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author:

Luo, Yang (Luo, Yang.) [1] | Tao, Jianguo (Tao, Jianguo.) [2] | Li, Zhandong (Li, Zhandong.) [3] | Ding, Liang (Ding, Liang.) [4] | Deng, Zongquan (Deng, Zongquan.) [5] | Sun, Hao (Sun, Hao.) [6] (Scholars:孙浩) | Song, Xingguo (Song, Xingguo.) [7]

Indexed by:

CPCI-S

Abstract:

Cavitation effect generates when the propeller of the thruster revolves close to the free surface which can lead to thrust attenuation and high noise. In this paper, a Gaussian process regression based thruster model which considers cavitation effect is presented. The conventional thruster model is introduced at first. Afterwards, the error of the conventional thruster model caused by cavitation effect is analyzed and the cavitation prediction thruster model based on Gaussian process regression is proposed. The effectiveness of the proposed model is validated via proof-of-concept experiments. The experimental results demonstrate the accuracy of the proposed model with the error less than the conventional model.

Keyword:

cavitation Gaussian process thruster model underwater vehicle

Community:

  • [ 1 ] [Luo, Yang]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China
  • [ 2 ] [Tao, Jianguo]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China
  • [ 3 ] [Li, Zhandong]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China
  • [ 4 ] [Ding, Liang]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China
  • [ 5 ] [Deng, Zongquan]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China
  • [ 6 ] [Sun, Hao]Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou, Peoples R China
  • [ 7 ] [Song, Xingguo]Southwest Jiaotong Univ, Dept Mech Engn, Chengdu, Peoples R China

Reprint 's Address:

  • [Tao, Jianguo]Harbin Inst Technol, Dept Mech Engn, Harbin, Peoples R China

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Source :

2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017)

Year: 2017

Page: 575-580

Language: English

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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