Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

doi:10.1007/s12204-017-1882-9

上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (6): 657-663.doi: 10.1007/s12204-017-1882-9

Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

HOU Yuanhang* (候远杭), YOU Yuan (游园), LIANG Xiao (梁霄)

(Transportation Equipment and Ocean Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China)

出版日期:2017-12-01 发布日期:2017-12-03
通讯作者: HOU Yuanhang (候远杭) E-mail:houyuanhang6@163.com

Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

HOU Yuanhang* (候远杭), YOU Yuan (游园), LIANG Xiao (梁霄)

(Transportation Equipment and Ocean Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China)

Online:2017-12-01 Published:2017-12-03
Contact: HOU Yuanhang (候远杭) E-mail:houyuanhang6@163.com

摘要/Abstract

摘要： In the ship hull optimization design based on simulation-based design (SBD) technology, low precision of the approximate model leads to an uncertainty form of optimization model. In order to enable the approximate model with finite precision to maximize the effectiveness, uncertainty optimization method is introduced here. Wave resistance coefficient approximation model, built by back propagation (BP) neural network, is represented as a form of interval. Afterwards, a minimum resistance optimization model is established with the design space constituted by principal dimensions and ship form coefficients. Double-level nested optimization architecture is proposed: for outer layer, improved particle swarm optimization (IPSO) algorithm with learning factor improvement strategy is used to generate design variables, and for inner layer, modified very fast simulated annealing (MVFSA) algorithm is used to solve the objective function interval with uncertainty region. Cases calculation proves the effectiveness and superiority of uncertainty optimization method for ship hull SBD optimization design, thus providing a good way for finding optimal designs.

关键词: ship hull optimization, approximation model, uncertainty optimization, interval programming

Abstract: In the ship hull optimization design based on simulation-based design (SBD) technology, low precision of the approximate model leads to an uncertainty form of optimization model. In order to enable the approximate model with finite precision to maximize the effectiveness, uncertainty optimization method is introduced here. Wave resistance coefficient approximation model, built by back propagation (BP) neural network, is represented as a form of interval. Afterwards, a minimum resistance optimization model is established with the design space constituted by principal dimensions and ship form coefficients. Double-level nested optimization architecture is proposed: for outer layer, improved particle swarm optimization (IPSO) algorithm with learning factor improvement strategy is used to generate design variables, and for inner layer, modified very fast simulated annealing (MVFSA) algorithm is used to solve the objective function interval with uncertainty region. Cases calculation proves the effectiveness and superiority of uncertainty optimization method for ship hull SBD optimization design, thus providing a good way for finding optimal designs.

Key words: ship hull optimization, approximation model, uncertainty optimization, interval programming

中图分类号:

U 661.1

HOU Yuanhang* (候远杭), YOU Yuan (游园), LIANG Xiao (梁霄). Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method[J]. 上海交通大学学报（英文版）, 2017, 22(6): 657-663.

HOU Yuanhang* (候远杭), YOU Yuan (游园), LIANG Xiao (梁霄). Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method[J]. Journal of Shanghai Jiao Tong University (Science), 2017, 22(6): 657-663.

参考文献 14

[1]	LIU P, ZHANG H, CHENG H, et al. An overview on hull form generations and geometric representation methods of surface naval ship [J]. Ship Science and Technology, 2014, 36(6): 1-6 (in Chinese).
[2]	QIAN J K, MAO S F, WANG X Y, et al. Ship hull automated optimization of minimum resistance via CFD and RSM technique [J]. Journal of Ship Mechanics,2012, 16(1/2): 36-43 (in Chinese).
[3]	KAMI′NSKI B. A method for the updating of stochastic Kriging meta-models [J]. European Journal of Operational Research, 2015, 247(3): 859-866.
[4]	LI N, CHEN M, LIU F, et al. Optimization research of ship parameters for FRP fishing vessels based on GRNN and genetic algorithm [J]. Ship Engineering,2012, 34(4): 18-22 (in Chinese).
[5]	PRACZYK T. Using evolutionary neural networks to predict spatial orientation of a ship [J]. Neurocomputing,2015, 166(10): 229-243.
[6]	GEN M, CHENG R W. Optimal design of system reliability using interval programming and genetic algorithms[J]. Computers and Industrial Engineering,1996, 31(1/2): 237-240.
[7]	CASTRO J. A stochastic programming approach to cash management in banking [J]. European Journal of Operational Research, 2009, 192(3): 963-974.
[8]	LI F Y, LI G Y, ZHENG G. Uncertain multi-objective optimization method based on interval [J]. Chinese Journal of Solid Mechanics, 2010, 31(1): 86-93 (in Chinese).
[9]	GONG G W, SUN J. Theory and application of interval multi-objective evolutionary optimization [M].Beijing: Science Press, 2013: 1-22 (in Chinese).
[10]	HU J G, ZHOU G M, XU X J. Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data [J]. Ecological Modelling, 2013, 266(12): 86-96.
[11]	ZHANG H P, HAN D F, GUO C Y. Modeling of the principal dimensions of large vessels based on a BPNN trained by an improved PSO [J]. Journal of Harbin Engineering University, 2012, 33(7): 806-810(in Chinese).
[12]	CHEN H G, LI L H, XU H P, et al. Modified very fast simulated annealing algorithm [J]. Journal of Tongji University (Natural Science), 2006, 34(8): 1121-1125(in Chinese).
[13]	SAHA G K, SUZUKI K, KAI H. Hydrodynamic optimization of ship hull forms in shallow water [J]. Journal of Marine Science and Technology, 2004, 9(9): 51-62.
[14]	GOTMAN A S. Study of Michell’s integral and influence of viscosity and ship hull form on wave resistance[J]. Oceanic Engineering International, 2002, 6(2): 74-115.

Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method

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