Skip to main content
SpringerLink
Log in

Constrained Multi-objective Large Deformation Shape Optimization of Blended-Wing-Body Underwater Glider

  • Conference paper
  • First Online:
Advances in Mechanical Design (ICMD 2021)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 111))

Included in the following conference series:

  • 3775 Accesses

Abstract

There is a strong relationship of mutual influence between different performance indexes of Blended-Wing-Body Underwater Gliders (BWBUGs). For example, the shape with better hydrodynamic efficiency often limits the allowable internal volume, and further affects energy carrying capacity of BWBUGs. In this paper, two design objectives for BWBUGs are considered: lift-to-drag ratio (LDR) and internal volume, and the size and position of internal equipment are changeable. Due to the variable layout size and position, the interference between shape and internal layout is more likely to occur, which is a complex and harsh constraint. To solve this constrained multi-objective engineering problem, the surrogate-based TCOR-NSGA-II method is presented, where a new constraint-handling method (TCOR) is proposed to handle constraints more effectively. This novel constraint-handling technique combines with Non-dominated Sorting Genetic Algorithm (TCOR-NSGA-II) to tackle constrained multi-objective optimization problems. TCOR-NSGA-II has been tested on the MW test suites, and the experimental results show high effectiveness and strong robustness compared with several existing algorithms. Finally, the surrogate-based TCOR-NSGA-II is used for the shape optimization of BWBUG, and a set of non-dominant solutions are attained, which can provide a variety of glider shapes, including large LDR, large internal volume and trade-off individuals between the two indexes.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stommel, H.: The Slocum mission. Oceanography 2(1), 22–25 (1989)

    Article  Google Scholar 

  2. Webb, D.C., Simonetti, P.J., Jones, C.P.: SLOCUM: An underwater glider propelled by environmental energy. IEEE J. Oceanic Eng. 26(4), 447–452 (2001)

    Article  Google Scholar 

  3. Sherman, J., Davis, R.E., Owens, W.B., et al.: The autonomous underwater glider “Spray.” IEEE J. Oceanic Eng. 26(4), 437–446 (2001)

    Article  Google Scholar 

  4. Eriksen, C.C., Osse, T.J., Light, R.D., et al.: Sea glider: A long-range autonomous underwater vehicle for oceanographic research. IEEE J. Oceanic Eng. 26(4), 424–436 (2001)

    Article  Google Scholar 

  5. Yang, M., Wang, Y., Wang, S., Yang, S., Song, Y., Zhang, L.: Motion parameter optimization for gliding strategy analysis of underwater gliders. Ocean Eng. 191, 106502

    Google Scholar 

  6. Stevenson, P., Furlong, M., Dormer, D.: AUV design–shape, drag and practical issues. Sea Technol. 50(1), 41–44 (2009)

    Google Scholar 

  7. Hildebrand, J.A., D’Spain, G.L., Roch, M.A., et al.: Glider-based passive acoustic monitoring techniques in the southern California region. Scripps Institution of Oceanography la Jolla ca (2009)

    Google Scholar 

  8. D’ Spain, G.L., Jenkins, S.A., Zimmerman, R., et al.: Underwater acoustic measurements with the Liberdade/X‐ray flying wing glider. J. Acoust. Soc. Am. 117(4), 2624–2624 (2005)

    Google Scholar 

  9. D’Spain, G.L., Jenkins, S.A., Zimmerman, R., et al.: Underwater acoustic measurements with the Liberdade/X-ray flying wing glider. J. Acoust. Soc. Am. 117(4), 2624–2635 (2005)

    Article  Google Scholar 

  10. Haase, M., Seil, G., Allum, R., et al.: Underwater glider performance at model-scale and full-scale Reynolds numbers. In: Proceedings of Pacific 2017 International Maritime Conference (2017)

    Google Scholar 

  11. Yang, M., Wang, Y., Yang, S., et al.: Shape optimization of underwater glider based on approximate model technology. Appl. Ocean Res. 110(4), 102580 (2021)

    Google Scholar 

  12. Sun, C., Song, B., Peng, W.: Parametric geometric model and shape optimization of an underwater glider with blended-wing-body. Int. J. Naval Archit. Ocean Eng. 7(6), 995–1006 (2015)

    Article  Google Scholar 

  13. Fu, X., Lei, L., Yang, G., et al.: Multi-objective shape optimization of autonomous underwater glider based on fast elitist non-dominated sorting genetic algorithm. Ocean Eng. 157, 339–349 (2018)

    Article  Google Scholar 

  14. Coello, C.A., Veldhuizen, D., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer-Verlag, New York, Inc (2006)

    MATH  Google Scholar 

  15. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: First International Conference on Genetic Algorithms & Their Applications. Lawrence Erlbaum Associates. Inc. Publishers, pp. 93–100 (1985)

    Google Scholar 

  16. Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: A comparative case study and the strength Parato approach. IEEE Trans. Evol. Comput. 3(4), 257–260 (1999)

    Article  Google Scholar 

  17. Ded, K., Pratap, S., Agarwal, et al.: A fast and Elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans, Evol. Comput 6(2), 182–190 (2002)

    Google Scholar 

  18. Takahama, T., Sakai, S.: Constrained Optimization by the Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites. IEEE 1–8 (2006)

    Google Scholar 

  19. Runarsson, T.P., Xin, Y.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)

    Article  Google Scholar 

  20. Fan, Z., Li, W., Cai, X., et al.: Push and pull search for solving constrained multi-objective optimization problems. Swarm Evol. Comput. 44, 665–679 (2017)

    Article  Google Scholar 

  21. Liu, Z.Z., Wang, Y.: Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces. IEEE Trans. Evol. Comput. 1–1 (2019)

    Google Scholar 

  22. Zhu, Q., Zhang, Q., Lin, Q.: A constrained multiobjective evolutionary algorithm with detect-and-escape strategy. IEEE Trans. Evol. Comput. 99, 1–1 (2020)

    Google Scholar 

  23. Bai, J.B., Liu, T.W., Wang, Z.Z., et al.: Determining the best practice-Optimal designs of composite helical structures using genetic algorithms. Compos. Struct. 268, 113982 (2021)

    Google Scholar 

  24. Wang, Z., Bai, J., Adam, S., et al.: Optimal design of triaxial weave fabric composites under tension. Compos. Struct. 201, 616–624 (2018)

    Article  Google Scholar 

  25. Box, G.E.P., Draper, N.R.: Empirical model-building and response surfaces. Wiley, New York (1987)

    MATH  Google Scholar 

  26. Sacks, J., Welch, W.J., Mitchell, T.J., et al.: Design and analysis of computer experiments. Stat. Sci. 409–423 (1989)

    Google Scholar 

  27. Hardy, R.L.: Multiquadric equations of topography and other irregular surfaces. J. Geophys. Res. 76(8), 1905–1915 (1971)

    Article  Google Scholar 

  28. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  29. Vt, S.E., Shin, Y.C.: Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Trans. Neural Netw. 5(4), 594–603 (1994)

    Article  Google Scholar 

  30. Hicks, R.M., Henne, P.A.: Wing design by numerical optimization. J. Aircr. 15(7), 407–412 (1978)

    Article  Google Scholar 

  31. Kulfan, B., Bussoletti, J.: “Fundamental” parameteric geometry representations for aircraft component shapes. In: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference 6948 (2006)

    Google Scholar 

  32. Qu, B.Y., Suganthan, N.: Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods. Eng. Optim. 43(4), 403–416 (2011)

    Article  MathSciNet  Google Scholar 

  33. Oyama, A., Shimoyama, K., Fujii, K.: New constraint-handling method for multi-objective and multi-constraint evolutionary optimization. Trans. Jpn. Soc. Aeronaut. Space Sci. 50(167), 56–62 (2007)

    Article  Google Scholar 

  34. Ma, Z., Wang, Y., Song, W.: A new fitness function with two rankings for evolutionary constrained multiobjective optimization. IEEE Trans. Syst. Man Cybern. Syst. 99, 1–12

    Google Scholar 

  35. Ma, Z., Wang, Y.: Evolutionary constrained multiobjective optimization: Test suite construction and performance comparisons. IEEE Trans. Evol. Comput 23(6), 972–986 (2019)

    Article  MathSciNet  Google Scholar 

  36. Agrawal, R.B., Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(3), 115–148 (1994)

    MathSciNet  MATH  Google Scholar 

  37. Deb, K., Goyal, M.: A Combined Genetic Adaptive Search (GeneAS) for Engineering Design (1996)

    Google Scholar 

  38. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer Science & Business Media (2006)

    Google Scholar 

  39. Bosman, P.A.N., Thierens, D. et al.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174–18 (2003)

    Google Scholar 

  40. Tian, Y., Cheng, R., Zhang, X., Jin, Y.: PlatEMO: A MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)

    Article  Google Scholar 

  41. Dong, H., Dong, Z.: Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems. Swarm Evol. Comput. 57 (2020)

    Google Scholar 

  42. Dong, H., Sun, S., Song, B., et al.: Multi-surrogate-based global optimization using a score-based infill criterion. Struct. Multidiscip. Optim. (1) (2019)

    Google Scholar 

Download references

Acknowledgments

This project is supported by Support from National Natural Science Foundation of China (Grant No. 51875466, 51805436) is gratefully acknowledged. Besides, the research work is also supported by the Fundamental Research Funds for the Central Universities (Grant No. 3102020HHZY030003).

Author information

Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710068, China

    Wenyi Long, Peng Wang, Huachao Dong, Weixi Chen & Xubo Yang

Authors
  1. Wenyi Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huachao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weixi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xubo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Wang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, Zhejiang, China

    Jianrong Tan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Long, W., Wang, P., Dong, H., Chen, W., Yang, X. (2022). Constrained Multi-objective Large Deformation Shape Optimization of Blended-Wing-Body Underwater Glider. In: Tan, J. (eds) Advances in Mechanical Design. ICMD 2021. Mechanisms and Machine Science, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-7381-8_125

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-7381-8_125

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7380-1

  • Online ISBN: 978-981-16-7381-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation