Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics-Reference-Cited by-同舟云学术

Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics

Published:2017-03-14 Issue:2 Volume:17 Page:59-84
ISSN:2046-4177
Container-title:Journal of Marine Engineering & Technology
language:en
Short-container-title:Journal of Marine Engineering & Technology

Author:

Shora Mohammad Mahdi¹,Ghassemi Hassan¹^ORCID,Nowruzi Hashem¹

Affiliation:

1. Department of Maritime Engineering, Amirkabir University of Technology, Tehran, Iran

Publisher

Informa UK Limited

Subject

Ocean Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/20464177.2017.1300983

Reference31 articles.

1. Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method

2. Error measures for generalizing about forecasting methods: Empirical comparisons

3. Effects of the advance ratio on the evolution of a propeller wake

4. Application of neuro-fuzzy approach in prediction of runup in swash zone

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