Browse > Article
http://dx.doi.org/10.1016/j.ijnaoe.2020.05.002

Performance study of a simplified shape optimization strategy for blended-wing-body underwater gliders  

Li, Chengshan (School of Marine Science and Technology, Northwestern Polytechnical University)
Wang, Peng (School of Marine Science and Technology, Northwestern Polytechnical University)
Li, Tianbo (School of Marine Science and Technology, Northwestern Polytechnical University)
Dong, Huachao (School of Marine Science and Technology, Northwestern Polytechnical University)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.12, no.1, 2020 , pp. 455-467 More about this Journal
Abstract
Shape design optimization for Blended-wing-body Underwater Gliders (BWBUGs) is usually computationally expensive. In our previous work, a simplified shape optimization (SSO) strategy is proposed to alleviate the computational burden, which optimizes some of the Sectional Airfoils (SAs) instead of optimizing the 3-D shape of the BWBUG directly. Test results show that SSO can obtain a good result at a much smaller computational cost when three SAs are adopted. In this paper, the performance of SSO is investigated with a different number of SAs selected from the BWBUG, and the results are compared with that of the Direct Shape Optimization (DSO) strategy. Results indicate that SSO tends to perform better with more SAs or even outperforms the DSO strategy in some cases, and the amount of saved computational cost also increases when more SAs are adopted, which provides some reference significance and enlarges the applicability range of SSO.
Keywords
Shape design optimization; Blended-wing-body underwater glider; Lift-to-drag ratio; Surrogate-based optimization; CFD-based simulation;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Bachmayer, R., Leonard, N.E., Graver, J., Fiorelli, E., 2004. Underwater Gliders: Recent Developments and Future Applications.
2 Box, G.E.P., Draper, N.R., 1987. Empirical Model-Building and Response Surfaces/George E. P. Box. Wiley, Norman R. Draper.
3 Dong, H., Li, C., Song, B., Wang, P., 2018a. Multi-surrogate-based Differential Evolution with multi-start exploration (MDEME) for computationally expensive optimization. Adv. Eng. Software 123, 62-76.   DOI
4 Dong, H., Song, B., Dong, Z., Wang, P., 2018c. SCGOSR: surrogate-based constrained global optimization using space reduction. Appl. Soft Comput. 65, 462-477.   DOI
5 Graver, Grady, J., 2005. Underwater gliders :dynamics, control and design. J. Fluid Eng. 127 (3), 523-528.   DOI
6 Dong, H., Sun, S., Song, B., Wang, P., 2018d. Multi-surrogate-based global optimization using a score-based infill criterion. Struct. Multidiscip. Optim. 59 (2), 485-506.   DOI
7 Elanayar, V.T.S., Shin, Y.C., 1994. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Trans. Neural Network. 5 (4), 594-603.   DOI
8 Eriksen, C.C., Osse, T.J., Light, R.D., Wen, T., Lehman, T.W., Sabin, P.L., Chiodi, A.M., 2001. Seaglider: a long-range autonomous underwater vehicle for oceanographic research. IEEE J. Ocean. Eng. 26 (4), 424-436.   DOI
9 Guo, Y., Li, Y., Abbes, B., Naceur, H., Halouani, A., 2015. Damage Prediction in Metal Forming Process Modeling and Optimization. Springer, New York.
10 Hicks, R.M., Henne, P.A., 1978. Wing design by numerical optimization. J. Aircraft 15(7), 407-412.   DOI
11 Dong, H., Song, B., Wang, P., Dong, Z., 2018b. Hybrid surrogate-based optimization using space reduction (HSOSR) for expensive black-box functions. Appl. Soft Comput. 64, 641-655.   DOI
12 Hildebrand, J.A., Spain, G.L.D., Roch, M.A., Porter, M.B., 2009. Glider-based Passive Acoustic Monitoring Techniques in the Southern California Region.
13 Kulfan, B., Bussoletti, J., 2006. "Fundamental" parameteric geometry representations for aircraft component shapes. In: Paper Presented at the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference: the Modeling and Simulation Frontier for Multidisciplinary Design Optimization.
14 Passos, A.G., Luersen, M.A., 2017. Multiobjective optimization of laminated composite parts with curvilinear fibers using Kriging-based approaches. Struct. Multidiscip. Optim. 57 (3), 1115-1127.   DOI
15 Mullur, A.A., Messac, A., 2005. Extended radial basis functions: more flexible and effective metamodeling. AIAA J. 43 (6), 1306-1315.   DOI
16 ONR, 2006. Liberdade XRAY Advanced Underwater Glider [Online]. U.S. Office of Naval Research. URL. http://www.onr.navy.mil/media/extra/factsheets/advancedunderwaterglider.pdf.
17 Osse, T.J., Eriksen, C.C., 2007. The Deepglider: A Full Ocean Depth Glider for Oceanographic Research. OCEANS 2007. IEEE.
18 Pereira, S., Ferreira, P., Vaz, A.I.F., 2015. A simplified optimization model to shortterm electricity planning. Energy 93, 2126-2135.   DOI
19 Li, C., Wang, P., Dong, H., Wang, X., 2018. A simplified shape optimization strategy for blended-wing-body underwater gliders. Struct. Multidiscip. Optim. 58 (5), 2189-2202.   DOI
20 Li, J., Cai, J., Qu, K., 2019. Surrogate-based aerodynamic shape optimization with the active subspace method. Struct. Multidiscip. Optim. 59 (2), 403-419.   DOI
21 Liu, J., Han, Z., Song, W., 2012. Comparison of infill sampling criteria in Krigingbased aerodynamic optimization. Icas.
22 Romanenko, A., Zatvornitsky, A., Medennikov, I., 2014. Simplified simultaneous perturbation stochastic approximation for the optimization of free decoding parameters. Speech Comput. 8773, 402-409.
23 Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P., 1989. Design and analysis of computer experiments. Stat. Sci. 4 (4), 409-423.   DOI
24 Sun, C.Y., Song, B.W., Wang, P., Wang, X.J., 2017. Shape optimization of blendedwing-body underwater glider by using gliding range as the optimization target. Int. J. Nav. Archit. Ocean Eng. 9 (6), 693-704.   DOI
25 Sherman, J., Davis, R., Owens, W.B., Valdes, J., 2001. The autonomous underwater glider "Spray". Ocean. Eng. IEEE J 26 (4), 437-446.   DOI
26 Sobieczky, H., 1980. Computational methods for the design of adaptive airfoils and wings. In: Paper Presented at the Proceedings of the Third GAMM d Conference on Numerical Methods in Fluid Mechanics.
27 Sobieczky, H., 1999. Parametric Airfoils and Wings. Recent Development of Aerodynamic Design.
28 Stommel, H., 1989. The Slocum mission. Oceanography 2 (1), 22-25.   DOI
29 Sun, C.Y., Song, B.W., Wang, P., 2015. Parametric geometric model and shape optimization of an underwater glider with blended-wing-body. Int. J. Nav. Archit. Ocean Eng. 7 (6), 995-1006.   DOI
30 Smola, A.J., Scholkopf, B., 2004. A tutorial on support vector regression. Stat. Comput. 14 (3), 199-222.   DOI
31 Wang, Q., Moin, P., Iaccarino, G., 2010. A high order multivariate approximation scheme for scattered data sets. J. Comput. Phys. 229 (18), 6343-6361.   DOI
32 Webb, D.C., Simonetti, P.J., Jones, C.P., 2001. SLOCUM: an underwater glider propelled by environmental energy. IEEE J. Ocean. Eng. 26 (4), 447-452.   DOI
33 Wu, X., Peng, X., Chen, W., Zhang, W., 2019. A developed surrogate-based optimization framework combining HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems. Struct. Multidiscip. Optim. 1-18.
34 Zhang, M.L., Ren, J.D., Yin, Y., Du, J., 2016. Coach simplified structure modeling and optimization study based on the PBM method. Chin. J. Mech. Eng. 29 (5), 1010-1018.   DOI
35 Xiu, D., 2010. Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton University Press.