational power to perform a large number of dynamic calculations and so Steven created a machine learning model that is capable of calculating the DP capability of a vessel within a fraction of a second. With the data input process fast and simple it is straightforward to assess whether or not a change involving one or more of those variables will affect the DP capability of a vessel. This makes the machine learning model a potentially powerful tool.
At present, Steven’s achievement is at the proof of concept stage yet, while its full potential has yet to be realised, Damen R&D’s new Simulation Development Team will be using elements of his thesis code to perform dynamic DP simulations. However, some additional development work should see it able to make a significant contribution to the design stage of DP vessels. In that environment its ability to generate dynamic DP calculations in seconds would be highly valuable in demonstrating the impact of potential design changes on the DP profile as they are proposed. In the future it would even be possible to take it a step further by installing the system on board W2W ships. There its dynamic DP calculation mode would be used when the ship is operational to take into account variations such as its weight and centre of gravity, so as to determine its real-time operational envelope.
Steven van ‘t Klooster recently joined Damen as a general trainee having graduated from Delft University of Technology with a bachelor’s in Mechanical Engineering and a master’s in Offshore and Dredging Engineering. However, while he was still studying he worked part-time at Damen Dredging Nijkerk, attended the Damen Business Course and spent time in the R&D department at Gorinchem.