Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models - Publikacja - MOST Wiedzy

Wyszukiwarka

Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models

Abstrakt

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

Cytowania

  • 1 9

    CrossRef

  • 0

    Web of Science

  • 2 2

    Scopus

Cytuj jako

Pełna treść

pobierz publikację
pobrano 80 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2020 the American Institute of Aeronautics and Astronautics, Inc)

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
JOURNAL OF AIRCRAFT nr 57, strony 1 - 11,
ISSN: 0021-8669
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Amrit A., Leifsson L., Kozieł S.: Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models// JOURNAL OF AIRCRAFT -Vol. 57,iss. 3 (2020), s.1-11
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.2514/1.c035500
Bibliografia: test
  1. Slotnick, J., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E., and Mavriplis, D., "CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences," NASA CR-2014-218178, 2014. otwiera się w nowej karcie
  2. Koziel, S., and Leifsson, L., "Surrogate-Based Aerodynamic Shape Optimization by Variable-Resolution Models," AIAA Journal, Vol. 51, No. 1, 2013, pp. 94-106. https://doi.org/10.2514/1.J051583 otwiera się w nowej karcie
  3. Bandler, J. W., Cheng, Q. S., Dakroury, S., Mohamed, A. S., Bakr, M. H., Madsen, K., and Sondergaard, J., "Space Mapping: The State of the Art," IEEE Transactions of Microwave Theory and Techniques, Vol. 52, No. 1, 2004, pp. 337-361. https://doi.org/10.1109/TMTT.2003.820904 otwiera się w nowej karcie
  4. Koziel, S., and Bekasiewicz, A., "Multi-Objective Design Optimization of Antenna Structures Using Sequential Domain Patching with Auto- mated Patch Size Determination," Engineering Optimization, Vol. 50, No. 2, 2018, pp. 218-234. https://doi.org/10.1080/0305215X.2017.1311879 otwiera się w nowej karcie
  5. Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and Tucker, P. K., "Surrogate-Based Analysis and Optimization," Progress in Aerospace Sciences, Vol. 41, No. 1, 2005, pp. 1-28. https://doi.org/10.1016/j.paerosci.2005.02.001 otwiera się w nowej karcie
  6. Haftka, R. T., "Combining Global and Local Approximations," AIAA Journal, Vol. 29, No. 9, 1991, pp. 1523-1525. https://doi.org/10.2514/3.10768 otwiera się w nowej karcie
  7. Rao, S. S., Engineering Optimization: Theory and Practice, 3rd ed., Wiley, New York, 1996.
  8. Amrit, A., Leifsson, L., and Koziel, S., "Design Strategies for Multi- Objective Optimization of Aerodynamic Surfaces," Engineering Com- putations, Vol. 34, No. 5, 2017, pp. 1724-1753. https://doi.org/10.1108/EC-07-2016-0239 otwiera się w nowej karcie
  9. Obayashi, S., and Sasaki, D., "Finding Tradeoffs by Using Multiobjective Optimization Algorithms," Transactions of JSASS, Vol. 47, No. 155, 2004, pp. 51-58. otwiera się w nowej karcie
  10. Buckley, H. P., Zhou, B. Y., and Zing, D. W., "Airfoil Optimization Using Practical Aerodynamic Design Requirements," Journal of Aircraft, Vol. 47, No. 5, 2010, pp. 1707-1719. https://doi.org/10.2514/1.C000256 otwiera się w nowej karcie
  11. Hwang, C. L., and Masud, A. S. M., Multiple Objective Decision Making, Methods and Applications: A State-of-the-Art Survey, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, Berlin, 1979. otwiera się w nowej karcie
  12. Mengistu, T., and Ghaly, W., "Aerodynamic Optimization of Turbomachi- nery Blades Using Evolutionary Methods and ANN-Based Surrogate Models," Optimization and Engineering, Vol. 9, No. 3, 2008, pp. 239-255. https://doi.org/10.1007/s11081-007-9031-1 otwiera się w nowej karcie
  13. Liem, R. P., Martins, J. R. R. A., and Kenway, G. K., "Expected Drag Minimization for Aerodynamic Design Optimization Based on Aircraft Operational Data," Aerospace Science and Technology, Vol. 63, April 2017, pp. 344-362. https://doi.org/10.1016/j.ast.2017.01.006 otwiera się w nowej karcie
  14. Zhao, K., Gao, Z., Huang, J., and Li, Q., "Aerodynamic Optimization of Rotor Airfoil Based on Multi-Layer Hierarchical Constraint Method," Chinese Journal of Aeronautics, Vol. 29, No. 6, 2016, pp. 1541-1552. https://doi.org/10.1016/j.cja.2016.09.005 otwiera się w nowej karcie
  15. Yang, Z., Cai, X., and Fan, Z., "Epsilon Constrained Method for Constrained Multiobjective Optimization Problems: Some Preliminary Results," Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO Comp '14), Assoc. for Computing Machinery, New York, 2014, pp. 1181-1186. otwiera się w nowej karcie
  16. Eiben, A. E., and Smith, J., "From Evolutionary Computation to the Evolution of Things," Nature, Vol. 521, No. 7553, 2015, pp. 476-482. https://doi.org/10.1038/nature14544 otwiera się w nowej karcie
  17. Poli, R., Kennedy, J., and Blackwell, T., "Particle Swarm Optimization," Swarm Intelligence, Vol. 1, No. 1, 2007, pp. 33-57. https://doi.org/10.1007/s11721-007-0002-0 otwiera się w nowej karcie
  18. Storn, R., and Price, K., "Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces," Journal of Global Optimization, Vol. 11, No. 4, 1997, pp. 341-359. https://doi.org/10.1023/A:1008202821328 otwiera się w nowej karcie
  19. Yang, X. S., "Firefly Algorithms for Multimodal Optimization," Stochastic Algorithms: Foundations and Applications, SAGA 2009, Vol. 5792, Lecture Notes in Computer Sciences, Spring-Verlag, Berlin, Germany, 2009, pp. 169-178. otwiera się w nowej karcie
  20. Yang, X. S., and Deb, S., "Cuckoo Search: Recent Advances and Applications," Neural Computing and Applications, Vol. 24, No. 1, 2014, pp. 169-174. https://doi.org/10.1007/s00521-013-1367-1 otwiera się w nowej karcie
  21. Forrester, A. I. J., and Keane, A. J., "Recent Advances in Surrogate- Based Optimization," Progress in Aerospace Sciences, Vol. 45, Nos. 1-3, 2009, pp. 50-79. https://doi.org/10.1016/j.paerosci.2008.11.001 otwiera się w nowej karcie
  22. Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P., "Design and Analysis of Computer Experiments," Statistical Science, Vol. 4, No. 4, 1989, pp. 409-423. https://doi.org/10.1214/ss/1177012413 otwiera się w nowej karcie
  23. Yondo, R., Andres, E., and Valero, E., "A Review on Design of Experi- ments and Surrogate Models in Aircraft Real-Time and Many-Query Aerodynamic Analyses," Progress in Aerospace Sciences, Vol. 96, Jan. 2018, pp. 23-61. https://doi.org/10.1016/j.paerosci.2017.11.003 otwiera się w nowej karcie
  24. Knowles, J., "ParEGO: A Hybrid Algorithm with On-Line Landscape Approximation for Expensive Multiobjective Optimization Problems," IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, 2006, pp. 50-66. https://doi.org/10.1109/TEVC.2005.851274 otwiera się w nowej karcie
  25. Shan, S., and Wang, G., "An Efficient Pareto Set Identification Approach for Multiobjective Optimization on Black-Box Functions," Journal of Mechanical Design, Vol. 127, No. 5, 2005, pp. 866-874. https://doi.org/10.1115/1.1904639 otwiera się w nowej karcie
  26. Forrester, A., Sobester, A., and Keane, A., Engineering Design via Surrogate Modelling: A Practical Guide, Wiley, New York, 2008.
  27. Praveen, C., and Duvigneau, R., "Low Cost PSO Using Metamodels and Inexact Pre-Evaluation: Application to Aerodynamic Shape Design," Computer Methods in Applied Mechanics and Engineering, Vol. 198, Nos. 9-12, 2009, pp. 1087-1096. https://doi.org/10.1016/j.cma.2008.11.019 otwiera się w nowej karcie
  28. Karakasis, M. K., and Giannakoglou, K. C., "On the Use of Metamodel- Assisted, Multi-Objective Evolutionary Algorithms," Engineering Optimization, Vol. 38, No. 8, 2006, pp. 941-957. https://doi.org/10.1080/03052150600848000 otwiera się w nowej karcie
  29. Li, M., Li, G., and Azarm, S., "A Kriging Metamodel Assisted Multi- Objective Genetic Algorithm for Design Optimization," Journal of Mechanical Design, Vol. 130, No. 3, 2008, Paper 031401. https://doi.org/10.1115/1.2829879 otwiera się w nowej karcie
  30. Hu, W., Li, M., Azarm, S., and Almansoori, A., "Multi-Objective Robust Optimization Under Interval Uncertainty Using Online Approximation and Constraint Cuts," Journal of Mechanical Design, Vol. 133, No. 6, 2011, Paper 061002. https://doi.org/10.1115/1.4003918 otwiera się w nowej karcie
  31. Hu, W. W., Saleh, K. H., and Azarm, S. S., "Approximation Assisted Multiobjective Optimization with Combined Global and Local Meta- modeling," International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 3: 38th Design Automation Conference, Parts A and B, ASME, New York, 2012, pp. 753-764. otwiera się w nowej karcie
  32. Zhang, L., Zhang, J., Li, T., and Zhang, Y., "Multiobjective Aerody- namic Optimization Design of High-Speed Train Head Shape," Journal of Zhejiang University-Science A, Vol. 18, No. 11, 2017, pp. 841-854. https://doi.org/10.1631/jzus.A1600764 otwiera się w nowej karcie
  33. Wang, W., Mo, R., and Zhang, Y., "Multi-Objective Aerodynamic Optimization Design Method of Compressor Rotor Based on Isight," Procedia Engineering, Vol. 15, Dec. 2011, pp. 3699-3703. https://doi.org/10.1016/j.proeng.2011.08.693 otwiera się w nowej karcie
  34. Leusink, D., Alfano, D., and Cinnella, P., "Multi-Fidelity Optimization Strategy for the Industrial Aerodynamic Design of Helicopter Rotor Blades," Aerospace Science and Technology, Vol. 42, April 2015, pp. 136-147. https://doi.org/10.1016/j.ast.2015.01.005 otwiera się w nowej karcie
  35. Leifsson, L., Koziel, S., and Tesfahunegn, A. Y., "Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates," AIAA Journal, Vol. 54, No. 2, 2016, pp. 531-541. https://doi.org/10.2514/1.J054128 otwiera się w nowej karcie
  36. Koziel, S., Tesfahunegn, A. Y., and Leifsson, L., "Expedited Constrained Multi-Objective Aerodynamic Shape Optimization by means of Physics- Based Surrogates," Applied Mathematical Modelling, Vol. 40, Nos. 15- 16, 2016, pp. 7204-7215. https://doi.org/10.1016/j.apm.2016.03.020 otwiera się w nowej karcie
  37. Fincham, J. H. S., and Friswell, M. I., "Aerodynamic Optimization of a Camber Morphing Aerofoil," Aerospace Science and Technology, Vol. 43, June 2015, pp. 245-255. https://doi.org/10.1016/j.ast.2015.02.023 otwiera się w nowej karcie
  38. Peherstorfer, B., Willcox, K., and Gunzburger, M., "Survey of Multi- fidelity Methods in Uncertainty Propagation, Inference, and Optimiza- tion," SIAM Review, Vol. 60, No. 3, 2018, pp. 550-591. otwiera się w nowej karcie
  39. Han, Z. H., Gortz, S., and Zimmermann, R., "Improving Variable- Fidelity Surrogate Modeling via Gradient-Enhanced Kriging and a Generalized Hybrid Bridge Function," Aerospace Science and Technol- ogy, Vol. 25, No. 1, 2013, pp. 177-189. https://doi.org/10.1016/j.ast.2012.01.006 otwiera się w nowej karcie
  40. Rathinam, M., and Petzold, L., "A New Look at Proper Orthogonal Decomposition," SIAM Journal on Numerical Analysis, Vol. 41, No. 5, 2003, pp. 1893-1925. https://doi.org/10.1137/S0036142901389049 otwiera się w nowej karcie
  41. Rozza, G., Huynh, D. B. P., and Patera, A. T., "Reduced Basis Approxi- mation and a Posteriori Error Estimation for Affinely Parametrized Elliptic Coercive Partial Differential Equations," Archives of Computa- tional Methods in Engineering, Vol. 15, No. 3, 2008, pp. 229-275. https://doi.org/10.1007/s11831-008-9019-9 otwiera się w nowej karcie
  42. Scholkopf, B., and Smola, A. J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cam- bridge, MA, 2001. otwiera się w nowej karcie
  43. March, A., and Willcox, K., "Constrained Multifidelity Optimization Using Model Calibration," Structural and Multidisciplinary Optimiza- tion, Vol. 46, No. 1, 2012, pp. 93-109. https://doi.org/10.1007/s00158-011-0749-1 otwiera się w nowej karcie
  44. Keane, A. J., "Cokriging for Robust Design Optimization," AIAA Jour- nal, Vol. 50, No. 11, 2012, pp. 2351-2364. https://doi.org/10.2514/1.J051391 otwiera się w nowej karcie
  45. Laurenceau, J., and Sagaut, P., "Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging," AIAA Journal, Vol. 46, No. 2, 2008, pp. 498-507. https://doi.org/10.2514/1.32308 otwiera się w nowej karcie
  46. Qian, P. Z., and Wu, C. J., "Bayesian Hierarchical Modeling for Inte- grating Low-Accuracy and High-Accuracy Experiments," Technomet- rics, Vol. 50, No. 2, 2008, pp. 192-204. https://doi.org/10.1198/004017008000000082 otwiera się w nowej karcie
  47. Teckentrup, A. L., Jantsch, P., Webster, C. G., and Gunzburger, M., "A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data," SIAM/ASA Journal on Uncertainty Quantification, Vol. 3, No. 1, 2015, pp. 1046-1074. https://doi.org/10.1137/140969002 otwiera się w nowej karcie
  48. Fonseca, C., "Multiobjective Genetic Algorithms with Applications to Control Engineering Problems," Ph.D. Thesis, Det. of Automatic Control and Systems Engineering, Univ. of Sheffield, Sheffield, England, U.K., 1995. otwiera się w nowej karcie
  49. Ren, Z., Thelen, A. S., Amrit, A., Du, X., Leifsson, L., Tesfahunegn, Y. A., and Koziel, S., "Application of Multifidelity Optimization Tech- niques to Benchmark Aerodynamic Design Problems," 54th AIAA Aerospace Sciences Meeting, AIAA Paper 2016-1542, 2016. otwiera się w nowej karcie
  50. Booker, A. J., Dennis, J. E., Jr., Frank, P. D., Serafini, D. B., Torczon, V., and Trosset, M. W., "A Rigorous Framework for Optimization of Expensive Functions by Surrogates," Structural Optimization, Vol. 17, No. 1, 1999, pp. 1-13. https://doi.org/10.1007/BF01197708 otwiera się w nowej karcie
  51. Fonzeca, C. M., and Fleming, P. J., "An Overview of Evolutionary Algorithms in Multiobjective Optimization," Evolutionary Computa- tion, Vol. 3, No. 1, 1995, pp. 1-16. https://doi.org/10.1162/evco.1995.3.1.1 otwiera się w nowej karcie
  52. Economon, T. D., Palacios, F., Copeland, S. R., Lukaczyk, T. W., and Alonso, J. J., "SU2: An Open-Source Suite for Multiphysics Simulation and Design," AIAA Journal, Vol. 54, No. 3, 2016, pp. 828-846. https://doi.org/10.2514/1.J053813 otwiera się w nowej karcie
  53. Spalart, P. R., and Allmaras, S. R., "A One Equation Turbulence Model for Aerodynamic Flows," 38th AIAA Aerospace Sciences Meeting and Exhibit, AIAA Paper 1992-0439, Jan. 1992. otwiera się w nowej karcie
  54. Jameson, A., Schmidt, W., and Turkel, E., "Numerical Solution of the Euler Equations by Finite Volume Methods Using Runge-Kutta Time- Stepping Schemes," AIAA 14th Fluid and Plasma Dynamic Conference, AIAA Paper 1981-1259, June 1981. otwiera się w nowej karcie
  55. Kinsey, D. W., and Barth, T. J., "Description of a Hyperbolic Grid Generation Procedure for Arbitrary Two-Dimensional Bodies," AFWAL TM 84-191-FIMM, Wright-Patterson Air Force Base Aero- nautical Lab., 1984. otwiera się w nowej karcie
  56. Koziel, S., Echeverría-Ciaurri, D., and Leifsson, L., "Surrogate-Based Methods," Computational Optimization, Methods and Algorithms, Series: Studies in Computational Intelligence, edited by S. Koziel, and X. S. Yang, Springer-Verlag, Berlin, Germany, 2011, pp. 33-60. otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 66 razy

Publikacje, które mogą cię zainteresować

Meta Tagi