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Cavitation Noise Spectra Prediction with Hybrid Models

  • Francesca CipolliniEmail author
  • Fabiana MigliantiEmail author
  • Luca OnetoEmail author
  • Giorgio TaniEmail author
  • Michele VivianiEmail author
  • Davide AnguitaEmail author
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

In many real world applications the physical knowledge of a phenomenon and data science can be combined together in order to get mutual benefits. As a result, it is possible to formulate a so-called hybrid model from the combination of the two approaches. In this work, we propose an hybrid approach for the prediction of the ship propeller cavitating vortex noise, adopting real data collected during extensive model scale tests in a cavitation tunnel. Results will show the effectiveness of the proposal.

Keywords

Cavitation noise prediction Physical models Data-Driven Models Hybrid Models 

References

  1. 1.
    Bosschers, J.: Investigation of hull pressure fluctuations generated by cavitating vortices. In: Proceedings of the First Symposium on Marine Propulsors (2009)Google Scholar
  2. 2.
    Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Evgeniou, T., Pontil, M.: Regularized multi–task learning. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)Google Scholar
  4. 4.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  5. 5.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence (1995)Google Scholar
  6. 6.
    McCormick, B.W.: On cavitation produced by a vortex trailing from a lifting surface. J. Basic Eng. 84(3), 369–378 (1962)CrossRefGoogle Scholar
  7. 7.
    Raestad, A.: Tip vortex index-an engineering approach to propeller noise prediction. The Naval Architect, pp. 11–15 (1996)Google Scholar
  8. 8.
    Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  9. 9.
    Tani, G., Aktas, B., Viviani, M., Atlar, M.: Two medium size cavitation tunnel hydro-acoustic benchmark experiment comparisons as part of a round robin test campaign. Ocean. Eng. 138, 179–207 (2017)CrossRefGoogle Scholar
  10. 10.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.DIBRIS - University of GenoaGenovaItaly
  2. 2.DITEN - University of GenoaGenovaItaly

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