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A Double-Stage Surrogate-Based Shape Optimization Strategy for Blended-Wing-Body Underwater Gliders

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Abstract

In this paper, a Double-stage Surrogate-based Shape Optimization (DSSO) strategy for Blended-Wing-Body Underwater Gliders (BWBUGs) is proposed to reduce the computational cost. In this strategy, a double-stage surrogate model is developed to replace the high-dimensional objective in shape optimization. Specifically, several First-stage Surrogate Models (FSMs) are built for the sectional airfoils, and the second-stage surrogate model is constructed with respect to the outputs of FSMs. Besides, a Multi-start Space Reduction surrogate-based global optimization method is applied to search for the optimum. In order to validate the efficiency of the proposed method, DSSO is first compared with an ordinary One-stage Surrogate-based Optimization strategy by using the same optimization method. Then, the other three popular surrogate-based optimization methods and three heuristic algorithms are utilized to make comparisons. Results indicate that the lift-to-drag ratio of the BWBUG is improved by 9.35% with DSSO, which outperforms the comparison methods. Besides, DSSO reduces more than 50% of the time that other methods used when obtaining the same level of results. Furthermore, some considerations of the proposed strategy are further discussed and some characteristics of DSSO are identified.

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References

  • Alam, K., Ray, T. and Anavatti, S.G., 2014. Design and construction of an autonomous underwater vehicle, Neurocomputing, 142, 16–29.

    Article  Google Scholar 

  • Bachmayer, R., Leonard, N.E., Graver, J., Fiorelli, E., Bhatta, P. and Paley, D., 2004. Underwater gliders: recent developments and future applications, Proceedings of the 2004 International Symposium on Underwater Technology, Taipei, China.

    Google Scholar 

  • Box, G.E.P. and Draper, N.R., 1987. Empirical Model-Building and Response Surfaces, Wiley, New York.

    MATH  Google Scholar 

  • Dong, H.C., Song, B.W., Dong, Z.M. and Wang, P., 2016. Multi-start space reduction (MSSR) surrogate-based global optimization method, Structural and Multidisciplinary Optimization, 54(4), 907–926.

    Article  Google Scholar 

  • Dong, H.C., Song, B.W., Dong, Z.M. and Wang, P., 2018. SCGOSR: Surrogate-based constrained global optimization using space reduction, Applied Soft Computing, 65, 462–477.

    Article  Google Scholar 

  • Dong, H.C., Sun, S.Q., Song, B.W. and Wang, P., 2019. Multi-surrogate-based global optimization using a score-based infill criterion, Structural and Multidisciplinary Optimization, 59(2), 485–506.

    Article  MathSciNet  Google Scholar 

  • Eriksen, C.C., Osse, T.J., Light, R.D., Wen, T., Lehman, T.W., Sabin, P.L., Ballard, J.W. and Chiodi, A.M., 2001. Seaglider: A long-range autonomous underwater vehicle for oceanographic research, IEEE Journal of Oceanic Engineering, 26(4), 424–436.

    Article  Google Scholar 

  • Graver, G.J., 2005. Underwater Gliders: Dynamics, Control and Design, Ph.D. Thesis, Princeton University, Princeton.

    Google Scholar 

  • Gu, H.T., Lin, Y., Hu, Z.Q. and Yu, J.C., 2009. Surrogate models for shape optimization of underwater glider, Proceedings of 2009 International Conference on Computer Modeling and Simulation, Macau, China.

    Google Scholar 

  • Gu, J., Li, G.Y. and Dong, Z., 2012. Hybrid and adaptive meta-model-based global optimization, Engineering Optimization, 44(1), 87–104.

    Article  Google Scholar 

  • Guo, Y.Q., Li, Y.M., Abbès, B., Naceur, H. and Halouani, A., 2013. Damage prediction in metal forming process modeling and optimization: Simplified approaches, in: Handbook of Damage Mechanics, Voyiadjis, G. (ed.), Springer, New York, pp. 1–43.

    Google Scholar 

  • Hildebrand, J.A., D’Spain, G.L., Roch, M.A. and Porter, M.B., 2009. Glider-Based Passive Acoustic Monitoring Techniques in the Southern California Region, Technical Report, Scripps Institution of Oceanography la Jollaca.

    Book  Google Scholar 

  • Kuya, Y., Takeda, K., Zhang, X. and Forrester, A.I.J., 2011. Multifi-delity surrogate modeling of experimental and computational aerodynamic data sets, AIAA Journal, 49(2), 289–298.

    Article  Google Scholar 

  • Leifsson, L., Koziel, S., Tesfahunegn, Y. and Bekasiewicz, A., 2016. Fast multi-objective aerodynamic optimization using space-mapping-corrected multi-fidelity models and kriging interpolation, in: Simulation-Driven Modeling and Optimization. Springer Proceedings in Mathematics & Statistics, Koziel, S., Leifsson, L. and Yang, X.S. (eds.), vol 153, Springer, Cham, pp. 55–73.

    Article  Google Scholar 

  • Ma, Z., Zhang, H., Zhang, N. and Ma, D.M., 2006. Study on energy and hydrodynamic performance of the underwater glider, Journal of Ship Mechanics, 10(3), 53–60. (in Chinese)

    Google Scholar 

  • Müller, J., 2012. User Guide for Modularized Surrogate Model Toolbox, Tampere University of Technology, Tampere.

    Google Scholar 

  • Müller, J., 2016. MISO: Mixed-integer surrogate optimization framework, Optimization and Engineering, 17(1), 177–203.

    Article  MathSciNet  Google Scholar 

  • Mirjalili, S., Mirjalili, S.M. and Lewis, A., 2014. Grey wolf optimizer, Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  • Mullur, A.A. and Messac, A., 2005. Extended radial basis functions: More flexible and effective metamodeling, AIAA Journal, 43(6), 1306–1315.

    Article  Google Scholar 

  • ONR, 2006. Liberdade XRAY Advanced Underwater Glider, U.S. Office of Naval Research.

    Google Scholar 

  • Osse, T.J. and Eriksen, C.C., 2007. The deepglider: A full ocean depth glider for oceanographic research, OCEANS 2007, Vancouver, BC, Canada.

    Google Scholar 

  • Sacher, M., Durand, M., Berrini, É., Hauville, F., Duvigneau, R., Le Maître, O. and Astolfi, J.A., 2018. Flexible hydrofoil optimization for the 35th America’s Cup with constrained EGO method, Ocean Engineering, 157, 62–72.

    Article  Google Scholar 

  • Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P., 1989. Design and analysis of computer experiments, Statistical Science, 4(4), 409–423.

    Article  MathSciNet  Google Scholar 

  • Sherman, J., Davis, R., Owens, W.B. and Valdes, J., 2001. The autonomous underwater glider “Spray”, IEEE Journal of Oceanic Engineering, 26(4), 437–446.

    Article  Google Scholar 

  • Smola, A.J. and Schölkopf, B., 2004. A tutorial on support vector regression, Statistics and Computing, 14(3), 199–222.

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  • Stommel, H., 1989. The slocum mission, Oceanography, 2(1), 22–25.

    Article  Google Scholar 

  • Storn, R. and Price, K., 1997. Differential evolution- A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11(4), 341–359.

    Article  MathSciNet  Google Scholar 

  • Sun, C.Y., Song, B.W. and Wang, P., 2015. Parametric geometric model and shape optimization of an underwater glider with blended-wing-body, International Journal of Naval Architecture and Ocean Engineering, 7(6), 995–1006.

    Article  Google Scholar 

  • Sun, C.Y., Song, B.W., Wang, P. and Wang, X.J., 2017. Shape optimization of blended-wing-body underwater glider by using gliding range as the optimization target, International Journal of Naval Architecture and Ocean Engineering, 9(6), 693–704.

    Article  Google Scholar 

  • Wang, X.J., Song, B.W., Wang, P., Tian, W.L. and Wang, Y.J., 2018. Surrogate-based optimization of location hole for contactless power transmission system, Ocean Engineering, 157, 35–43.

    Article  Google Scholar 

  • Wang, Z.Y., Yu, J.C., Zhang, A.Q., Wang, Y.X. and Zhao, W.T., 2017. Parametric geometric model and hydrodynamic shape optimization of a flying-wing structure underwater glider, China Ocean Engineering, 31(6), 709–715.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Whitley, D., 1994. A genetic algorithm tutorial, Statistics and Computing, 4(2), 65–85.

    Article  Google Scholar 

  • Zhang, M.L., Ren, J.D., Yin, Y. and Du, J., 2016. Coach simplified structure modeling and optimization study based on the PBM method, Chinese Journal of Mechanical Engineering, 29(5), 1010–1018.

    Article  Google Scholar 

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Correspondence to Peng Wang.

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Foundation item: This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 51875466 and 51805436), the China Postdoctoral Science Foundation (Grant No. 2019T120941) and the China Scholarships Council (Grant No. 201806290133).

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Li, Cs., Wang, P., Qiu, Zm. et al. A Double-Stage Surrogate-Based Shape Optimization Strategy for Blended-Wing-Body Underwater Gliders. China Ocean Eng 34, 400–410 (2020). https://doi.org/10.1007/s13344-020-0036-2

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  • DOI: https://doi.org/10.1007/s13344-020-0036-2

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