Abstract
ABSTRACTWith the rapid development of the computational technology, computational fluid dynamics (CFD) tools have been widely used to evaluate the ship hydrodynamic performances in the hull forms optimization. However, it is very time consuming since a great number of the CFD simulations need to be performed for one single optimization. It is of great importance to find a high-effective method to replace the calculation of the CFD tools. In this study, a CFD-based hull form optimization loop has been developed by integrating an approximate method to optimize hull form for reducing the total resistance in calm water. In order to improve the optimization accuracy of particle swarm optimization (PSO) algorithm, an improved PSO (IPSO) algorithm is presented where the inertia weight coefficient and search method are designed based on random inertia weight and convergence evaluation, respectively. To improve the prediction accuracy of total resistance, a data prediction method based on IPSO-Elman neural network (NN) is proposed. Herein, IPSO algorithm is used to train the weight coefficients and self-feedback gain coefficient of ElmanNN. In order to build IPSO-ElmanNN model, optimal Latin hypercube design (Opt LHD) is used to design the sampling hull forms, and the total resistance (objective function) of these hull forms are calculated by Reynolds averaged Navier–Stokes (RANS) method. For the purpose of this article, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III, and these hull forms are changed by arbitrary shape deformation (ASD) technique. The results show that the optimization framework developed in this study can be used to optimize hull forms with significantly reduced computational effort.
Highlights
In recent years, hull form optimization has gained great interest for the purpose of minimizing the total resistance which results in minimizing the running cost
As can be seen from the figure, in 1000 iterations, the improved PSO (IPSO) algorithm can give a better fitness value which is near to the global optimal solution at the initial stage of optimization compared to the Particle swarm optimization (PSO) algorithm
In order to test the effect of the IPSO-ElmanNN, ElmanNN and IPSO-ElmanNN prediction models are implemented in MATLAB (R2016a) with the samples from Table 3
Summary
Hull form optimization has gained great interest for the purpose of minimizing the total resistance which results in minimizing the running cost. On the basis of the local feedback network function, the ElmanNN can process the data more precision for nonlinear problem (Liu et al, 2015), which is of great important for the hull resistance prediction. Particle swarm optimization (PSO), a kind of intelligent optimization algorithm, was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) It has obtained a lot of interest in diverse optimization problems because of the advantages of fast convergence, easy implementation and simple calculation rules. An improved PSO (IPSO) algorithm has been developed by integrating the randomly distributed inertia weight coefficient and the evaluation of premature convergence The performance of this new method has been evaluated by employing them in the optimization of the four mathematical functions. Two ships are presented and discussed: namely, the David Taylor Model Basin (DTMB) model5512 (a ship model of the US Navy Combatant) and the WIGLEY III (a mathematical ship form widely used on the international) ships
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