Intelligent Optimization of Fast Boat Hull Form for Resistance Reduction Using CFD and Surrogate-Assisted Algorithms

Romadhoni (Departement of Naval Architecture, Politeknik Negeri Bengkalis, Bengkalis, 28711)
Budhi Santoso (Department of Naval Architecture, Politeknik Negeri Bengkalis, Bengkalis, 28711)
Muhammad Alimul Hafiz (Laboratory of Ship Hydrodynamics, Departement of Naval Architecture, Polytechnic of Bengkalis, Bengkalis, 28711, Indonesia)

International Journal of Marine Engineering Innovation and Research
Vol. 11 No. 1 (2026)

High-speed fast boats operating at high Froude numbers experience rapidly increasing resistance due to coupled viscous and wave-making effects. This study proposes a surrogate-assisted hull-form optimization framework that combines Reynolds-averaged Navier-Stokes (RANS) CFD (SST k-ω) with Gaussian Process Regression (Kriging) to minimize the total resistance of a fast monohull while maintaining displacement and geometric feasibility. The hull geometry was parameterized using four variables: deadrise angle, prismatic coefficient, longitudinal centre of buoyancy (LCB), and bow entrance angle. A set of candidate designs was evaluated by CFD, a surrogate model was trained and validated (R² = 0.985, prediction error < 2%), and global optimization was carried out using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). CFD verification of the best design shows a consistent resistance reduction across Fn = 0–0.75, with a maximum reduction of 14.4% at Fn = 0.65 compared to the baseline hull. The optimized hull exhibits reduced bow pressure peaks and delayed flow separation at the transom. The surrogate-assisted strategy reduces the number of CFD evaluations and lowers the overall computational effort by about 78% while preserving prediction accuracy. R^2=0.992.