World's first AI-designed CTV ship concept promises to slash fuel use by 100,000 litres per year
The AI-designed crew transfer vessel (CTV) unveiled by Compute Maritime could annually cut fuel use by 11.1% and CO2 emissions by 258.7 tonnes
The AI-designed crew transfer vessel (CTV) unveiled by Compute Maritime could annually cut fuel use by 11.1% and CO2 emissions by 258.7 tonnes.
The AI-designed crew transfer vessel (CTV) unveiled by Compute Maritime could annually cut fuel use by 11.1% and CO2 emissions by 258.7 tonnes.
The AI-designed crew transfer vessel (CTV) unveiled by Compute Maritime could annually cut fuel use by 11.1% and CO2 emissions by 258.7 tonnes.
An AI-designed crew transfer vessel (CTV), unveiled by Compute Maritime, a deep tech firm engaged in ship design, could annually cut fuel consumption by 11.1 per cent and CO2 emissions by 258.7 tonnes, as compared to a diesel vessel.
Produced for the offshore wind sector, the next-generation CTV concept was designed by BYD Naval Architects using NeuralShipper, the world's first AI platform for ship design from Compute Maritime.
The concept was then paired with a diesel-electric hybrid propulsion system, developed with Siemens Energy.
This comes under a UK Government-funded project called GenDSOM, which aims to bring generative AI and additive manufacturing into ship design.
Apart from Compute Maritime and BYD Naval Architects, others involved in the consortium behind the implementation of the project include Rapid Fusion, HP, and the University of Southampton.
Measuring 32.5m long, the twin-hull CTV would require 6.3 per cent less power at speeds of 25 knots—up to 11.6 per cent less at higher speeds—which is one of the key factors behind its promise to save more than 100,000 litres of fuel per year.
According to Compute Maritime, the power difference is a feature of its AI optimising the ship's hull to give it a 106 kWh surplus of energy at the end of each day on the offshore wind duty cycle, as compared to the diesel vessel, which was found to have a 34 kWh deficit on the same cycle.
Another part of the GenDSOM project saw the deep tech firm and Rapid Fusion develop a complete additive-manufacturing toolset that factors in production constraints—such as build volume, support structures and material behaviour—into the NeuralShipper design.
This helped the AI tool optimise the ship's components at all stages, which helped the consortium design and produce a hydrofoil with Rapid Fusion’s Apollo, a robotic large-format additive manufacturing (LFAM) system.
This LFAM system uses high-deposition pellet printing to build large composite and thermoplastic components.
"This is what the future of shipbuilding looks like to us: intelligence at the core of design, delivering efficiency the industry can measure on its fuel bills and its emissions reports," said Shahroz Khan, CEO, Compute Maritime.
Touted as "future-proof", this ship can be upgraded, and is already capable of drawing power from charging infrastructure that wind farm operators are beginning to install at sea.
Over the ship's 25-year life, this is expected to play a major part in the larger shift from diesel to battery power without too many changes to the core propulsion systems.