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Computational fluid dynamics-based hull form optimization using approximation method
- Source :
- Engineering Applications of Computational Fluid Mechanics, Vol 12, Iss 1, Pp 74-88 (2018)
- Publication Year :
- 2017
-
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.
- Subjects :
- Mathematical optimization
Engineering
General Computer Science
media_common.quotation_subject
VM
arbitrary shape deformation
020101 civil engineering
02 engineering and technology
Computational fluid dynamics
Inertia
01 natural sciences
010305 fluids & plasmas
0201 civil engineering
approximate method
Hull
0103 physical sciences
Convergence (routing)
Hull forms optimization
Simulation
media_common
Artificial neural network
business.industry
Particle swarm optimization
IPSO-Elman neural network
optimal Latin hypercube design
Latin hypercube sampling
lcsh:TA1-2040
Modeling and Simulation
lcsh:Engineering (General). Civil engineering (General)
Reynolds-averaged Navier–Stokes equations
business
Subjects
Details
- Language :
- English
- ISSN :
- 19942060
- Database :
- OpenAIRE
- Journal :
- Engineering Applications of Computational Fluid Mechanics, Vol 12, Iss 1, Pp 74-88 (2018)
- Accession number :
- edsair.doi.dedup.....07ccd15496ba6c57d963d705c51d1c61