The paper presents recent research conducted within the NATO RTO Task Group AVT-204 “Assess the Ability to Optimize Hull Forms of Sea Vehicles for Best Performance in a Sea Environment.” The objective is the improvement of the hydrodynamic performances (resistance/powering requirements, seakeeping, etc.) of naval vessels, by integration of computational methods used to generate, evaluate, and optimize hull-form variants. Several optimization approaches are brought together and compared. A multi-objective optimization of the DTMB 5415 (specifically the MARIN variant 5415M) is used as a test case and results obtained so far using low-fidelity solvers show an average improvement for resistance and seakeeping performances of nearly 10 and 9%, respectively.

Keywords:
artificial intelligence, optimization problem, machine learning, diez multi-objective hydrodynamic optimization, itu, coefficient, solver, dtmb 5415, hull, pareto front
Subjects:
Information Management and Systems, Artificial intelligence
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2015. The Society of Naval Architects and Marine Engineers
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