Computational fluid dynamics-based hull form optimization using approximation method

Abstract

With 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. ARTICLE HISTORY

Ship Hull Form Optimization: A Computational Fluid Dynamics (CFD) Approach

“…IPSO algorithm was blended with Elman neural network to ensure prediction accuracy of total resistance. This method is also consistent with Zhang, B-Zhang, Tezdogan, Xu & Lai [3]. Zonga, Hong, Wang & Hefazi [4] applied ship hull modification method based on self-blending.…”

Application and optimization of drag reduction characteristics on the flow around a partial grooved cylinder by using the response surface method

“…Also, it can be solved by an alternative better performed method than the calculation of the CFD tools Fotovatikhah et al, 2018). Some of the advanced algorithms like PSO (IPSO) algorithm and neural network are used for optimization (Zhang, Zhang, Tezdogan, Xu, & Lai, 2018).…”

Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

“…For example, the self-feedback gain coefficient of ENN is generally determined by the attempt, which leads to low learning efficiency [31]. Fortunately, along with the development of softcomputing technique, abundant optimization algorithms have emerged to improve the deficiency of single-objective neural network in predicting, such as genetic algorithm (GA), particle swarm optimization (PSO), improved particle swarm optimization (IPSO), and whale optimization algorithm (WOA) [32][33][34][35]. Although the aforementioned optimization algorithms can improve the prediction performance, they still have some defects of slow convergence speed, local optimum, and long training process.…”