Computational fluid dynamics-based hull form optimization using approximation method

Shenglong Zhang, Baoji Zhang, Tahsin Tezdogan, Leping Xu, Yuyang Lai

Research output: Contribution to journalArticle

11 Citations (Scopus)

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 paper, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III ships, 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.
LanguageEnglish
Pages1-15
Number of pages15
JournalEngineering Applications of Computational Fluid Mechanics
Early online date5 Jul 2017
DOIs
Publication statusE-pub ahead of print - 5 Jul 2017

Fingerprint

Computational Fluid Dynamics
Approximation Methods
Optimization Methods
Computational fluid dynamics
Particle swarm optimization (PSO)
Optimization
Ship
Ships
Weight Coefficient
Optimise
Particle Swarm Optimization Algorithm
Inertia
Latin Hypercube Design
Elman Neural Network
Prediction
Form
Navier-Stokes
Search Methods
Dynamic Simulation
Hydrodynamics

Keywords

  • hull forms optimization
  • approximate method
  • IPSO-Elman neural network
  • optimal Latin hypercube design
  • arbitrary shape deformation

Cite this

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title = "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 paper, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III ships, 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.",
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Computational fluid dynamics-based hull form optimization using approximation method. / Zhang, Shenglong; Zhang, Baoji; Tezdogan, Tahsin; Xu, Leping; Lai, Yuyang.

In: Engineering Applications of Computational Fluid Mechanics, 05.07.2017, p. 1-15.

Research output: Contribution to journalArticle

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