TRANSACTIONS OF THE KRYLOV STATE RESEARCH CENTRE

Science journal

 
ISSN (print) 2542-2324 /(online) 2618-8244


Articles of The Transactions of KSRC








Hybrid neural network enabling protection potential prediction for underwater parts of marine platforms



Full text article ( in russian)

Year

 
2019

Issue

 
20192

Volume

 
2

Pages

 
254-262

Caption

 
Hybrid neural network enabling protection potential prediction for underwater parts of marine platforms

Authors

 
Rodkina A., Kramar V., Gramuzov Ye., Ivanova O.

Keywords

 
neural network, corrosion, potential, polarization

DOI

 
10.24937/2542-2324-2019-2-S-I-254-262

Summary

 
This paper discusses hull structures of ships and floating platforms. The purpose is to develop a hybrid neural network that would enable protection potential prediction for underwater hull structures in terms of corrosion and mechanical damage, with intermediate prediction of basic parameters (potential of steel with and without oxide surface layer) for different steels and water salinities. This study made it possible to develop prediction software for protective properties of steel underwater structures of ships and other marine platforms that ensures the potential of uncharged steel surface, including the juvenile one. This software can accurately calculate protection potential, which paves way to development of hull structures for ships and marine platforms more resistant to corrosion and mechanical damage thanks to stabilization of cathodic polarization process at the potential of uncharged surface. This protection will prevent corrosion and mechanical damage of hull structures for ships and marine platforms in service and under design.

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