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An effective approximation modeling method for ship resistance in multidisciplinary ship design optimization

An effective approximation modeling method for ship resistance in multidisciplinary ship design optimization
An effective approximation modeling method for ship resistance in multidisciplinary ship design optimization
Ship design is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional ship design process only involves independent design optimization with some regression formulas within each discipline. With such an approach, there is no guarantee to achieve the optimum design. At the same time, it is also crucial for modem ship design to improve the efficiency of ship optimization. Nowadays, Computational fluid dynamics (CFD) has brought into ship design optimization. However, there are still some problems such as modeling, calculation precision and time consumption even when CFD software is inlaid into the optimization procedure. Modeling is a far-ranging and all-around subject, and its precision directly affects the scientific decision in future. How to use an algorithm to establish a statistical approximation model instead of the CFD calculation will be the key problem. The Support Vector Machines (SVM), a new general machine learning method based on the frame of statistical learning theory, may solve the problems of non-linear classification and regression in sample space and be an effective method of processing the non-liner classification and regression. Recently, Support Vector Regression (SVR) has been introduced to solve regression and modeling problems and been used in wide fields. The classical SVR has two parameters to control the errors. A new algorithm of Support Vector Regression proposed in this article has only one parameter to control the errors, adds b2/2 to the item of confidence interval at the same time, and adopts the Laplace loss function. It is named Single-parameter Lagrangian Support Vector Regression (SPLSVR). This effective algorithm can improve the operation speed of program to a certain extent, and has better fitting precision. In practical design of ship, Design of Experiment (DOE) and the proposed support vector regression algorithm are applied to ship design optimization to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. The result indicates that SPLSVR method to establish approximate models can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.
V002T08A023-[9pp]
ASME
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Zhao, Xin
634e60cd-764f-48ab-9759-673fe78842d4
Guan, Yifeng
dd54db9c-2a8e-4153-8ce9-5978504b1d0a
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Zhao, Xin
634e60cd-764f-48ab-9759-673fe78842d4
Guan, Yifeng
dd54db9c-2a8e-4153-8ce9-5978504b1d0a

Li, Dongqin, Wilson, P.A., Zhao, Xin and Guan, Yifeng (2014) An effective approximation modeling method for ship resistance in multidisciplinary ship design optimization. In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. vol. 2, ASME. V002T08A023-[9pp] . (doi:10.1115/OMAE2014-23407).

Record type: Conference or Workshop Item (Paper)

Abstract

Ship design is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional ship design process only involves independent design optimization with some regression formulas within each discipline. With such an approach, there is no guarantee to achieve the optimum design. At the same time, it is also crucial for modem ship design to improve the efficiency of ship optimization. Nowadays, Computational fluid dynamics (CFD) has brought into ship design optimization. However, there are still some problems such as modeling, calculation precision and time consumption even when CFD software is inlaid into the optimization procedure. Modeling is a far-ranging and all-around subject, and its precision directly affects the scientific decision in future. How to use an algorithm to establish a statistical approximation model instead of the CFD calculation will be the key problem. The Support Vector Machines (SVM), a new general machine learning method based on the frame of statistical learning theory, may solve the problems of non-linear classification and regression in sample space and be an effective method of processing the non-liner classification and regression. Recently, Support Vector Regression (SVR) has been introduced to solve regression and modeling problems and been used in wide fields. The classical SVR has two parameters to control the errors. A new algorithm of Support Vector Regression proposed in this article has only one parameter to control the errors, adds b2/2 to the item of confidence interval at the same time, and adopts the Laplace loss function. It is named Single-parameter Lagrangian Support Vector Regression (SPLSVR). This effective algorithm can improve the operation speed of program to a certain extent, and has better fitting precision. In practical design of ship, Design of Experiment (DOE) and the proposed support vector regression algorithm are applied to ship design optimization to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. The result indicates that SPLSVR method to establish approximate models can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

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Submitted date: 15 January 2014
Accepted/In Press date: 15 February 2014
Published date: June 2014
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 364413
URI: http://eprints.soton.ac.uk/id/eprint/364413
PURE UUID: a4d656d6-e5a2-4e4b-bcf4-5197f338605b
ORCID for P.A. Wilson: ORCID iD orcid.org/0000-0002-6939-682X

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Date deposited: 10 Oct 2014 10:17
Last modified: 09 Jan 2022 02:34

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Contributors

Author: Dongqin Li
Author: P.A. Wilson ORCID iD
Author: Xin Zhao
Author: Yifeng Guan

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