Machine Learning (a.k.a AI) for Naval Architecture?
When we think of ship design, we often imagine massive containerships or tankers; But tugboats are a class of their own and as complex to design as another ship. Compact, powerful, and maneuverable, tugboats main mission is to push and pull and salvage large vessels. And as with any vessel, design starts with one thing: requirements.
In the case of tugboats, the most critical parameter that sets the foundation for the design spiral is the bollard pull—the ability to generate pulling force at zero speed, as I’ve written some time ago…
Once this value is determined, naval architects begin the intricate spiral of defining hull dimensions, propulsion power, stability, and arrangements.
As I previously wrote in 2021, the Ship Design Spiral is not a straight path; it’s an iterative journey. As each parameter is refined, other parameters must adjust. For tugboats, designers must balance power, stability, and compactness while ensuring the vessel meets class rules and high power delivery.
A Recent study by Karaçay et al. (2024) published in Applied Sciences proposes a machine learning model to predict the main particulars of diesel-powered Z-Drive harbor tugboats during the concept design stage.
The model uses Bayesian networks and non-linear regression to estimate key dimensions (length, beam, draft) and Power, based on inputs like bollard pull and service speed. The authors compiled a robust dataset of 476 tugboats, achieving interesting results (6.57% MAPE).
This study demonstrates that ML (AI for the buzzword lovers) models can support the early-stage concept design of specialized vessels like tugboats once we have a good historical dataset. The use of a data-driven framework can reduce the number of revisions down the spiral, which traditionally involved CFD simulations or towing tank tests.
What’s exciting is how machine learning can help accelerate early decisions, giving naval architects more room to focus on refinement and performance tuning.
I also developed a simple linear regression model, on june 2023, that predicts Bollard Pull based on Power and Propeller diameter, from Kongsberg and Caterpillar’s z-drive catalog data, check it here.
