Computational fluid dynamics-based hull form optimization using approximation method
Computational fluid dynamics-based hull form optimization using approximation method
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DOI：
10.1080/19942060.2017.1343751
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发表时间：
2018-01
影响因子：
6.1
通讯作者：
Shenglong Zhang;Bao-ji Zhang;T. Tezdogan;Leping Xu;Yu-yang Lai
中科院分区：
文献类型：
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作者：
Shenglong Zhang;Bao-ji Zhang;T. Tezdogan;Leping Xu;Yu-yang Lai
摘要随着计算技术的飞速发展，计算流体动力学（CFD）工具在船体体型优化设计中得到了广泛的应用。然而，这是非常耗时的，因为大量的CFD模拟需要执行一个单一的优化。寻找一种高效的方法来代替CFD工具的计算具有重要意义。在这项研究中，基于CFD的船体优化循环已被开发，通过集成的近似方法来优化船体，以减少在静水中的总阻力。为了提高粒子群优化（PSO）算法的寻优精度，提出了一种改进的PSO（IPSO）算法，该算法分别基于随机惯性权重和收敛性评价设计惯性权重系数和搜索方法。为了提高总阻力的预测精度，提出了一种基于IPSO-Elman神经网络的数据预测方法。本文采用IPSO算法训练Elman神经网络的权系数和自反馈增益系数。为了建立IPSO-Elman神经网络模型，采用最优拉丁超立方设计（Opt LHD）对样本船体外形进行设计，并采用雷诺平均Navier-Stokes（RANS）方法计算这些船体外形的总阻力（目标函数）。本文应用该优化框架对DTMB 5512型和WIGLEY III型两种船型进行了优化设计，并通过任意形状变形（ASD）技术改变了船体形状。结果表明，在这项研究中开发的优化框架可以用来优化船体的形式，显着减少计算工作量。
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.