Numerical Prediction of Self-propulsion Point of AUV with a Discretized Propeller and MFR Method

  • Lihong WuEmail author
  • Xisheng Feng
  • Xiannian Sun
  • Tongming Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)


It is important to determine the self-propulsion point for marine vehicles to evaluate the approaching velocity output from a determined propeller. A method is presented that significantly reduces the computational cost by coupling a discretized propeller with a MFR (Multiple Frames of Reference) method for evaluation of the propulsion factors of AUV (Autonomous Underwater Vehicle). The predicted approaching velocity in this study was approximately 2.8% lower than the design value of 1.0 m/s obtained using nominal wake fraction, which can be attributed to increased energy dissipation for the water at the wake caused by the propeller. The effective wake fraction was 0.303 and the thrust deduction was 0.163.Vortex pairing was found at the blade tip and developed downstream of the propeller. In addition, the hull and tail-planes were beneficial for improving the thrust of the propeller. The proposed method is a viable option to validate fluid dynamics analyses of the unsteady motion of self-propelled marine vehicles simulated with physics-based methods, particularly for cases which have a shortage of experimental data.


Self-propulsion point AUV Discretized propeller MFR 



The authors are grateful to the Chinese Scholarship Council (CSC), the State Key Laboratory of Robotics, the Natural Science Foundation of China (with Grant No. 51009016 and 51409047) and the Fundamental Research Funds for the Central Universities (with Grant No. 3132017030, 3132018206) for their financial support, as well as the University of Western Australia (UWA in Australia) for providing facilities for simulations. In addition, many thanks should be given to the underwater vehicle center of SIA (Shenyang Institute of Automation, China) for providing the AUV model and some experimental data for validation. Grateful acknowledgement should also be given to Professor Xiannian Sun, who helped a lot in editing the manuscript.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ship Building and Ocean Engineering CollegeDalian Maritime UniversityDalianChina
  2. 2.State Key Laboratory of RoboticsShenyang Institute of Automation, Chinese Academy of SciencesShenyangChina
  3. 3.School of Civil and Resource EngineeringThe University of Western AustraliaCrawleyAustralia

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