Compute Maritime, the UK-based developer of the NeuralShipper AI ship design platform, has unveiled the results of a government-funded project that used artificial intelligence and additive manufacturing technologies to develop a more efficient crew transfer vessel (CTV) for offshore wind operations.
The GenDSOM project, carried out with Siemens Digital Industries Software, Rapid Fusion, HP, BYD Naval Architects and the University of Southampton, produced a 32.5-metre twin-hull vessel designed to transport 24 offshore wind technicians and four crew.
Using NeuralShipper to optimise the vessel’s hull form and pairing the design with a diesel-electric hybrid propulsion system developed with Siemens Energy, the project achieved an 11.1% reduction in annual fuel consumption and an 8.9% cut in CO2 emissions compared with a conventional diesel-powered vessel performing the same operating profile.
According to the project results, the design would save about 101,671 litres of fuel and 258.7 tonnes of CO2 per vessel each year.
The modelling also highlighted the impact of the AI-driven hull optimisation on vessel performance. Under a full-day operating scenario, the conventional vessel was found to end the day with a 34 kWh energy deficit, exceeding the battery’s safe discharge limit and failing to maintain a 25-knot service speed.
By contrast, the AI-optimised design finished the same operating cycle with a 106 kWh energy surplus while maintaining full service speed.
Compute Maritime said the findings suggest hull optimisation can play a critical role in enabling hybrid and electric propulsion systems, helping improve energy efficiency while reducing fuel consumption and emissions in offshore wind vessel operations.
