International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Multi-objective optimization design for the multi-bubble pressure cabin in BWB underwater glider

  • He, Yanru (School of Marine Science and Technology, Northwestern Polytechnical University) ;
  • Song, Baowei (School of Marine Science and Technology, Northwestern Polytechnical University) ;
  • Dong, Huachao (School of Marine Science and Technology, Northwestern Polytechnical University)
  • Received : 2016.12.08
  • Accepted : 2017.08.27
  • Published : 2018.07.31
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Abstract

In this paper, multi-objective optimization of a multi-bubble pressure cabin in the underwater glider with Blended-Wing-Body (BWB) is carried out using Kriging and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Two objective functions are considered: buoyancy-weight ratio and internal volume. Multi-bubble pressure cabin has a strong compressive capacity, and makes full use of the fuselage space. Parametric modeling of the multi-bubble pressure cabin structure is automatic generated using UG secondary development. Finite Element Analysis (FEA) is employed to study the structural performance using the commercial software ANSYS. The weight of the primary structure is determined from the volume of the Finite Element Structure (FES). The stress limit is taken into account as the constraint condition. Finally, Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) method is used to find some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. The best solution is compared with the initial design results to prove the efficiency and applicability of this optimization method.

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References

  1. Annaratone, D., 2007. Pressure Vessel Design. Springer.
  2. Chang, F.J., Chen, L., Chang, L.C., 2005. Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol. Process. 19, 2277-2289. https://doi.org/10.1002/hyp.5674
  3. Chen, G., Han, X., Liu, G., Jiang, C., Zhao, Z., 2012. An efficient multiobjective optimization method for black-box functions using sequential approximate technique. Appl. Soft Comput. 12, 14-27. https://doi.org/10.1016/j.asoc.2011.09.011
  4. Chen, S., Shi, T., Wang, D., Chen, J., 2015. Multi-objective optimization of the vehicle ride comfort based on Kriging approximate model and NSGA-II. J. Mech. Sci. Technol. 29, 1007-1018. https://doi.org/10.1007/s12206-015-0215-x
  5. Coello, C.C., 2006. Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. Mag. 1, 28-36. https://doi.org/10.1109/MCI.2006.329691
  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182-197. https://doi.org/10.1109/4235.996017
  7. Fallah-Mehdipour, E., Haddad, O.B., Mari, O.M., 2011. MOPSO algorithm and its application in multipurpose multireservoir operations. J. Hydroinformatics 13, 794-811. https://doi.org/10.2166/hydro.2010.105
  8. Forrester, A.I.J., Keane, A.J., 2009. Recent advances in surrogate-based optimization. Prog. Aerosp. Sci. 45, 50-79. https://doi.org/10.1016/j.paerosci.2008.11.001
  9. Friedman, J.H., 1991. Multivariate adaptive regression splines. Ann. Statistics 1-67.
  10. Geuskens, F., Bergsma, O.K., Koussios, S., Beukers, A., 2011. Analysis of conformable pressure vessels: introducing the multibubble. Aiaa J. 49, 1683-1692. https://doi.org/10.2514/1.J050822
  11. Geuskens, F., Koussios, S., Bergsma, O.K., Beukers, A., 2008. Non-cylindrical Pressure Fuselages for Future Aircraft.
  12. Gu, X., Sun, G., Li, G., Huang, X., Li, Y., Li, Q., 2013. Multiobjective optimization design for vehicle occupant restraint system under frontal impact. Struct. Multidiscip. Optim. 47, 465-477. https://doi.org/10.1007/s00158-012-0811-7
  13. Haddad, O.B., Afshar, A., Marino, M.A., 2006. Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour. Manag. 20, 661-680. https://doi.org/10.1007/s11269-005-9001-3
  14. Hildebrand, J.A., Spain, G.L.D., Roch, M.A., Porter, M.B., 2010. Glider-based Passive Acoustic Monitoring Techniques in the Southern California Region.
  15. Iman, R.L., 2008. Latin hypercube sampling. Encycl. Quantitative Risk Analysis Assess. 408-411.
  16. Jalali, M., Afshar, A., Marino, M., 2007. Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem. Water Resour. Manag. 21, 1429-1447. https://doi.org/10.1007/s11269-006-9092-5
  17. Javaid, M.Y., Ovinis, M., Nagarajan, T., Hashim, F.B., 2014. Underwater Gliders: a Review. MATEC Web of Conferences. EDP Sciences, 02020.
  18. Jenkins, S.A., Humphreys, D.E., Sherman, J., Osse, J., Jones, C., Leonard, N., Graver, J., Bachmayer, R., Clem, T., Carroll, P., 2012. Underwater Glider System Study. Scripps Institution of Oceanography.
  19. Jie, H., Wu, Y., Zhao, J., Ding, J., Liangliang, 2016. An efficient multiobjective PSO algorithm assisted by Kriging metamodel for expensive black-box problems. J. Glob. Optim. 1-25.
  20. Jin, R., Chen, W., Sudjianto, A., 2005. An efficient algorithm for constructing optimal design of computer experiments. J. Stat. Plan. Inference 134, 268-287. https://doi.org/10.1016/j.jspi.2004.02.014
  21. Li, M., Li, G., Azarm, S., 2008. A kriging metamodel assisted multi-objective genetic algorithm for design optimization. J. Mech. Des. 130, 031401. https://doi.org/10.1115/1.2829879
  22. Liao, X., Li, Q., Yang, X., Li, W., Zhang, W., 2008. A two-stage multiobjective optimisation of vehicle crashworthiness under frontal impact. Int. J. Crashworthiness 13, 279-288. https://doi.org/10.1080/13588260801933659
  23. Liebeck, R., 2002. Design of the Blended-Wing-body Subsonic Transport.
  24. Mirjalili, S., 2015. The ant lion optimizer. Adv. Eng. Softw. 83, 80-98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  25. Mukhopadhyay, V., 1996. Structural Concepts Study of Non-circular Fuselage Configurations.
  26. Mukhopadhyay, V., Sobieszczanskisobieski, J., Kosaka, I., Quinn, G., Vanderpaats, G.N., 2012. Analysis, design, and optimization of noncylindrical fuselage for blended-wing-body vehicle. J. Aircr. 41, 925-930.
  27. Mukhopadhyay, V., Welstead, J., Quinlan, J., Guynn, M.D., 2016. Structural Configuration Systems Analysis for Advanced Aircraft Fuselage Concepts.
  28. Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M., 2016. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons.
  29. Opricovic, S., Tzeng, G.-H., 2004. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Operational Res. 156, 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1
  30. Queipo,N.V.,Haftka,R.T.,Wei, S.,Goel,T.,Vaidyanathan, R., Tucker, P.K., 2005. Surrogate-based analysis and optimization. Prog. Aerosp. Sci. 41, 1-28. https://doi.org/10.1016/j.paerosci.2005.02.001
  31. Rajagopal, S., Ganguli, R., 2008. Conceptual design of UAV using Kriging based multi-objective genetic algorithm. Aeronautical J. 112, 653-662. https://doi.org/10.1017/S0001924000002621
  32. Saijal, K., Ganguli, R., Viswamurthy, S., 2011. Optimization of helicopter rotor using polynomial and neural network metamodels. J. Aircr. 48, 553-566. https://doi.org/10.2514/1.C031156
  33. Sun, G., Li, G., Gong, Z., He, G., Li, Q., 2011. Radial basis functional model for multi-objective sheet metal forming optimization. Eng. Optim. 43, 1351-1366. https://doi.org/10.1080/0305215X.2011.557072
  34. Viswamurthy, S., Ganguli, R., 2007. Optimal placement of trailing-edge flaps for helicopter vibration reduction using response surface methods. Eng. Optim. 39, 185-202. https://doi.org/10.1080/03052150601047123
  35. Vos, R., Geuskens, F., Hoogreef, M.F.M., 2012. A new structural design concept for blended wing body cabins.
  36. Wang, H., Zhu, X., Du, Z., 2010. Aerodynamic optimization for low pressure turbine exhaust hood using Kriging surrogate model. Int. Commun. Heat Mass Transf. 37, 998-1003. https://doi.org/10.1016/j.icheatmasstransfer.2010.06.022
  37. Yang, H., Chan, L., King, I., 2002. Support vector machine regression for volatile stock market prediction. In: International Conference on Intelligent Data Engineering and Automated Learning. Springer, pp. 391-396.
  38. Yang, R., Wang, N., Tho, C., Bobineau, J., Wang, B., 2005. Metamodeling development for vehicle frontal impact simulation. J. Mech. Des. 127, 1014-1020. https://doi.org/10.1115/1.1906264

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