AI-Based Ship Concept-Design: Automated Ship Layout and Energy Storage Sizing Depending on Asymmetric Landing Strip Angle for a Drone Carrier

A drone carrier is a ship capable of deploying, recovering, resupplying/recharging and maintaining a fleet of aerial, surface, and underwater unmanned vehicles for naval purposes. It provides increased flexibility to a surface fleet, widening its mission capabilities while reducing the risks through the exploitation of the varied vehicles it can deploy, whether autonomous or remotely operated, as well as vehicles for standard amphibious operations. Given the complexity of the drone carrier configurations, AI based ship concept-design tools must be used to determine feasible designs and select the best compromise between all the different requirements such vessels must comply with. In this paper, the construction of mathematical metamodels relies on data mining techniques, the design alternatives are defined through Montecarlo generation, and Multi-Attribute Decision Making (MADM) techniques are employed for selecting the designs presenting a fair trade-off of competing requirements. This AI-based concept-design approach is here applied to the determination of the optimal layout for a drone carrier featuring an asymmetric landing strip, evaluating a parametric range of horizontal inclination angles relative to the ship's centerline. The latter are constrained by the maximum allowable acceleration and deceleration forces experienced by 3rd-class drones during take-off-assisted by an electromagnetic catapult-and landing, which employs arresting cables with energy recovery. Additionally, for each inclination angle, an energy storage system is sized to enable peak shaving of impulse loads generated during take-off and landing while also supporting the recharging of underwater unmanned vehicles. The findings offer key insights into the relationship between flight deck orientation, energy management, and ship main particulars in drone carrier ship design.