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Neural network modeling of submarine shell

Neural network modeling of submarine shell
Neural network modeling of submarine shell
Submarine propeller operating in unsteady wake flow often results in dynamic fluctuating force which is transmitted through rotator-raft system and leads to shell structure vibration and structure-borne noise transmission. Even though methods like Flügge’s equations of motion or FEM can be used to model cylinder shell, as for submarine equipped with devices the vibration behaviour of shell will not agree with that acquired from model developed with those methods. In this paper, Neural Network is used to analyse vibration of the submarine shell structure consisting of conical shell, hull and hemisphere shell. Firstly, a finite element model for the combined shell structure is developed for numerical simulations, whose aim is to acquire shell’s vibration information so as to represent the actual output of sensors arranged on submarine shell. Then actuating force is applied on this model based on the prior knowledge of typical blade passing frequency and fluctuating force. The vibration characteristics and responses at the interested positions on the shell are analyzed numerically. These data are used in Neural Network as training and testing data respectively to further develop a non-linear submarine shell model. Vibration responses at the same points acquired from finite element model are compared with the Neural Network model respectively. Results show that Neural Network can be used to model the complex submarine shell structure for effective vibration analysis
978-3-319-09917-0
1055-1064
Springer
Wang, Fei
d74a565b-35c9-43af-95ba-06782f81a561
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Weng, Zhenping
be7768cd-09e9-4ba3-89cb-625bee19c2a2
Wang, Fei
d74a565b-35c9-43af-95ba-06782f81a561
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Weng, Zhenping
be7768cd-09e9-4ba3-89cb-625bee19c2a2

Wang, Fei, Xiong, Yeping and Weng, Zhenping (2014) Neural network modeling of submarine shell. In Vibration Engineering and Technology of Machinery. vol. 23, Springer. pp. 1055-1064 . (doi:10.1007/978-3-319-09918-7_93).

Record type: Conference or Workshop Item (Paper)

Abstract

Submarine propeller operating in unsteady wake flow often results in dynamic fluctuating force which is transmitted through rotator-raft system and leads to shell structure vibration and structure-borne noise transmission. Even though methods like Flügge’s equations of motion or FEM can be used to model cylinder shell, as for submarine equipped with devices the vibration behaviour of shell will not agree with that acquired from model developed with those methods. In this paper, Neural Network is used to analyse vibration of the submarine shell structure consisting of conical shell, hull and hemisphere shell. Firstly, a finite element model for the combined shell structure is developed for numerical simulations, whose aim is to acquire shell’s vibration information so as to represent the actual output of sensors arranged on submarine shell. Then actuating force is applied on this model based on the prior knowledge of typical blade passing frequency and fluctuating force. The vibration characteristics and responses at the interested positions on the shell are analyzed numerically. These data are used in Neural Network as training and testing data respectively to further develop a non-linear submarine shell model. Vibration responses at the same points acquired from finite element model are compared with the Neural Network model respectively. Results show that Neural Network can be used to model the complex submarine shell structure for effective vibration analysis

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More information

Published date: 9 September 2014
Venue - Dates: VETOMAC X, United Kingdom, 2014-09-01
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 370603
URI: http://eprints.soton.ac.uk/id/eprint/370603
ISBN: 978-3-319-09917-0
PURE UUID: f3f0a071-3d61-45b4-9f57-c426ee294794
ORCID for Yeping Xiong: ORCID iD orcid.org/0000-0002-0135-8464

Catalogue record

Date deposited: 31 Oct 2014 15:19
Last modified: 03 Dec 2019 01:53

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Contributors

Author: Fei Wang
Author: Yeping Xiong ORCID iD
Author: Zhenping Weng

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