Novel Approach to Ship Multidisciplinary Design and Optimization Using Genetic Algorithm and Response Surface Method

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Abstract:

Ship design is a complex engineering effort required excellent coordination between the various disciplines and essentially applies iteration to satisfy the relevant requirements, such as stability, power, weight, and strengths. Through, all-in-one Multidisciplinary Design Optimization (MDO) approach is proposed to get the optimum performance of the ship considering three disciplines, power of propulsion, ship loads and structure. In this research a Latin Hypercube Sampling (LHS) is employed to improve the space filling property of the design and explore it to sample data for covering the design space. To avoid the problem of huge calculation time and saving the develop time, a quadratic Response Surface Method (RSM) is adopted as an approximation model to study the relation between a set of design variables and the system output for solving the system design problems. A genetic algorithm (GA) is adopted as search technique used in computing to find exact or approximate solutions to optimize and search problems and appropriate design result in MDO in ship design. Finally, the validity of the proposed approach is proven by a case study of a bulk carrier.

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Periodical:

Advanced Materials Research (Volumes 118-120)

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967-971

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June 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y.S. Yang, C.K. Park, K. Lee and J. Suh: Structural and Multidisciplinary Optimization, Volume 33, Number 6, pp.529-539. (2006).

Google Scholar

[2] G.E.P. Box and N.R. Draper:. Jon Wiley & Sons, New York. (1987).

Google Scholar

[3] M. Petelet, B. Iooss, O. Asserin and A. Loredo: Latin hypercube sampling with inequality constraints. Advances in Statistical Analysis, Online. (2009).

DOI: 10.1007/s10182-010-0144-z

Google Scholar

[4] R. Unal, R.A. Lepsch and W. Engelund: AIAA Paper 96-4044, pp.592-598. (1996).

Google Scholar

[5] S.N. Sivanandam and S.N. Deepa: Introduction to Genetic Algorithms. Springer, P. 30-31. (2008).

Google Scholar

[6] Genetic Algorithm and Direct Search Toolbox: User's Guide, by Math Works. (2004).

Google Scholar

[7] X.Y. Shao, X.Z. Chu, H.B. Qiu, L. Gao and J. Yan: Elsevier, Vol. 36 (2), pp.3223-3233. (2009).

Google Scholar