In this research, a framework for the analysis and design optimization of ship hull–propeller systems (HPSs) in waves is developed. This framework can be utilized as an efficient synthesis tool to determine the main geometric characteristics of the HPSs during the early stage of ship design. The optimization is carried out in two levels and under multipoint operating conditions (OC). Multiobjective evolutionary algorithm based on decomposition (MOEA/D) as an efficient multiobjective evolutionary algorithm, Michell integral and OpenProp tool as low-fidelity hydrodynamic solvers and boundary element method (BEM) as medium-fidelity solver are applied on two case studies to minimize the effective power and maximize the propulsive efficiency of HPSs. To estimate the added wave resistance, an efficient semiempirical formula is also employed. The Series 60 hull form with DTMB P4118 single propeller and S175 hull form with KP505 twin-propeller are considered as the original models. The numerical results show that the framework can find optimized designs with better hydrodynamic performance.
Optimizing the hydrodynamic performance of ships’ hull and propeller(s) based on multiple design condition has gained considerable importance over the last few years. High fuel oil costs are the reason that shipyards and ship owners are now focusing more than ever on the reduction of effective power and propulsive efficiency. Hydrodynamic performance parameters, such as effective power and propulsive efficiency, are determined by the hull form and propeller shape, so it is very important to choose a hull–propeller system (HPS) with good performance in early stage ship design.
There exist two main factors in the hydrodynamic design optimization of marine systems. The first factor is simultaneously considering all components of the system influencing objective function(s) and the second one is selecting a less time-consuming solver with satisfactory accuracy. In the ship design process, these two factors must be taken for conducting a reasonable optimization into consideration.