Skip to main content
Log in

Multiple objective multidisciplinary design optimization of heavier-than-water underwater vehicle using CFD and approximation model

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

As ocean resources received considerable attention, the autonomous underwater vehicle (AUV) has been more widely applied due to its excellent flexibility and adaptability, and the optimization design of AUV has becoming a more important issue. The heavier-than-water underwater vehicle (HUV) is a new conceptual AUV, and its design is a typical multidisciplinary problem, but heavily dependent on the experience with naval architects at the present engineering design. To realize the optimization design of HUV, the multidisciplinary design optimization (MDO) method is applied to the multiple objective MDO design of an HUV. In this paper, a system synthesis model of the HUV has been set up. To predict the hydrodynamic characteristics accurately, the computational fluid dynamics (CFD) method is used. In the MDO process, the all-in-one (AIO) method has been adopted with non-dominated sorting genetic algorithms (NSGA)-II and a Kriging model for constructing global approximations to reduce the cost of the CFD numerical simulation. According to the multiple objective MDO design, the Pareto-optimal solutions are analyzed and verified by the numerical simulation, and it is found that the optimal design of HUV has better performance than that of prototype.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Barry JP, Hashimoto J (2009) Revisiting the challenger deep using the ROV Kaiko. Mar Technol Soc J 43(5):77–78

    Article  Google Scholar 

  2. Wynn Russell B, Huvenne Veerle AI, Le Bas Timothy P et al (2014) Autonomous underwater vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience. Mar Geol 352:451–468

    Article  Google Scholar 

  3. Li X, Zhao M, Zhao FM, Yuan QQ, Ge T (2014) Study on hydrodynamic performance of heavier-than-water AUV with overlapping grid method. Ocean Syst Eng 4(1):1–19

    Article  Google Scholar 

  4. Wu C, Wang Q, Yan H (2010) Practicability research and design of the underwater plane. In: Proceedings of the ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2010-20266, Shanghai, China

  5. Yan H, Ge T, Ying SB, Wu C, Yuan QQ (2012) Analysis of motion in longitudinal plane of negative buoyancy vehicle flying fish II. J Shanghai Jiao Tong Univ (Sci) 17(1):20–24

    Article  Google Scholar 

  6. Sobieszczanski-Sobieski J (1989) Multidisciplinary optimization for engineering systems: achievements and potential. Springer, Berlin Heidelberg

    Google Scholar 

  7. Malone B, Mason WH (1995) Multidisciplinary optimization in aircraft design using analytic technology models. J Aircr 32(2):431–438

    Article  Google Scholar 

  8. Peri D, Campana EF (2003) Multidisciplinary design optimization of a naval surface combatant. J Ship Res 47(1):1–12

    Google Scholar 

  9. Martz M, Neu WL (2009) Multi-objective optimization of an autonomous underwater vehicle. Mar Technol Soc J 43(2):48–60

    Article  Google Scholar 

  10. McAllister CD, Simpson TW, Kurtz PH, Yukish M (2002) Multidisciplinary design optimization testbed based on autonomous under water vehicle design. In: Proceedings of 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia, USA

  11. Cramer EJ, Dennis JE, Frank PD, Lewis RM, Shubin GR (1994) Problem formulation for multidisciplinary optimization. SIAM J 4(4):754–776

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhao M, Cui WC, Ge T (2015) Multidisciplinary design optimization of a heavier-than-water underwater vehicle using a semi-empirical model. Submitted to Ocean Engineering

  13. Simpson TW, Booker AJ, Ghosh D, Giunta AA, Koch PN, Yang RJ (2004) Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Struct Multidiscip Optim 27(5):302–313

    Article  Google Scholar 

  14. Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12

    Article  Google Scholar 

  15. Simpson TW, Mauery TM, Korte JJ, Mistree F (2001) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J 39(12):2233–2241

    Article  Google Scholar 

  16. Yan H (2012) Investigation on design, navigation and motion performance of a heavier-than-water AUV. Ph.D Thesis, Shanghai Jiao Tong University (in Chinese)

  17. Jasak H, Jemcov A, Tukovic Z (2007) OpenFOAM: a C++ library for complex physics simulations. In: International workshop on coupled methods in numerical dynamics, Dubrovnik, Croatia

  18. Alvarez L (2000) Design optimization based on genetic programming. University of Bradford, UK

    Google Scholar 

  19. Jin R, Chen W, Sudjianto A (2005) An efficient algorithm for constructing optimal design of computer experiments. J Stat Plan Inference 134(1):268–287

    Article  MathSciNet  MATH  Google Scholar 

  20. Simpson TW, Mauery TM, Korte JJ, Mistree F (1998) Comparison of response surface and kriging models in the multidisciplinary design of an aerospike nozzle (No. 98). Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA, USA

  21. Zhao M, Cui WC (2009) System synthesis model for a HOV in conceptual design. J Ship Mech 13(3):426–443

    Google Scholar 

  22. Nystrom JW (1863) A treatise on parabolic construction of ships and other marine engineering subjects. JB Lippincott & Company, London, British

  23. Phillips A, Furlong M, Turnock SR (2007) The use of computational fluid dynamics to assess the hull resistance of concept autonomous underwater vehicles. In: Proceedings of OCEANS2007-Europe. Institute of Electrical and Electronics Engineers, Richardson, TX, USA

  24. American Bureau of Shipping (ABS) (2015) Rules for building and classing-underwater vehicles, 2015. System and Hyperbaric Facilities, Houston, TX, USA

  25. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lect Notes Comput Sci 1917:849–858

    Article  Google Scholar 

  26. Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  27. Zhu JM (1992) Underwater vehicle design. Shanghai Jiao Tong University Press, China (in Chinese)

    Google Scholar 

  28. ITTC Recommended Procedures and Guidelines, 7.5-03-01-01 (2008) Uncertainty analysis in CFD-verification and validation methodology and procedures

  29. Zhao M, Cui WC (2007) Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization. J Mar Sci Appl 6(3):24–32

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51109132) and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110073120015). Critical comments from reviewers are greatly appreciated.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Min Zhao.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Yuan, Q., Zhao, M. et al. Multiple objective multidisciplinary design optimization of heavier-than-water underwater vehicle using CFD and approximation model. J Mar Sci Technol 22, 135–148 (2017). https://doi.org/10.1007/s00773-016-0399-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-016-0399-5

Keywords

Navigation