Coupled aerostructural design optimization using the kriging model and integrated multiobjective optimization algorithm

Journal of Optimization Theory and Applications

Volumn 142, Issue 3, 2009, Pages 533-556

  Lam, X B ^a   Kim, Y S ^a   Hoang, A D ^a   Park, C W ^a  

^a Gyeongsang National University   (South Korea)

Author keywords

Fluid structure interface (FSI); Global optimization; Kriging model; Multiobjective optimization

Indexed keywords

AERODYNAMIC DRAG; AERODYNAMICS; GLOBAL OPTIMIZATION; KRIGING; SHAPE OPTIMIZATION; STRUCTURAL DESIGN;

AERO-DYNAMIC PERFORMANCE; AERO-STRUCTURAL DESIGN; DESIGN OPTIMIZATION; FLUID-STRUCTURE INTERFACES; FLUID/STRUCTURE INTERFACE; GLOBAL OPTIMISATION; KRIGING MODEL; MULTI-OBJECTIVES OPTIMIZATION; OPTIMIZATION ALGORITHMS; PERFORMANCE;

MULTIOBJECTIVE OPTIMIZATION;

EID: 70349586798     PISSN: 00223239     EISSN: 15732878     Source Type: Journal    
DOI: 10.1007/s10957-009-9520-9     Document Type: Article

References (69)

1. Sobieski, J.S., Haftka, R.T.: Multidisciplinary aerospace design optimization: survey of recent developments. AIAA J., AIAA-96-0711 (1996)
2. Wakayama, S.R.: Lifting surface design using multidisciplinary optimization. Ph.D. Thesis, Stanford University (1997)
3. Walsh, J.L., Townsend, J.C., Salas, A.O., Samareh, J.A., Mukhopadhyay, V., Barthelemy, J.-F.: Multidisciplinary high-fidelity analysis and optimization of aerospace vehicles. AIAA J., AIAA-2000-0418 (2000)
4. Martins, R.R.A.: A coupled-adjoint method for high-fidelity aero-structural optimization. Ph.D. Thesis, Stanford University (2002)
5. Venkataraman, S., Haftka, R.T.: Structural optimization complexity: what has Moore's law done for us. J. Struct. Multidiscip. Optim. 28, 375-387 (2004)
6. Kim, Y., Kim, J., Jeon, Y., Bang, J., Lee, D.-H., Kim, Y., Park, C.W.: Multidisciplinary aerodynamic-structural design optimization of supersonic fighter wing using response surface methodology. AIAA J., AIAA-2002-0322 (2002)
7. Giunta, A.A.: Aircraft multidisciplinary design optimization using design of experiments theory and response surface modeling methods. Ph.D. Thesis, University of Virginia (1997)
8. Giunta, A.A., Balabanov, V., Haim, D., Grossman, B., Mason, W.H., Watson, L.T., Haftka, R.T.: Wing design for a high-speed civil transport using a design of experiments methodology. AIAA J., AIAA-96-4001 (1996)
9. Joaquim, R.R., Alonso, J.J., Reuther, J.: Aero-Structural Wing Design Optimization using high-fidelity sensitivity analysis. In: Proceeding to CEAS Conference on Multidisciplinary Aircraft Design Optimization, Germany. Confederation of European Societies (2001)
10. Chittick, I.R., Martins, J.R.R.A.: Aero-structural optimization using adjoint coupled post-optimality sensitivities. J. Struct. Multidiscip. Optim. DOI 10.1007/s00158-007-0200-9 (2007)
11. Gumbert, C.R., Newman, P.A.: High-fidelity computational optimization for 3-D flexible wings. J. Optim. Eng. 6, 117-156 (2005)
12. Kumano, T., Jeong, S., Obayashi, S., Ito, Y., Hatanaka, K., Morino, H.: Multidisciplinary design optimization of wing shape with nacelle and pylon. In: European Conference on Computational Fluid Dynamics ECCOMAS CFD 2006, TU Delft, The Netherlands (2006)
13. Weck, O.D., Agte, J., Sobieski, J.S., Arendsen, P., Morris, A., Spieck, M.: State-of-the-art and future trends in multidisciplinary design optimization. In: 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Hawaii, USA. AIAA-2007-1905 (2007)
14. Martins, J.R.R.A., Marriage, C.: An objective-oriented framework for multidisciplinary design optimization. In: 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Hawaii, USA. AIAA-2007-1906 (2007)
15. Kamakoti, R., Shyy, W.: Fluid-structure interaction for aeroelastic applications. Prog. Aerospace Sci. 40, 535-558 (2005)
16. Guruswamy, G.P.: A review of numerical fluids/structures interface methods for computations using high-fidelity equations. J. Comput. Struct. 80, 31-41 (2001)
17. Hounjet, M.H.L., Meijer, J.J.: Evaluation of elastomechanical and aerodynamic data transfer methods for non-planar configurations in computational aeroelastic analysis. National Aerospace Laboratory NRL, NLR-TP-95690 U (1995)
18. Bhadra, S., Ganguli, R.: Aeroelastic optimization of a helicopter rotor using orthogonal array-based metamodels. AIAA J. 44(9), 1941-1951 (2006)
19. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1996)
20. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New Jersey (1999)
21. Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. Massachusetts (1996)
22. Friedman, J.H.: Multivariate adaptive regression splines, invited paper. Ann. Stat. 19(1), 1-67 (1991)
23. Turner, C.J., Crawford, R.H., Campbell, M.I.: Multidimensional sequential sampling for NURBs-based metamodel development. J. Eng. Comput. 23, 155-174 (2007)
24. Mullur, A.A., Messac, A.: Extended radial basis functions: More flexible and effective metamodeling. AIAA J. 43(6), 1306-1315 (2005)
25. Mullur, A.A., Messac, A.: Metamodeling using extended radial basis functions: A comparative approach. J. Eng. Comput. 21, 203-217 (2006)
26. Koehler, J.R., Owen, A.B.: Computer Experiments. Handbook of Statistics 13: Design and Analysis of Experiments. Elsevier, Amsterdam (1996)
27. Giunta, A.A., Watson, L.T.: A comparison of approximation modeling techniques: Polynomial versus interpolating models. AIAA J., AIAA-98-4758 (1998)
28. Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P.: Design and analysis of computer experiments. J. Stat. Sci. 4(4), 409-423 (1989)
29. Jeong, S., Murayama, M., Yamamoto, K.: Efficient optimization design method using Kriging model. AIAA J., AIAA-2004-118 (2004)
30. Simpson, T.W., Dennis, L., Chen, W.: Sampling strategies for computer experiments: design and analysis. Int. J. Reliab. Appl. 23(2), 209-240 (2001)
31. Simpson, T.W., Booker, A.J., Ghosh, D., Giunta, A.A., Koch, P.N., Yang, R.-J.: Approximation methods in multidisciplinary analysis and optimization: A panel discussion. J. Struct. Multidiscip. Optim. 27, 302-313 (2004)
32. Martin, J.D., Simpson, T.W.: Use of Kriging models to approximate deterministic computer models. AIAA J. 43(4), 853-863 (2005)
33. Clarke, S.M., Griebsch, J.H., Simpson, T.W.: Analysis of support vector regression for approximation of complex engineering analyses. ASME J. 127, 1077-1087 (2005)
34. Maisuradze, G.G., Thompson, D.L.: Interpolating moving least-squares methods for fitting potential energy surfaces: illustrative approaches and applications. J. Phys. Chem. A 107(37), 7118-7124 (2003)
35. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman Inc, Cambridge (1989)
36. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)
37. Yang, G., Reinstein, L.E., Pai, S., Xu, Z.: A new genetic algorithm technique in optimization of permanent prostate implants. J. Med. Phys. 25(12), 2308-2315 (1998)
38. Carroll, D.L.: Chemical laser modeling with genetic algorithms. AIAA J. 34(2), 338-346 (1996)
39. Arora, J.S.: Introduction to optimum design. Elsevier Academic, San Diego (2004)
40. Arora, J.S., Elwakeil, O.A., Chahande, A.I., Hsieh, C.C.: Global optimization methods for engineering applications: A review. J. Struct. Optim. 9, 137-159 (1995)
41. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. J. Sci. 220(4598), 671-680 (1983)
42. Goffe, W.L., Ferrier, G.D., Rogers, J.: Global optimization of statistical functions with simulated annealing. J. Econom. 60(1/2), 65-100 (1993)
43. Corana, A., Marchesi, M., Martini, C., Ridella, S.: Minimizing multimodal functions of continuous variables with the 'Simulated Annealing' algorithm. ACM Trans. Math. Softw. 13(3), 262-280 (1987)
44. Yao, X.: Simulated annealing with extended neighbourhood. Int. J. Comput. Math. 40, 169-189 (1991)
45. Coello Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York (2007)
46. Deb, K.: Current trends in evolutionary multi-objective optimization. Int. J. Simul. Multidiscip. Des. Optim. 1, 1-8 (2007)
47. Marler, R.T.: A study of multi-objective optimization methods for engineering applications. Ph.D. Thesis, University of Iowa (2005)
48. FLUENT INC: Fluent User's Manual. Fluent Inc, New Hampshire (2005)
49. Blom, F.J.: Considerations on the spring analogy. Int. J. Numer. Methods Fluids 32, 647-668 (2000)
50. Tsai, H.M., Wong, A.S.F., Cai, J., Zhu, Y., Liu, F.: Unsteady flow calculations with a parallel multiblock moving mesh algorithm. AIAA J. 39(6), 1021-1029 (2000)
51. Dubuc, L., Cantariti, F., Woodgate, M., Gribben, B., Badcock, K.J., Richards, B.E.: A grid deformation technique for unsteady flow computations. Int. J. Numer. Methods Fluids 32, 285-311 (2000)
52. Spekreijse, S.P., Prananta, B.B., Kok, J.C.: A simple, robust and fast algorithm to compute deformations of multi-block structured grids. National Aerospace Laboratory NLR, NLR-TP-2002-105 (2002)
53. Thompson, J.F., Soni, B.K., Weatherill, N.P.: Handbook of Grid Generation. CRC Press LLC, Boca Raton (1999)
54. Sadeghi, M., Liu, F., Lai, K.L., Tsai, H.M.: Application of three-dimensional interfaces for data transfer in aeroelastic computations. AIAA J., AIAA-2004-5376 (2004)
55. Dowell, E.H., Hall, K.C.: Modeling of fluid-structure interaction. J. Fluid Mech. 33, 445-490 (2001)
56. Hirsch, C.: Numerical Computation of Internal and External Flows. Butterworth-Heinemann, Oxford (2007)
57. Blazek, J.: Computational Fluid Dynamics: Principles and Applications. Elsevier Science Ltd, Oxford (2001)
58. Chung, T.J.: Computational Fluid Dynamics. Cambridge University Press, Cambridge (2002)
59. Ferziger, J.H., Peric, M.: Computational Methods for Fluid Dynamics. Springer, Berlin (2002)
60. Anderson, J.D.: Computational Fluid Dynamics: The Basics with Applications. McGraw-Hill, Columbus (1995)
61. Pointwise: Gridgen User's Manual. Pointwise Inc, Texas, USA (2005)
62. Zienkiewicz, O.C., Taylor, L.R.: The Finite Element Method, 5th edn. Butterworth-Heinemann, Oxford (2000)
63. Bathe, K.-J.: Finite Element Procedures. Prentice-Hall, Englewood Cliffs (1996)
64. Smith, I.M., Griffiths, D.V.: Programming the Finite Element Method. Wiley, Chichester (2004)
65. Reddy, J.N.: An introduction to the Finite Element Method, 3rd edn. McGraw-Hill, New York (2006)
66. Liu, G.R., Quek, S.S.: The Finite Element Method-A Practical Course. Butterworth-Heinemann, Oxford (2003)
67. Ribo, R., Pasenau, M.D.R., Escolano, E., Ronda, J.S.P., Sans, A.C., Gonzalez, L.F.: GiD User's Manual. CIMNE, Barcelona, Spain (2007)
68. Mitchell, T.J., Morris, M.D.: Bayesian design and analysis of computer experiments: Two examples. J. Stat. Sinica 2, 359-379 (1992)
69. The Mathworks: Matlab User'S Manual. The MathWorks Inc, Massachusetts, USA (2007)