Computational fluid dynamics-based hull form optimization using approximation method
Computational fluid dynamics-based hull form optimization using approximation method
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.
74-88
Zhang, Shenglong
60b44337-e1a3-4bcc-ba72-d9a69739f3e1
Zhang, Baoji
df86802d-153e-4b78-aea3-ab81d6cb3bd0
Tezdogan, Tahsin
7e7328e2-4185-4052-8e9a-53fd81c98909
Xu, Leping
e1bbe4b4-b420-4c86-9d20-ad58dfee93b3
Lai, Yuyang
cb5e4875-bf1f-4277-98a6-b847d6ef792f
5 January 2018
Zhang, Shenglong
60b44337-e1a3-4bcc-ba72-d9a69739f3e1
Zhang, Baoji
df86802d-153e-4b78-aea3-ab81d6cb3bd0
Tezdogan, Tahsin
7e7328e2-4185-4052-8e9a-53fd81c98909
Xu, Leping
e1bbe4b4-b420-4c86-9d20-ad58dfee93b3
Lai, Yuyang
cb5e4875-bf1f-4277-98a6-b847d6ef792f
Zhang, Shenglong, Zhang, Baoji, Tezdogan, Tahsin, Xu, Leping and Lai, Yuyang
(2018)
Computational fluid dynamics-based hull form optimization using approximation method.
Engineering Applications of Computational Fluid Mechanics, 12 (1), .
(doi:10.1080/19942060.2017.1343751).
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.
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Accepted/In Press date: 14 June 2017
Published date: 5 January 2018
Identifiers
Local EPrints ID: 479294
URI: http://eprints.soton.ac.uk/id/eprint/479294
PURE UUID: dd6fcc61-3be6-4939-8ca0-35220b265e85
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Date deposited: 20 Jul 2023 16:53
Last modified: 17 Mar 2024 04:18
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Author:
Shenglong Zhang
Author:
Baoji Zhang
Author:
Tahsin Tezdogan
Author:
Leping Xu
Author:
Yuyang Lai
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