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Cruise missile head shape optimisation using an adaptive sampling surrogate model

  • S. Z. Guo (a1), X. M. Zheng (a1), H. S. Ang (a1) and H. M. Cai (a2)

Abstract

High-precision response of the surrogate model is desired in the process of optimisation. An excessive number of sampling points will increase the cost of the calculation. The appropriate number of sampling points cannot only guarantee the accuracy of the surrogate model but also save the calculation cost. The purpose of this research is to demonstrate the eventuality of using an adaptive surrogate model for optimisation problems. The adaptive surrogate model is built on an adaptive sampling approach and an extended radial basis function (ERBF). The adaptive sampling is an approach that new sampling points are placed in the blank area and all the sampling points are uniformly distributed in the design region using Multi-Island GA. The precision of the ERBF surrogate model is checked using standard error measure to determine whether the surrogate model should be updated or not. This adaptive surrogate model is used to optimise a cruise missile head shape. Aerodynamic and stealthy performance of the cruise missile head shape are considered in this research. Different global objective function and different weight factor are used to research the aerodynamic and stealthy performance in this optimisation process. The results show that the drag is reduced with a slender head shape and the radar-cross section (RCS) value is reduced with a short head shape.

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1. Forrester, A.I.J., Sóbester, A. and Keane, A.J. Engineering Design via Surrogate Modelling: A Practical Guide. DBLP, 2008.
2. Deng, S., Percin, M., van-Oudheusden, B.W., Bijl, H., Remes, B. and Xiao, T. Numerical simulation of a flexible x-wing flapping-wing micro air vehicle, AIAA J, 2017, 55, (7), pp 22952306.
3. Braun, M., Kleditzsch, S. and Scharler, R. A method for reduction of computational time of local equilibria for biomass flue gas compositions in CFD, Progress in Computational Fluid Dynamics, an Int J, 2006, 6, (4–5), pp 272277.
4. Braun, U.M. and Riedel, U. Alternative fuels in aviation, Aeronaut J, 2015, 6, (1), pp 8393.
5. Jouhaud, J.C., Sagaut, P. and Montagnac, M. A. Surrogate-model based multidisciplinary shape optimisation method with application to a 2D subsonic airfoil, Computers & Fluids, 2007, 36, (3), pp 520529.
6. Queipo, N.V., Haftka, R.T. and Shyy, W. Surrogate-based analysis and optimization, Progress in Aerospace Sciences, 2005, 41, (1), pp 128.
7. Rosenow, J., Lindner, M. and Fricke, H. Impact of climate costs on airline network and trajectory optimization: A parametric study, Aeronaut J, 2017, 8, (2), pp 371384.
8. Conway, B. A., ed. Spacecraft Trajectory Optimization. Cambridge University Press, 2010.
9. Box, G.E.P. and Wilson, K.B. On the experimental attainment of optimum conditions, Journal of the Royal Statistical Society, 1951, 13, (1), pp 145.
10. Wu, Z., Huang, D. and Wang, W. Optimization for fire performance of ultra-low density fiberboards using response surface methodology, BioResources, 2017, 12, (2), pp 37903800.
11. Sun, Z.G., Xiao, S.D. and Xu, M.H. Optimization of the structure of water axial piston pump and cavitation of plunger cavity based on the Kriging model, J Vibroengineering, 2016, 18, (4), pp 2460-2474.
12. Akhtar, T. and Shoemaker, C.A. Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection, J Global Optimization, 2016, 64, (1), pp 1732.
13. Mullur, A.A. and Messac, A. Extended radial basis functions: More flexible and effective metamodeling, AIAA J, 2005, 43, (6), pp 13061315.
14. Chen, Z., Qiu, H. and Gao, L. A local adaptive sampling method for reliability-based design optimization using Kriging model, Structural & Multidisciplinary Optimization, 2014, 49, (3), pp 401416.
15. Remondo, D., Srinivasan, R. and Nicola, V.F. Adaptive importance sampling for performance evaluation and parameter optimization of communication systems, IEEE Transactions on Communications, 2000, 48, (4), pp 557565.
16. Li, T.M., Wu, Y.T. and Chuang, Y.Y. SURE-based optimization for adaptive sampling and reconstruction, ACM Transactions on Graphics, 2012, 31, (6), pp 19.
17. Vytla, V.V.S., Huang, P. and Penmetsa, R. Multi-objective aerodynamic shape optimization of high speed train nose using adaptive surrogate model, AIAA Applied Aerodynamics Conference, 2010, 15, pp 2534.
18. Golzari, A., Sefat, M.H. and Jamshidi, S. Development of an adaptive surrogate model for production optimization, J Petroleum Science & Engineering, 2015, 133, (6), pp 677688.
19. Jones, D. R., Schonlau, M. and Welch, W. J. Efficient global optimization of expensive black-box functions. J Global Optimization, 1998, 13, (4), pp 455492.
20. Zhang, J.J., Xu, L.W. and Gao, R.Z. Multi-island genetic algorithm opetimization of suspension system, Telkomnika Indonesian J Electrical Engineering, 2012, 10, (7), pp 16851691.
21. Guo, S.Z., Ang, H.S. and Cai, H.M. Construction of an adaptive sampling surrogate model and application in composite material structure optimization, Acta Materiae Compositae Sinica, 2018, doi:10.13801/j.cnki.fhclxb.20170904.003.
22. Peng, F., Wu, Z.Z. and Yi, Z. Influence of sampling point distribution in freeform surfaces fitting with radial based function model, Optics & Precision Engineering, 2016, 24, (7), pp 15641572.

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