Volume 20, Issue 1 (January 2020)
Modares Mechanical Engineering 2020, 20(1): 45-56 |
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Mesgary S, Bazazzadeh M, Mostofizadeh A. Comparing Metamodel Methods of Adaptive Basis Function Construction and Artificial Neural Network in Finocyl Grain Design . Modares Mechanical Engineering 2020; 20 (1) :45-56
URL: http://mme.modares.ac.ir/article-15-17487-en.html
URL: http://mme.modares.ac.ir/article-15-17487-en.html
1- Aerospace Department, University Complex of Mechanical Engineering, Malek-Ashtar University of Technology, Shahin-shahr, Iran
2- Aerospace Department, University Complex of Mechanical Engineering, Malek-Ashtar University of Technology, Shahin-shahr, Iran , Bazazzadeh@mut-es.ac.ir
2- Aerospace Department, University Complex of Mechanical Engineering, Malek-Ashtar University of Technology, Shahin-shahr, Iran , Bazazzadeh@mut-es.ac.ir
Abstract: (4483 Views)
Grain design is the most important part of a solid rocket motor. The aim of this study is finocyl grain design based on predetermined objective function with respect to ballistic curves in order to satisfy various thrust performance requirements through an innovative design approach using a genetic algorithm optimization method. The classical sampling method has been used for design space-filling. The level set method has been used for simulating the evolution of the burning surface in the propellant grain. An algorithm has been developed beside the level set code that prepares the initial grain configuration using Pro/Engineer software to export generated models to level set code. The lumped method has been used to perform internal ballistic analysis. Two meta-models are used to surrogate the level set method in the optimization design loop. The first method is based on adaptive basis function construction and the second method is based on the artificial neural network. In order to validate the proposed algorithm, a grain finosyl sample has been investigated. The results show that both grain design method reduced the design time significantly and this algorithm can be used in designing of any grain configuration. In addition, data have more accuracy in grain design based on the artificial neural network, so this method is the more effective and practical method to grain burn-back training.
Article Type: Original Research |
Subject:
Gas Dynamics
Received: 2019/04/21 | Accepted: 2019/05/4 | Published: 2020/01/20
Received: 2019/04/21 | Accepted: 2019/05/4 | Published: 2020/01/20
References
1. Kamran A, Guozhu L. An integrated approach for optimization of solid rocket motor. Aerospace Science and Technology. 2012;17(1):50-64. [Link] [DOI:10.1016/j.ast.2011.03.006]
2. Kamran A, Guozhu L. Design and optimization of 3D radial slot grain configuration. Chinese Journal of Aeronautics. 2010;23(4):409-414. [Link] [DOI:10.1016/S1000-9361(09)60235-1]
3. Dong-Hui W, Yang F, Fan H, Wei-Hua Z. An integrated framework for solid rocket motor grain design optimization. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2014;228(7):1156-1170. [Link] [DOI:10.1177/0954410013486589]
4. Szmelter J, Ortiz P. Burning surfaces evolution in solid propellants: A numerical model. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2007;221(3):429-439. [Link] [DOI:10.1243/09544100JAERO102]
5. Zeeshan Q, Yunfeng D, Ghumman S, Rafique A, Kamran A. Support vector regression-driven multidisciplinary design optimization of a multistage ground based interceptor. AIAA Modeling and Simulation Technologies Conference, 2009 August 10-13, Chicago, Illinois. Reston: AIAA; 2009. [Link] [DOI:10.2514/6.2009-6238]
6. Brown NF, Olds JR. Evaluation of multidisciplinary optimization techniques applied to a reusable launch vehicle. Journal of Spacecraft and Rockets. 2006;43(6):1289-1300. [Link] [DOI:10.2514/1.16577]
7. Rafique AF, Zeeshan Q, Kamran A, Guozhu L. A new paradigm for star grain design and optimization. Aircraft Engineering and Aerospace Technology: An International Journal. 2015;87(5):476-482. [Link] [DOI:10.1108/AEAT-07-2013-0141]
8. Simpson TW, Peplinski JD, Koch PN, Allen JK. On the use of statistics in design and the implications for deterministic computer experiments. ASME Design Engineering Technical Conferences, 1997 September 14-17, Sacramento, California. New York: ASME; 1997. [Link]
9. Hartfield RJ, Jenkins R, Burkhalter J, Foster W. Analytical methods for predicting grain regression in tactical solid-rocket motors. Journal of Spacecraft and Rockets. 2004;41(4):689-693. [Link] [DOI:10.2514/1.3177]
10. Ricciardi A. Complete geometric analysis of cylindrical burning star grains. 25th Joint Propulsion Conference, 12 July 1989 - 16 July 1989, Monterey,CA,U.S.A. Reston: AIAA; 1989. [Link] [DOI:10.2514/6.1989-2783]
11. Hartfield R, Jenkins R, Burkhalter J, Foster W. A review of analytical methods for solid rocket motor grain analysis. 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2003 July 20-23, Huntsville, Alabama. Reston: AIAA; 2003. [Link] [DOI:10.2514/6.2003-4506]
12. Kamran A, Gouzhu L, Godil J, Siddique Z, Zeeshan Q, Rafique A. Design and performance optimization of Finocyl grain. AIAA Modeling and Simulation Technologies Conference, 2009 August 10-13, Chicago, Illinois. Reston: AIAA; 2009. [Link] [DOI:10.2514/6.2009-6234]
13. Coats D, Levine JN, Nickerson GR, Tyson TJ, Cohen NS, Barry DP, et al. A computer program for the prediction of solid propellant rocket motor performance. Vol. I, II, and III. Unknown City: Defense Technical Information Center (DTIC); 1975. [Link] [DOI:10.21236/ADA015140]
14. Barron J. Generalized coordinate grain design and internal ballistics evaluation program. 3rd Solid Propulsion Conference, 1968 June 4-6, Atlantic City, New Jersey. Reston: AIAA; 1968. [Link] [DOI:10.2514/6.1968-490]
15. Coats D, Nickerson G, Dang A, Dunn S, Kehtarnavaz H. Solid performance program (SPP). 23rd Joint Propulsion Conference, 1987 29 June-2 July, San Diego, California. Reston: AIAA; 1987. [Link] [DOI:10.2514/6.1987-1701]
16. Levine JN, Nickerson GR, Tyson TJ, Cohen NS, Barry DP, Price CF. A Computer program for the prediction of Solid Propellant Rocket Motor Performance. Technical Report, Vol. I, FRPL‐TR‐80‐34. Carson: Software and Engineering Associates; 1981. [Link]
17. Toker KA. Three-dimensional retarding walls and flow in their vicinity [Dissertation]. Ankara: The School of Natural and Applied Sciences Middle East Technical University; 2004. [Link]
18. Cavallini E. Modeling and numerical simulation of solid rocket motors internal ballistics [Dissertation]. Rome: Sapienza University of Rome; 2010. [Link]
19. Willcox MA, Brewster MQ, Tang KC, Stewart DS. Solid propellant grain design and burnback simulation using a minimum distance function. Journal of Propulsion and Power. 2007;23(2):465-475. [Link] [DOI:10.2514/1.22937]
20. Pons Lorente A. Study of grain burnback and performance of solid rockets motors [Dissertation]. Barcelona: Polytechnic University of Catalonia; 2013. [Link]
21. Sullwald W. Grain regression analysis [Dissertation]. Stellenbosch: Stellenbosch University; 2014. [Link]
22. Ghasemi H, Barkhordar A. Numerical simulation of complicated grain burnback in three dimensions. Journal of Space Science and Technology. 2012;5(1):15-28. [Persian] [Link]
23. Estekhareh SGM, Mostofizadeh A, Fouladi N. Numerical simulation of the grain burn-back in solid propellant rocket motor based on level set method. Iranian Scientific Association of Energetic Material. 2012;7(3):29-40. [Persian] [Link]
24. Gheisari MM, Mirsajedi SM. Using marching cube algorithm for 3D grain burn-back analysis in solid rocket motors based on level set method. Modares Mechanical Engineering. 2015;14(15):85-95. [Persian] [Link]
25. Zeping W, Donghui W, Weihua Z, Okolo PN, Yang F. Solid-rocket-motor performance-matching design framework. Journal of Spacecraft and Rockets. 2017;54(3):698-707. [Link] [DOI:10.2514/1.A33655]
26. Zhao L, Choi KK, Lee I. Metamodeling method using dynamic kriging for design optimization. AIAA Journal. 2011;49(9):2034-2046. [Link] [DOI:10.2514/1.J051017]
27. Wang GG, Shan S. Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design. 2007;129(4):370-380. [Link] [DOI:10.1115/1.2429697]
28. Ryberg AB, Domeij Bäckryd R, Nilsson L. Metamodel-based multidisciplinary design optimization for automotive applications. Linköping: Linköping University Electronic Press; 2012. [Link]
29. Haftka RT, Scott EP, Cruz JR. Optimization and experiments: A survey. Applied Mechanics Reviews. 1998;51(7):435-448. [Link] [DOI:10.1115/1.3099014]
30. Park HS, Dang XP. Structural optimization based on CAD-CAE integration and metamodeling techniques. Computer-Aided Design. 2010;42(10):889-902. [Link] [DOI:10.1016/j.cad.2010.06.003]
31. Arabnia M. Aerodynamic shape optimization of axial turbines in three dimensional flow [Dissertation]. Montreal: Concordia University; 2012. [Link]
32. Jēkabsons G, Lavendels J. A comparison of subset selection and adaptive basis function construction for polynomial regression model building. Computer Sciences. 2009;38(38):187-197. [Link] [DOI:10.2478/v10143-009-0017-7]
33. Jekabsons G. Adaptive basis function construction: An approach for adaptive building of sparse polynomial regression models. In: Zhang Y, editor. Machine learning. Norderstedt: Bod-Books on Demand; 2010. pp. 127-155. [Link] [DOI:10.5772/9157]
34. Jekabsons G, Lavendels J. A comparison of heuristic methods for polynomial regression model induction. Mathematical Modelling and Analysis. 2008;13(1):17-27. [Link] [DOI:10.3846/1392-6292.2008.13.17-27]
35. Mesgari S, Bazazzadeh M, Mostofizadeh A. Finocyl grain design using the genetic algorithm in combination with adaptive basis function construction. International Journal of Aerospace Engineering. 2019;2019:3060173. [Link] [DOI:10.1155/2019/3060173]
36. Willcox MA, Brewster MQ, Tang KC, Stewart DS, Kuznetsov I. Solid rocket motor internal ballistics simulation using three-dimensional grain burnback. Journal of Propulsion and Power. 2007;23(3):575-584. [Link] [DOI:10.2514/1.22971]
37. Sethian JA. Level set methods and fast marching methods. Journal of Computing and Information Technology. 2003;1:1-2. [Link]
38. Saintout E, Le Roux A, Ribereau D, Perrin P. ELEA: A tool for 3D surface regression analysis in propellant grains. 25th Joint Propulsion Conference, 1989 July 12-16, Monterey, California. Reston: AIAA; 1989. [Link] [DOI:10.2514/6.1989-2782]
39. Di Giacinto M, Favini B, Cavallini E, Serraglia F. An ignition-to-burn out analysis of SRM internal ballistic and performances. 44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2008 July 21-23, Hartford, Connecticut. Reston: AIAA; 2008. [Link] [DOI:10.2514/6.2008-5141]
40. Humble RW, Henry GN, Larson WJ. Space propulsion analysis and design. New York: McGraw-Hill; 1995. [Link]
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