Hull form optimisation in waves based on CFD technique

Abstract

The seakeeping behavior of a ship in waves is different from its behavior in calm water. The resistance and seakeeping performance of a ship are of great importance and must be considered in the early-stage design of a ship's hull form design. Therefore, this paper proposes a hull form optimization framework aiming to achieve the minimum total resistance in waves using a CFD technique. A sinusoidal wave is adopted to establish the numerical wave tank and the overset mesh technique is used to facilitate the motions of the ships in question. Considering the motions of pitching and heaving, the total resistance of the hull in waves is regarded as the objective function which is calculated using the Reynolds averaged Navier-Stokes (RANS) method. The arbitrary shape deformation (ASD) technique is used to change the geometry. Under displacement and design variables, a hybrid algorithm is developed to evaluate the objective function combining the optimal Latin hypercube design (Opt LHD) and the non-linear programming by quadratic Lagrangian (NLPQL) algorithm. Finally, two examples of hull form optimization are presented and discussed for David Taylor Model Basin (DTMB) model 5512 and WIGLEY III cases. The results show the effectiveness of the optimization framework developed in the present study, which can lay the foundation for further optimization of full-scale ships.

Numerical and experimental study on hydrodynamic performance of ships advancing through different canals

“…Slight variations in the wetted area of the ship can have dramatic consequences in terms of the behaviour and performance of a ship (Tezdogan et al, 2016). Coincidently, this is also the main motivation of hull form optimisation studies (Zhang et al, 2018a(Zhang et al, , 2018b(Zhang et al, , 2017. A change in depth or width also invalidates any analysis that was not performed using a similar set-up.…”

An Estimation of Hydraulic Power Take-off Unit Parameters for Wave Energy Converter Device Using Non-Evolutionary NLPQL and Evolutionary GA Approaches

“…The NLPQL algorithm was a local optimiser and has the advantages of fast convergence and high-stability [42]. In several studies, the NLPQL-based optimisation was applied to solve and optimise various non-linear problems during the design stage [42][43][44][45]. Figure 7A shows the flowchart of the NLPQL-based optimisation technique.…”

Structure optimization of submerged water jet cavitating nozzle with a hybrid algorithm

“…In LHD method, the design space for each factor is divided uniformly (the same number of divisions, n, for all factors). These levels are randomly combined to specify n points defining the matrix design (each level of a factor is studied only once) (Zhang et al, 2018). The space-filling capacity of LHD method is good Figure 1.…”

“…Velden and Koch (2010) pointed out that MMFD method can be used to optimize the non-linear problems when starting from a feasible design point. In addition, Zhang et al (2018) assumed that a LHD is a good method to obtain an optimal initial point for a gradient optimization method. Therefore, this study uses a LHD algorithm to choose a feasible design initial point.…”

“…For the gradient algorithm, it is highly dependent on the initial point, and it is also likely to fall into a local solution. Zhang, Tezdogan, Zhang, Xu, and Lai (2018) used an experimental design method to find an initial value of the gradient algorithm, achieving higher evaluation accuracy. Thus, a Latin hypercube design (LHD) (McKay, Beckman, & Conover, 1979), an experimental design method, is selected in this study to obtain an optimal initial value, and a MMFD algorithm (Vanderplaats, 1984), a gradient algorithm, is used to search the local part in detail in the water jet cavitating nozzle optimization space.…”