Multidisciplinary Conceptual Design Optimization of Manned Launch Vehicle Using All At Once Method and Simulated Annealing Algorithm

Document Type : Research Paper

Authors

Abstract

Multidisciplinary design optimization is one of the new methods of design with ability solving complicated problems with large design space including aerospace problems. The purpose of this article is the conceptual design of a two-stage crew launch vehicle with side boosters. Thus, in first phase, in order to achieve a suitable design point, the statistical design technique is used, and then statistical design process is validated by using two degree of freedom simulation and doing energetic-mass calculations. In second phase, multidisciplinary design optimization approach is applied for initial conceptual design optimization. The preferred structure for multidisciplinary design optimization is all-at-once and simulated annealing is used as the optimizer algorithm. Having performed the optimization process, a mass decrease of about 7 tons from missile gross weight was attained with respect to normal simulation results, and as already known, the decrease in gross mass undeniably leads to a consequent decrease in the cost of producing and launching missiles.

[1] William, D. N. 2005. Multidisciplinary Structural Design and Optimization for Performance, Cost and Flexibility. Massachusetts Institute of Technology.
[2] Li, Y., X. Ling, and W. Zhenguo. 2006. Research on Theory and Application of Multidisciplinary Design Optimization of Flight Vehicles. 47th AIAA/ASME/ASCE/AHS/ASC structures, Structural Dynamics and Materials Conferee. Newport. Rhode Island.
[3] American Institute for Aeronautics and Astronautics Inc. (AIAA). 1991. Current State of the Art in Multidisciplinary Design Optimization. Technical report.
[4] Zachary C., Krevor. 2007. A Methodology to Link Cost and Reliability for Launch Vehicle Design. School of Aerospace Engineering Georgia Institute of Technology.
[5] Balling R. J., and J. Sobieszczanski-Sobieski. 1995. An Algorithm for Solving the System-level Problem in Multilevel Optimization. Structural optimization 9. 168-177.
[6] Balling R. J., and J. Sobieszczanski-Sobieski. 1996. Optimization of Coupled Systems: a Critical Overview of Approaches.  AIAA Journal 34. 6-17.
[7] Cramer, E.J., et al., 1994. Problem formulation for multidisciplinary optimization. SIAM Journal of Optimization 4. 754-776.
[8] Alexandrov, N. M. and R. M.  Lewi. 1999. Comparative Properties of Collaborative Optimization and Other Approaches to MDO. MCB University Press.
[9] Thomas, A. Z. and L. G. Lawrence. 1999. Multidisciplinary Design Optimization Techniques. 30th AIAA Fluid Dynamics Conference. Norfolk.
[10] Arora, S., and Q. Wang. 2004. Review of Formulations for Structural and Mechanical System Optimization. The University of Iowa. Iowa City. IA 52242. USA. SMO 1239.
[11] Nathan, P. T., and R. M. Joaquim. 2010. Benchmarking multidisciplinary design optimization algorithms. Optimization and Engineering11. 159–183.
[12] Braun, R. and I. Kroo. 1995. Development and application of the Collaborative Optimization architecture in a multidisciplinary design environment. Multidisciplinary Design Optimization: State of the Art. SIAM: 98-116.
[13] Braun, R. 1996. Collaborative Optimization: An architecture for large-scale distributed design, PhD thesis Stanford University.
[14] Balling, R. J. and J. Sobieszczanski-Sobieski. 1994. An Algorithm for Solving the System-level Problem in Multilevel Optimization. AIAA Paper 94.
[15] Ming, L. 2000. A Study on the Multidisciplinary Design Optimization (MDO) using Collaborative optimization method, Shipbuilding and Marine Engineering Department. Pusan National University. South Korea.
[16] Balesdent, M. 2012. Multidisciplinary Design Optimization of Launch Vehicles. doctoral thesis.
[17] Xiaoqian, C., and et al. 2006. Research on Theory and Application of Multidisciplinary Design Optimization of Flight Vehicles. 47th AIAA /ASME /ASCE /AHS /ASC structures. Structural Dynamics, and Materials Conference. Newport. Rhode Island.
[18] Besnard, E., and C. L. Nicolas. 1999. Design Optimization With Advanced Simulated Annealing. (AIAA 99-0186). 37th AIAA Aerospace Sciences Meeting and Exhibit.
[19] Kirkpatrick, E., and C. D. Gelatt. 1983. Optimization by Simulated Annealing. Science 220.  671-680.
[20] Dr´eo, J., and  A. P´etrowski. 2006. Meta heuristicsfor Hard Optimization. Springer-Verlag Berlin Heidelberg.
[21] Zeeshan, Q., and D. Yunfeng. 2009. Meta-heuristic approach for the conceptual design and optimization of multistage interceptor. 18th World IMACS/MODSIM Congress. Australia.
[22] Steven J. I. 1991 International Reference Guide to Space Launch Systems, Martin Marietta Astronautics Group, Published and Distributed by AIAA.
[23] هاشمی دولابی سید مجتبی، حسین دارابی و جعفر روشنی‌یان. 1391. بررسی مقایسه‌ایی روش طراحی‌ آماری با روش بهینه‌سازی چندموضوعی(AAO) در طراحی مفهومی یک ماهواره‌بر سبک سوخت مایع. فصلنامه علمی و پژوهشی علوم و فنآوری فضایی5 (1) :72-61.
[24] Humble, R. W., G. N. Henry, and Larson. 1995. Space Propulsion Analysis and Design. McGraw-Hill. Inc.
[25] Blake, W.B. 1998. Missile Datcom: User’s manual -1997 Fortran 90 Revision, Wright-Patterson Air Force Base. Ohio.
[26] Robert M. F. 2004. Penalty and Barrier Methods for Constrained Optimization. Massachusetts Institute of Technology.
[27] Benjamin W. Wah., C. Yixin, and T. Wang. 2006. simulated annealing with asymptotic convergence for nonlinear constrained optimization. J Glob Optim. Springer Science.