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Empirical Formulae to Presume the Resistance Performance of High Speed Craft Models

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Computational Stochastic Mechanics

Abstract

This paper follows the previous one published at PACON 90. Analyzing 78 towing test data in still water of high speed craft 2.5 m length models by so-called principal component analyses, a presumption of resistance performance including total resistance coefficient, change of trim and of draft is proposed by using eleven non-dimensional hull form parameters including length, width, height of chine at midship, initial trim and so on. Results by two kinds of empirical formulae are favorably compared with the test data. Five more new models were tested to reconfirm those formulae, and we obtained reasonable correspondencies. Both formulae, therefore, are adequately clarified as applicable, defining two kinds of applicable ranges inside such as either the tolerance ellipse containing 95% data or the Cramér’s concentrated ellipse containing 75 % data.

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References

Paper in Conference Proceedings

  1. Yoshida, Y. and Nagai, T. Numerical Determination of the Lift Distribution of High Speed Craft, pp.235–242, Vol. I, Proceedings of the Fourth Pacific Congress on Marine Science and Technology (PACON 90), Tokyo, Japan, Jul. 16–20, 1990.

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Paper in journals

  1. Yoshida, Y. and Nagai, T. Principal Component Analysis and Factor Analysis referring to Model Test Results in Still Water of High Speed Craft, Jour. Soc. Naval Arch. of Japan, Vol.140, pp.58–66, 1976

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  2. Nagai, T., Tanaka, H. and Yoshida, Y. Statistical Analyses of Model Test Results in Still Water of High Speed Craft, Jour. Soc. Naval Arch. of Japan, Vol.137, pp.48–57, 1975.

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Book

  1. Cramér, H. Mathematical Methods of Statistics, Princeton University Press, Princeton, 1946.

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© 1991 Computational Mechanics Publications

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Nagai, T., Yoshida, Y. (1991). Empirical Formulae to Presume the Resistance Performance of High Speed Craft Models. In: Spanos, P.D., Brebbia, C.A. (eds) Computational Stochastic Mechanics. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3692-1_17

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  • DOI: https://doi.org/10.1007/978-94-011-3692-1_17

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-85166-698-0

  • Online ISBN: 978-94-011-3692-1

  • eBook Packages: Springer Book Archive

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