This paper presents a rational approach to the design of a catamaran's hydrofoil applied within a modern context of multidisciplinary optimization. The approach used includes the use of response surfaces represented by neural networks and a distributed programming environment that increases the optimization speed. A rational approach to the problem simplifies the complex optimization model; when combined with the distributed dynamic training used for the response surfaces, this model increases the efficiency of the process. The results achieved using this approach have justified this publication.
ShyyWPapilaNVaidyanathanRTuckerK‘Global design optimization for aerodynamics and rocket propulsion component.’Prog. Aerospace Sci.37 (2001): 59–118
2.
BirkLClaussG F‘Automated hull optimization of offshore structures based on rational seakeeping criteria.’ In Proceedings of the 11th International Offshore and Polar Engineering Conference (ISOPE 2001), Stavanger, Norway, 2001, pp. 382–389.
3.
GiassiABennisFMaisonneuve1J‘Multi-objective optimization of ship hull with distributed applications.’ In Proceedings of the ASME Design Engineering Technical Conference and Computers and Information in Engineering Conference, 2003, paper DETC2003/DAC–48732 (American Society of Mechanical Engineers, New York).
4.
AugustoO B, Processos de Busca não Linear para o Projeto da Estrutura do Navio, 1996, report BT/PNV/23, EPUSP, Brazil Trans. Algorithms of non-linear search for the ship's structure design
5.
AnjumM FTasadduqITasadduqK‘Response surface methodology: A neural network approach.’Eur. J. Opl Res.101 (1) (1997): 65–73
6.
Agatonovic-KustrinSZecevicMZivanovicLj.TuckerI G‘Application of neural networks for response surface modeling in HPLC optimization.’Analytica Chim. Acta364 (1–3) (1998): 265–273
7.
BatillS MStelmackM AYuX Q‘Multidisciplinary design optimization of an electric-powered unmanned air vehicle.’Aircraft Des.2 (1) (1999): 1–18
8.
ZeboudjSEboudjSBelhaneche-BensemraNBelabbesR‘Use of surface response methodology for the optimization of the concentration of the sweet orange essential oil of Algeria by wiped film evaporator.’J. Food Engng67 (4) (2005): 507–512
9.
EdwardsI MJutanA‘Optimization and control using response surface methods,’Computers Chem. Engng21 (4) (1997): 441–453
10.
ZivianiN‘Projeto e Análise de Algoritmos com Implementações em C e PASCAL,’ (Pioneira, Thomson Learning1993)
11.
AndrewsG RFoundations of multithread, parallel and distributed programming (Addison WesleyUSA: 1999)
12.
JúniorW F PHiperquicksort: uma análise prática com implementação em MPI. Monografia de Graduação, 2002 Univ. Federal de Lavras, Minas Gerais, Brasil). (Trans: Hyperquicksort: a practical analysis with implementation in MPI
13.
AlmasiG. S. E. G.A highly parallel computing, edition 2 (The Benjamins Cumming Publishing Company, Redwood City, CA, USA1994)
14.
TakaharaJTakayamaKNagaiT‘Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations.’J. Controlled Release49 (1) (1997): 11–20
15.
ZhuJ ZChangC SYanWXuG Y‘Reactive power optimization using an analytic hierarchical process and a nonlinear optimization neural network approach.’Proc. IEE, Generation, Transmission, Distribution145 (1) (1998): 89–97
16.
ShuaeibF MHamoudaA M SWongS VUmarR SAhmedM M H‘A new motorcycle helmet liner material: The finite element simulation and design of experiment optimization.’Mater. Des.28 (1) (2007): 182–195
GoodNBrownA‘Multi-objective concept design of an advanced logistics delivery system ship (ALDV).’ In Proceedings of ASNE Joint Sea Basing Symposium, Arlington, Virginia, 2006.
20.
21.
BelegunduA DHalbergEYukishM ASimpsonT W‘Attribute-based multidisciplinary optimization of undersea vehicles.’ In Proceedings of 8th AIAA/USAF/NASA/ISSMO Symposium onMultidisciplinary analysis and optimization, Long Beach, California, 2000 (American Institute of Aeronautics and Astronautics, Reston, Virginia).
22.
CarbonoA J JMenezesI F MMarthaL F‘Mooring pattern optimization using genetic algorithms.’ In Proceedings of the 6th World Congress onStructural and multidisciplinary optimization, Rio de Janeiro, Brazil, 2005 (ISSMO).
23.
Sobieszczanski-SobieskiJHaftkaR T‘Multi-disciplinary aerospace design optimization: Survey of recent developments.’ In Structural optimization 14, 1997, pp. 1–23 (NASA Langley Research Center, Hampton, USA).
DreyerTAugustoO BFouadB, Catamaran project: Report about the structural model of an hydrofoil, PNV, Universidade de Sao Paulo, Brazil, 2005.
26.
AugustoO BTancrediT PBennisF‘O uso de Redes Neurais Artificiais na Viabilização e Otimização de Projetos Offshore.’ In the Proceedings of Conferência Nacional emInteligência Computacional Aplicada à Indústria de Petróleo, Rio de Janeiro, 2007. (Trans: The use of artificial neural networks in the development and optimization of offshore designs.)
27.
TancrediT PAugustoO BFouadB‘Design optimization using response surface with dynamic training in distributed processing with load balance using the internet.’ In Proceedings of the International Conference onParallel and Distributed Processing Techniques and Applications, PDPTA 2007, Las Vegas, USA, 2007 (CSREA Press).
28.
MurrayA FApplications of neural networks (University of Edinburgh,UK1995)
29.
BaşDBoyacİ. H.‘Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction.’J. Food Engng78 (3) (2005): 846–854
30.
HaftkaR TGürdalZElements of structural optimization: Solid mechanics and its applications (Kluwer Academic Publishers, Dordrecht, The Netherlands1995)
31.
MollandA FWellicomeJ FCouserP R‘Resistance experiments on a systematic series of high speed displacement catamarans forms: Variation of length–displacement ratio and breadth–draught ratio.’Trans. R. Instn Nav. Architects138 (1996): 45–68
32.
HoltropJMennenG G J‘An approximate power prediction method.’Int. Shipbuilding Prog.29 (1982): 166–170
33.
MasuyanaY‘Stability analysis and prediction of performance for a hydrofoil sailing boat 1/2.’Int. Shipbuilding Prog.33 (1986): 1986 (1)–1987 (2)
34.
KodiyalamS, Evaluation of methods for multidisciplinary design optimization (MDO), NASA/CR-1998–208716, NASA, 1998.
