Surrogate Model–Based Robust Multidisciplinary Design Optimization of an Unmanned Aerial Vehicle
Publication: Journal of Aerospace Engineering
Volume 34, Issue 4
Abstract
Despite the many advantages of the design optimization technique, this method is costly for real engineering problems. This cost will increase sharply for issues with a multidisciplinary and uncertain nature and more than one objective function. In this paper, the metamodel concept has been used to overcome this problem. Because of the ability of neural networks to approximate the behavior of complex engineering systems, this tool has been used to create a surrogate model. Because multidisciplinary design optimization and robust design optimization methods have been used in this study and according to the high cost of the multidisciplinary analysis module, a surrogate model of this module has been made to reduce the imposed costs. To show the capability of the considered approach, robust multidisciplinary design optimization of an unmanned aerial vehicle (UAV) has been done. Take-off weight and cruise drag are the considered objective functions in this study, and the nondominated sorting genetic algorithm (NSGA-I) has been used for minimization of them. The optimization results show that the use of the metamodeling concept has reduced computational costs by 94.1%.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
No data, models, or code were generated or used during the study.
References
Abbas, H. A., R. Sarkar, and C. Newton. 2001. “PDE: A pareto-frontier evolution approach for multi-objective optimization problem.” Proc. Evolutionary Computation Conf., 971–978. New York: IEEE.
Anderson, J. D. 1999. Aircraft performance and design. New York: McGraw-Hill.
Ariyarit, A., M. Sugiura, Y. Tanabe, and M. Kanazaki. 2018. “Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design.” Eng. Optim. 50 (6): 1016–1040. https://doi.org/10.1080/0305215X.2017.1367391.
Azizi, M. A., S. M. B. Malaek, M. Ashrafizadeh, and S. M. Taheri. 2013. “Aircraft design cycle time reduction using artificial intelligence.” Aerosp. Sci. Technol. 26 (1): 244–258. https://doi.org/10.1016/j.ast.2012.05.003.
Babaei, A. R., S. M. R. Setayandeh, and H. Farrokhfal. 2018. “Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function.” Chin. J. Aeronaut. 31 (12): 2248–2259. https://doi.org/10.1016/j.cja.2018.04.018.
Baklacioglu, T. 2016. “Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks.” Aerosp. Sci. Technol. 49 (Feb): 52–62. https://doi.org/10.1016/j.ast.2015.11.031.
Balesdent, M., N. Berend, P. Depince, and A. Chriette. 2012. “A survey of multidisciplinary design optimization methods in launch vehicle design.” Struct. Multidiscip. Optim. 45 (5): 619–642. https://doi.org/10.1007/s00158-011-0701-4.
Brevault, L., M. Balesdent, N. Berend, and R. L. Riche. 2013. “Challenges and future trends in uncertainty-based multidisciplinary design optimization for space transportation system design.” In Proc., 5st European conf. for aerospace sciences. Sint-Genesius-Rode, Belgium: EUCASS.
Carlos, C. C. A. 2006. “Evolutionary multi-objective optimization: A historical view of the field.” IEEE Comput. Intell. Mag. 1 (1): 28–36.
Cavus, N. 2009. “Multidisciplinary and multi-objective design optimization of an unmanned combat aerial vehicle.” M.Sc. thesis, Dept. of Natural and Applied Sciences, Middle East Technical Univ.
Chakraborty, S., T. Chatterjee, R. Chowdhury, and S. Adhikari. 2017. “A surrogate based multi-fidelity approach for robust design optimization.” Appl. Math. Modell. 47 (Jul): 726–744. https://doi.org/10.1016/j.apm.2017.03.040.
Chatterjee, T., S. Chakraborty, and R. Chowdhury. 2019. “A critical review of surrogate assisted robust design optimization.” Arch. Comput. Methods Eng. 26 (1): 245–274. https://doi.org/10.1007/s11831-017-9240-5.
Daskilewicz, M. J., B. J. German, T. T. Takahashi, S. Donovan, and A. Shajanian. 2011. “Effects of disciplinary uncertainty on multi-objective optimization in aircraft conceptual design.” Struct. Multidiscip. Optim. 44 (6): 831–846. https://doi.org/10.1007/s00158-011-0673-4.
Du, S., and L. Wang. 2016. “Aircraft design optimization with uncertainty based on fuzzy clustering analysis.” J. Aerosp. Eng. 29 (1): 2–9. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000517.
Goel, T., R. Vaidyanathan, R. T. Haftka, W. Shyy, N. V. Queipo, and K. Tucker. 2007. “Response surface approximation to pareto optimal front in multi-objective optimization.” Comput. Methods Appl. Mech. Eng. 196 (4–6): 879–893. https://doi.org/10.1016/j.cma.2006.07.010.
Gonzalez, L. F., J. Periaux, K. Srinivas, and E. J. Whitney. 2006. “A generic framework for the design optimization of multidisciplinary UAV intelligent systems using evolutionary computing.” In Proc., 44st AIAA aerospace sciences meeting and exhibit. Reston, VA: American Institute of Aeronautics and Astronautics.
Hagan, M. T., H. B. Demuth, and M. H. Beale. 1996. Neural network design. Boston: PWS Publishing.
Haykin, S. 1999. Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice hall.
Hosseini, M., M. Nosratollahi, and H. Sadati. 2016. “Evaluation of multidisciplinary design optimization methods and applying single level framework in unmanned aerial vehicle design.” Indian J. Sci. Technol. 9 (1): 1–7. https://doi.org/10.17485/ijst/2016/v9i47/101514.
Hosseini, M., M. Nosratollahi, and H. Sadati. 2017. “Multidisciplinary design optimization of UAV under uncertainty.” J. Aerosp. Technol. Manage. 9 (2): 169–178. https://doi.org/10.5028/jatm.v9i2.725.
Im, J., and J. Park. 2013. “Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics.” Chin. J. Aeronaut. 26 (1): 112–121. https://doi.org/10.1016/j.cja.2012.12.022.
Jaeger, L., C. Gogu, S. Segonds, and C. Bes. 2013. “Aircraft multidisciplinary design optimization under both model and design variables uncertainty.” J. Aircr. 50 (2): 528–538. https://doi.org/10.2514/1.C031914.
Jun, S., K. Yee, J. Lee, and D. Lee. 2011. “Robust design optimization of unmanned aerial vehicle coaxial rotor considering operational uncertainty.” J. Aircr. 48 (2): 353–367. https://doi.org/10.2514/1.C001016.
Kartalopoulos, S. V. 1996. Understanding neural networks and fuzzy logic basic concepts and applications. New York: IEEE.
Leifsson, L., S. Koziel, and Y. A. Tesfahungen. 2016. “Multiobjective aerodynamic optimization by variable fidelity models and response surface surrogates.” AIAA J. 54 (2): 531–541. https://doi.org/10.2514/1.J054128.
Lin, C., F. Gao, and Y. Bai. 2018. “An intelligent sampling approach for meta model-based multi-objective optimization with guidance of the adaptive weighted-sum method.” Struct. Multidiscip. Optim. 57 (3): 1047–1060. https://doi.org/10.1007/s00158-017-1793-2.
Martins, J. R., and A. B. Lambe. 2013. “Multidisciplinary design optimization: A survey of architectures.” AIAA J. 51 (9): 2049–2075. https://doi.org/10.2514/1.J051895.
Mohammad Zadeh, P., and M. Sayadi. 2018. “An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models.” Chin. J. Aeronaut. 31 (6): 1165–1180. https://doi.org/10.1016/j.cja.2018.04.004.
Mohammad Zadeh, P., V. V. Toropov, and A. S. Wood. 2009. “Meta model-based collaborative optimization framework.” Struct. Multidiscip. Optim. 38 (2): 103–115.
Ng, T. T. H., and G. S. B. Leng. 2002. “Application of genetic algorithms to conceptual design of a micro-air vehicle.” Eng. Appl. Artif. Intell. 15 (5): 439–445. https://doi.org/10.1016/S0952-1976(02)00072-6.
Nguyen, N. V., S. M. Choi, W. S. Kim, J. W. Lee, S. Kim, D. Neufeld, and Y. H. Byun. 2013. “Multidisciplinary unmanned combat air vehicle system design using multi-fidelity model.” Aerosp. Sci. Technol. 26 (1): 200–210. https://doi.org/10.1016/j.ast.2012.04.004.
Nguyen, N. V., J. W. Lee, M. Tyan, and D. Lee. 2015. “Possibility-based multidisciplinary optimization for electric-powered unmanned aerial vehicle design.” Aeronaut. J. 119 (1221): 1397–1414. https://doi.org/10.1017/S0001924000011313.
Nguyen, N. V., T. Maxim, H. U. Park, S. H. Kim, and J. W. Lee. 2014. “A multidisciplinary robust optimization framework for UAV conceptual design.” Aeronaut. J. 118 (1200): 123–142. https://doi.org/10.1017/S0001924000009027.
Nosratollahi, M., M. Mortazavi, A. Adami, and M. Hosseini. 2010. “Multidisciplinary design optimization of a reentry vehicle using genetic algorithm.” Aircr. Eng. Aerosp. Technol. 82 (3): 194–203. https://doi.org/10.1108/00022661011075928.
Pagliuca, G., T. Kipouros, and M. A. Savil. 2019. “Surrogate modeling for wing plan form multidisciplinary optimization using model-based engineering.” Int. J. Aerosp. Eng. 8 (May): 1–15. https://doi.org/10.1155/2019/4327481.
Paiva, R. M., A. R. D. Carvalho, C. Crawford, and A. Suleman. 2010. “Comparison of surrogate models in a multidisciplinary optimization framework for wing design.” AIAA J. 48 (5): 995–1006. https://doi.org/10.2514/1.45790.
Park, H. U., J. W. Lee, J. Chung, and K. Behdinan. 2015. “Uncertainty-based MDO for aircraft conceptual design.” Aircr. Eng. Aerosp. Technol. 87 (4): 345–356. https://doi.org/10.1108/AEAT-07-2013-0128.
Rajabi, M. M., B. Ataie-Ashtiani, and H. Janssen. 2015. “Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling.” Adv. Water Resour. 76 (Feb): 127–139. https://doi.org/10.1016/j.advwatres.2014.12.008.
Rao, S. S., and L. Cao. 2002. “Optimal design of mechanical systems involving interval parameters.” J. Mech. Des. 124 (3): 465–472. https://doi.org/10.1115/1.1479691.
Roshanian, J., A. A. Bataleblu, and M. Ebrahimi. 2018. “A novel evolution control strategy for surrogate-assisted design optimization.” Struct. Multidiscip. Optim. 58 (3): 1255–1273. https://doi.org/10.1007/s00158-018-1969-4.
Roshanian, J., A. A. Bataleblu, M. H. Farghadani, and B. Ebrahimi. 2017. “Meta model-based multidisciplinary design optimization of a general aviation aircraft.” In Proc., 12st World Congress on Structural and Multidisciplinary Optimization. Braunschweig, Germany: Springer.
Roskam, J. 1987. Airplane design, part 6: Preliminary calculation of aerodynamic, thrust, and power characteristics. Ottawa, KS: Roskam Aviation and Engineering Corporation.
Roskam, J. 2001. Airplane flight dynamics and automatic flight controls. Lawrence, KS: DARcorporation.
Sadraey, M. H. 2013. Aircraft design: A system engineering approach. Chichester, UK: Wiley.
Schemensky, R. T. 1973. Development of an empirical based computer program to predict the aerodynamic characteristics. Vol 1. Empirical methods. AFFDL-TR-73-144, AD-780100. Springfield, VA: National Technical Information Service.
Sepulveda, E., H. Smith, and D. Sziroczak. 2019. “Multidisciplinary analysis of subsonic stealth unmanned combat aerial vehicle.” CEAS Aeronaut. J. 10 (2): 431–442. https://doi.org/10.1007/s13272-018-0325-0.
Tedford, N., and J. Martins. 2010. “Benchmarking multidisciplinary design optimization algorithms.” Optim. Eng. 11 (1): 159–183. https://doi.org/10.1007/s11081-009-9082-6.
Tianyuan, H., and Y. Xiongqing. 2009. “Aerodynamic/stealthy/structural multidisciplinary design optimization of unmanned combat air vehicle.” Chin. J. Aeronaut. 22 (4): 380–386. https://doi.org/10.1016/S1000-9361(08)60114-4.
Tyan, M., N. V. Nguyen, S. Kim, and W. Lee. 2017. “Database adaptive fuzzy membership function generation for possibility-based aircraft design optimization.” J. Aircr. 54 (1): 114–124. https://doi.org/10.2514/1.C033833.
Viana, F. A. C., V. Balabanov, and V. Toropov. 2014. “Metamodeling in multidisciplinary design optimization: How far have we really come?” AIAA J. 52 (4): 670–690. https://doi.org/10.2514/1.J052375.
Xin-Lai, L., L. Xu, W. Gang-Lin, and W. Zhe. 2006. “Helicopter sizing based on genetic algorithm optimized neural network.” Chin. J. Aeronaut. 19 (3): 212–218. https://doi.org/10.1016/S1000-9361(11)60347-6.
Yao, W., X. Chen, W. Luo, M. Van Tooren, and J. Guo. 2011. “Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles.” Prog. Aerosp. Sci. 47 (6): 450–479. https://doi.org/10.1016/j.paerosci.2011.05.001.
Yao, W., X. Chen, Q. Quyang, and M. V. Tooren. 2012. “A surrogate base multistage-multilevel optimization procedure for multidisciplinary design optimization.” Struct. Multidiscip. Optim. 45 (4): 559–574. https://doi.org/10.1007/s00158-011-0714-z.
Yuan-Ming, X. U., L. I. Shou, and R. Xiao-Min. 2005. “Composite structural optimization by genetic algorithm and neural network response surface modeling.” Chin. J. Aeronaut. 18 (4): 310–316. https://doi.org/10.1016/S1000-9361(11)60250-1.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 34 • Issue 4 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 21, 2020
Accepted: Dec 16, 2020
Published online: Mar 31, 2021
Published in print: Jul 1, 2021
Discussion open until: Aug 31, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.