Research on hull form optimization at multiple speeds based on machine learning and ship model experiments

Abstract

In order to improve the scientificity, efficiency and systematicness of ship form optimization, the multi-objective optimization research on the David Taylor Model Basin (DTMB) 5512 ship is carried out. First, the ship model experiment quantified the still water resistance of DTMB 5512 at six speeds at Froude number (Fr) as 0.25–0.40, demonstrating an almost linear resistance velocity relationship. Meanwhile, the DTMB 5512 ship is subjected to numerical simulations using the Computational Fluid Dynamics (CFD) method and the calculated results are compared with the experimental results. Then, Random Forest (RF)-based approximate models were developed for multi-speed resistance prediction, and verified its feasibility using Maximum Absolute Error (MAE). Finally, the parametric modeling method, the CFD method, and the optimization algorithm are integrated to construct a multi-objective optimization design system for ship forms. The resistance performance of the DTMB 5512 ship is optimized using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The results show that under the constructed hull form optimization framework, the optimized hull forms that meet the constraint conditions can be obtained. The total resistance of the obtained optimized ship at six speeds is reduced by 2.95 %, 4.44 %, 3.71 %, 5.22 %, 5.51 % and 4.83 % respectively. The research results indicate that the optimized hull forms with improved resistance performance can be obtained through the proposed methods, significantly enhancing the optimization efficiency. It also verifies the effectiveness of the random forest method in addressing the challenges of actual engineering optimization.