@article{GENERAL,
      recid = {11204},
      author = {Lopes Gamboa, F and Paterson, N},
      title = {Comparative Analysis of AI-Based Optimisation Techniques  for a Conceptual Frigate Hullform Design},
      journal = {Conference Proceedings of INEC},
      address = {2024-11-06},
      number = {GENERAL},
      abstract = {This paper presents the results of an investigation  conducted to assess the efficacy of Artificial Intelligence  (AI) in optimising the hullform of a conceptual frigate.  The frigate, with a length of 130 meters and a top speed of  24 knots, served as the subject for the experiment. A  baseline hull was initially designed, and spatial  constraints were defined for the study. Three different  contractors were invited to optimise the bare hull  resistance for various speeds and displacements. The  optimisation process considered a weighted grading matrix  based on the ship's operational profile.
One contractor  employed traditional optimisation techniques, including  empirical knowledge, regression series, and the  modification of ship hydrostatic characteristics. A  parametric hullform model was created to produce candidate  designs. Final analysis was made using Computational Fluid  Dynamics (CFD). Another contractor utilised machine  learning with neural networks, with training data sourced  from a parametric hullform model and CFD results. The final  contractor employed a combination of a parametric hullform  model, T-Search optimisation, potential flow, and CFD, with  a specific focus on achieving maximum top speed with the  lowest resistance.
Results indicated that traditional  techniques improved bare hull resistance by an average of  8%. The use of neural networks significantly outperformed  traditional methods, demonstrating a remarkable average  improvement of 22% in bare hull resistance. However, a  critical observation emerged regarding the optimisation  focused on top speed only, where machine learning  techniques demonstrated a 27% improvement on resistance.  This improvement, however, came at the cost of a notable  detriment to lower speeds resistance, resulting in an  average overall increase in resistance of 16% across the  speed range.
The findings of this study present a dilemma  for naval architects and customers alike. While  prioritising top-speed optimisation may lead to reduced  capital expenditures (CAPEX) due to lower costs associated  with machinery and auxiliary systems, it could also result  in increased through-life costs due to high resistance at  lower speeds, which constitute the majority of operational  time at sea. This highlights the importance of a balanced  approach and careful consideration when implementing  AI-based optimisation techniques in naval vessel design.  Ensuring a holistic view of performance and costs  throughout the vessel's lifecycle is crucial for making  informed decisions.},
      url = {http://library.imarest.org/record/11204},
      doi = {https://doi.org/10.24868/11204},
}