نوع مقاله : مقالة تحقیقی (پژوهشی)
نویسندگان
1 دانشیار، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناوری، تهران، ایران
2 دانشجوی دکتری، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناوری
چکیده
در این پژوهش، بهینه سازی مقاوم چندموضوعی پیکربندی کپسول بازگشتی با توجه به ملاحظات آیروترمودینامیک، مسیر، پایداری و هندسه بصورت چندهدفه انجام شده است. بیشینه سازی بازده حجمی، کمینه سازی ضریب بالستیک و بیشینه سازی پایداری استاتیکی کپسول بازگشتی اهداف در نظر گرفته شده در فرایند بهینه سازی مقاوم پیکربندی کپسول بازگشتی در حضور عدم قطعیت ها می باشند؛علاوه بر این، قیودی در زمینه های هندسه، بار حرارتی و ضریب بار در فرایند بهینه سازی لحاظ شده اند. برای کاهش زمان و هزینه بهینه سازی مقاوم، از روش شبیه سازی مونت کارلو تطبیقی استفاده شده تا تعداد ارزیابی های مورد نیاز در حین بهینه سازی مقاوم کاهش یابد. با استفاده از الگوریتم ژنتیک چندهدفه مقید، مجموعه ای از پیکربندی های بهینه مقاوم کپسول بازگشتی بدست می آیند. نتایج بدست آمده نشان می دهند که عملکرد پیکربندی های بهینه مقاوم حاصله به نحوی است که قیود درنظرگرفته شده حتی در حضور عدم قطعیت ها با سطح اطمینان 8/99% نقض نمی شوند.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Multidisciplinary optimization for configuration of a reentry capsule considering uncertainty
نویسندگان [English]
- Hamed Hashemi Mehneh 1
- amirreza Ghedamini Harouni 2
1 Associate professor, Aerospace Research Institute, MSRT, Tehran, Iran
2 Ph.D. student, Aerospace Research Institute, MSRT. Tehran, Iran
چکیده [English]
The robust multi-disciplinary, multi-objective shape optimization of re-entry capsule with aero-thermodynamic, trajectory, stability and the geometry considerations are presented in this paper. In this research, the results of maximizing the volumetric efficiency of the capsules while minimizing the ballistic coefficient and the longitudinal stability derivative with considering uncertainties are discussed in presence of some constraints on geometry, heating load, and load factor. To reduce the time and cost of robust optimization, the Adaptive Monte Carlo Simulation technique is used which decreases the number of required evaluations within the robust optimization process. Utilizing the constrained multi-objective genetic algorithm will result in a collection of robust optimal solutions. The results show that the performance of obtained robust optimal configurations is in a way that the considered constraints aren’t violated with 99.8% of confidence level even in the presence of uncertainties.
کلیدواژه‌ها [English]
- Robust Optimization
- Multidisciplinary Optimization
- Uncertainty
- Multi-objective Optimization
- Reentry Capsule
- Ridolfi, E. Mooij, D. Dirkx, S. Corpino, Robust multi-disciplinary optimization ofunmanned entry capsules, AIAA Modeling and Simulation Technologies Conference, 2012.
- Sudmeijer, E. Mooij, Shape Optimization for a Small Experimental Re-entry Module, AIAA/AAAF 11th International Space Planes and Hypersonic Systems and Technologies Conference, 2002.
- Sun, G. Zhang, N. Vlahopoulos, S. B. Hong, Multi-disciplinary design optimization under uncertainty for thermal protection system applications, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006.
- Akhtar, H. Linshu, An efficient evolutionary multi-objective approach for robust design of multi-stage space launch vehicle, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p. 7073, 2006.
- Zhang, J. He, N. Vlahopoulos, Multidisciplinary design under uncertainty for a hypersonic vehicle, 13th AIAA/ISSMO multidisciplinary analysis optimization conference, 2010.
- Jodei, M. Ebrahimi, J. Roshanian, Multidisciplinary design optimization of a small solid propellant launch vehicle using system sensitivity analysis,Structural and Multidisciplinary Optimization, Vol. 38, No. 1, pp. 93-100, 2009.
- Ebrahimi, M. R. Farmani, J. Roshanian, Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization,Structural and Multidisciplinary Optimization, Vol. 44, No. 6, pp. 773-784, 2011.
- M. Ryan, M. J. Lewis, K. H. Yu, Comparison of robust optimization methods applied to hypersonic vehicle design, Journal of Aircraft, Vol. 52, No. 5, pp. 1510-1523, 2015.
- Luo, J. Zheng, Efficient MOEAs with an adaptive sampling technique in searching robust optimal solutions, In Intelligent Control and Automation, 7th World Congress on, IEEE, pp. 117-123, 2008.
- A. Zang, M. J. Hemsch, M. W. Hilburger, S. P. Kenny, J. M. Luckring, P. Maghami, S. L. Padula, W. J. Stroud, Needs and opportunities for uncertainty-based multidisciplinary design methods for aerospace vehicles, Technical Report TM-2002-211462, NASA, 2002.
- Tang, J. Périaux, Uncertainty based robust optimization method for drag minimization problems in aerodynamics, Computer Methods in Applied Mechanics and Engineering, Vol. 217, pp. 12-24, 2012.
- G. Crespo, D. M. Bushnell, Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs, 2002.
- Roshanian, M. Ebrahimi and E.Bataleblu, " Survey on Nondeterministic Optimal Design and Its Applications in the Aerospace Industry," Journal of Space Science and Technology, Vol. 4, No. 3 & 4, Fall 2011 and Winter 2012.
- Padula, W. Li, Options for robust airfoil optimization under uncertainty, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, p. 5602, 2002.
- Jin, J. Branke, Evolutionary optimization in uncertain environments-a survey, IEEE Transactions on evolutionary computation, Vol. 9, No. 3, pp. 303-317, 2005.
- M. Zentner, A design space exploration process for large scale, multi-objective computer simulations, PhD Thesis, Georgia Institute of Technology, 2006.
- Hassan, W. Crossley, Spacecraft reliability-based design optimization under uncertainty including discrete variables, Journal of Spacecraft and Rockets, Vol. 45, No. 2, pp. 394-405, 2008.
- D. McKay, R. J. Beckman, W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, Vol. 42, No. 1, pp. 55-61, 2000.
- H. Halton, On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik, Vol. 2, No. 1, pp. 84-90, 1960.
- Kocis, W. J. Whiten, Computational investigations of low-discrepancy sequences, ACM Transactions on Mathematical Software (TOMS), Vol. 23, No. 2, pp. 266-294, 1997.
- E. Melchers, Simulation in time-invariant and time-variant reliability problems, Reliability and Optimization of Structural Systems’ 91, pp. 39-82, Springer, 1992.
- Y-T. Wu, Computational methods for efficient structural reliability and reliability sensitivity analysis, AIAA journal, Vol. 32, No. 8, pp. 1717-1723, 1994.
- Dirkx, E. Mooij, Continuous aerodynamic modelling of entry shapes, AIAA Atmospheric Flight Mechanics Conference, 2011.
- Theisinger, R. D. Braun, Multi-objective hypersonic entry aeroshell shape optimization, Journal of Spacecraft and Rockets, Vol. 46, No. 5, pp. 957-966, 2009.
- E. Theisinger, R. D. Braun, Hypersonic entry aeroshell shape optimization, MS Special Problems Report,Vol. 12,Georgia Institute of Technology, 2007.
- R. Ghaedamini Harouni, S. H. Hashemi Mehne, Multi-Disciplinary Multi-Objective Shape Optimization of Orion Type Re-entry Capsule, Modares Mechanical Engineering, Vol. 19, No. 3, pp. 665-675, 2019.
- Dirkx, E. Mooij, Optimization of entry-vehicle shapes during conceptual design,Acta Astronautica, Vol. 94, No. 1, pp. 198-214, 2014.
- B. Craidon, A description of the Langley wireframe geometry standard (LaWGS) format, Technical Report TM 85767, NASA, 1985.
- Theisinger, R. Braun, I. Clark, Aerothermodynamic Shape Optimization of Hypersonic Entry Aeroshells, 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, 2010.
- E. Gentry, D. N. Smyth, W. R. Oliver, The Mark IV Supersonic-Hypersonic Arbitrary-Body Program. Volume II- Program Formulation, AFFDL-TR-73-159, USAF Flight Dynamics Laboratory, 1973.
- A. Fay, Theory of stagnation point heat transfer in dissociated air, Journal of the Aeronautical Sciences, Vol. 25, No. 2, pp. 73-85, 1958.
- Ashley, Engineering analysis of flight vehicles, Courier Corporation, 1992.
- L. Hankey, Re-entry aerodynamics, American Institute of Aeronautics and Astronautics, 1988.
- J. Regan, S. M. Anandakrishnan, Dynamics of atmospheric re-entry, American Institute of Aeronautics and Astronautics, 1993.
- S. ATMOSPHERE, NOAA-S/T76-1562, US Government Printing Office, Washington, DC, 1976.
- Nosratollahi, M. Mortazavi, A. Adami, M. Hosseini, Multidisciplinary design optimization of a reentry vehicle using genetic algorithm, Aircraft Engineering and Aerospace Technology, Vol. 82, No. 3, pp. 194-203, 2010.
- Deb, A. Pratap, S. Agarwal, T. A. M. T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transaction on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197, 2002.
- Adami, M. Nosratollahi, M. Mortazavi, M. Hosseini, Multidisciplinary design optimization of a manned reentry mission considering trajectory and aerodynamic configuration, Proceedings of 5th International Conference on Recent Advances in Space Technologies - RAST2011, IEEE, pp. 598-603, 2011.
- J. Bertin, Hypersonic aerothermodynamics, American Institute of Aeronautics and Astronautics, 1994.
- J. Sellers, W. J. Astore, R. B. Giffen, W. J. Larson, Understanding space: an introduction to astronautics, Primis, 2000.
- W. Tang, M. Orlowski, J. M. Longo, P. Giese, Aerodynamic optimization of re-entry capsules, Aerospace science and technology, Vol. 5, No. 1, pp. 15-25, 2001.
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