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Modeling and optimization of a reluctance accelerator using DOE-based response surface methodology

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Abstract

This paper introduces experiment-based modeling and optimization of a reduced-scale Electromagnetic launcher (EML) using the Design of experiments (DOE) technique. Response surface models describing the velocity and kinetic energy of the launched projectile were developed using the Box–Behnken method with design variable transforms, and an Analysis of variance (ANOVA) was conducted to refine the models by removing statistically insignificant terms. A bi-objective optimization problem with the maximum velocity and maximum kinetic energy as objects was considered, and a Pareto front was obtained using the generated response surfaces as the solution of the problem. Verification tests on the optimal design points were conducted to demonstrate the validity of the developed models.

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References

  1. A. D. Ketsdever, M. P. Young and J. B. Mossman, Overview of advanced concepts for space access, Journal of Spacecraft and Rockets, 47 (2) (2010) 239–245.

    Article  Google Scholar 

  2. E. M. Schmidt, Comparison of conventional and electromagnetic gun launch, AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Fort Lauderdale, FL, USA (2004) AIAA 2004-3644.

    Google Scholar 

  3. I. R. McNab, Launch to space with an electromagnetic railgun, IEEE Transactions on Magnetics, 39 (1) (2003) 295–296.

    Article  Google Scholar 

  4. M. S. Aubuchon and T. R. Lockner, Results from Sandia National Laboratories/Lockheed Martin Electromagnetic Missile Launcher (EMML), IEEE Pulsed Power Conference, Monterey, CA, USA (2005).

    Google Scholar 

  5. B. N. Turman, Coilgun launcher for nanosatellites, 2nd International Conference Integrated Micro/Nanotechnology Space Application, Pasadena, CA, USA (1999).

    Google Scholar 

  6. D. A. Wetz, F. Stefani and I. R. McNab, Experimental results on a 7-m-long plasma-driven electromagnetic launcher, IEEE Transactions on Plasma Science, 39 (1) (2011) 180–185.

    Article  Google Scholar 

  7. I. R. McNab, Progress on hypervelocity railgun research for launch to space, IEEE Transactions on Magnetics, 45 (1) (2009) 381–382.

    Article  Google Scholar 

  8. U. Hasirci, A. Balikci, Z. Zabar and L. Birenbaum, Concerning the design of a novel electromagnetic launcher for Earthto- orbit micro- and nanosatellite system, IEEE Transactions on Plasma Science, 39 (1) (2011) 498–499.

    Article  Google Scholar 

  9. D. A. Bresie and J. A. Andrews, Design of a reluctance accelerator, IEEE Transactions on Magnetics, 27 (1) (1991) 623–625.

    Article  Google Scholar 

  10. S. K. Ingram and S. B. Pratap, A control algorithm for reluctance accelerators, IEEE Transactions on Magnetics, 27 (1) (1991) 156–159.

    Article  Google Scholar 

  11. G. W. Slade, A simple unified physical model for a reluctance accelerator, IEEE Transactions on Magnetics, 41 (11) (2005) 4270–4276.

    Article  Google Scholar 

  12. A. Mosallanejad and A. Shoulaie, A novel structure to enhance magnetic force and velocity in tubular linear reluctance motor, Turkish Journal of Electrical Engineering & Computer Sciences, 20 (1) (2012) 1063–1076.

    Google Scholar 

  13. A. Mosallanejad and A. Shoulaie, Calculation and measurement of coil inductance profile in tubular linear reluctance motor and its validation by three dimension FEM, Journal of Electrical Engineering, 62 (4) (2011) 220–221.

    Article  Google Scholar 

  14. L. M. Cooper, A. R. V. Cleef, B. T. Bristoll and P. A. Bartlett, Reluctance accelerator efficiency optimization via pulse shaping, IEEE Access, 2 (2014) 1143–1148.

    Article  Google Scholar 

  15. H. Xiang, B. Lei, Z. Li and K. Zhao, Design and experiment of reluctance electromagnetic launcher with new-style armature, IEEE Transactions on Plasma Science, 41 (5) (2013) 1066–1069.

    Article  Google Scholar 

  16. A. Waindok and G. Mazur, Mutual inductances in a mathematical model of the three-stage reluctance accelerator, Proceedings of the 3rd International Students Conference on Electrodynamics and Mechatronics, Opole, Poland (2011).

    Google Scholar 

  17. S. J. Lee, J. H. Kim, B. S. Song and J. H. Kim, Coil gun electromagnetic launcher (EML) system with multi-stage electromagnetic coils, Journal of Magnetics, 18 (4) (2013) 481–486.

    Article  Google Scholar 

  18. S. J. Lee, L. Kulinsky, B. Park, S. H. Lee and J. H. Kim, Design optimization of coil gun to improve muzzle velocity, Journal of Vibroengineering, 17 (2) (2015) 554–561.

    Google Scholar 

  19. D. R. Brown and E. P. Hamilton, Electromechanical Energy Conversion, Macmillan, New York, USA (1984) 14–17.

    Google Scholar 

  20. J. F. Gieras, Z. J. Piech and B. Z. Tomczuk, Linear Synchronous Motors, CRC Press, New York, USA (2011) 158.

    Google Scholar 

  21. J. H. Kim, S. W. Jeon and J. N. Kim, A mathematical and physical model for the design of a single stage coilgun, KARI Aerospace Engineering and Technology, 2 (2) (2013) 76–78.

    Google Scholar 

  22. G. Rizzoni, Principles and Applications of Electrical Engineering, Irwin, Boston, MA, USA (1993) 277–281.

    Google Scholar 

  23. D. Montgomery, Design and Analysis of Experiments, 7th ed., John Wiley & Sons, Hoboken, NJ, USA (2009) 439–446.

    Google Scholar 

  24. R. H. Myers, D. C. Montgomery and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd ed., John Wiley & Sons, Hoboken, NJ, USA (2009) 26–27., 220, 319-321.

    MATH  Google Scholar 

  25. NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ [cited 9 February 2016].

  26. Statistics and Machine Learning Toolbox, http://www.mathworks.com/products/statistics/ [cited 18 April 2016].

  27. I. Y. Kim and O. L. de Weck, Adaptive weighted sum method for multiobjective optimization: A new method for Pareto front generation, Structural and Multidisciplinary Optimization, 31 (2) (2006) 105–107.

    Article  MathSciNet  MATH  Google Scholar 

  28. T. Coleman, M. A. Branch and A. Grace, Optimization Toolbox: Users Guide, Ver. 2, The MathWorks Inc., Natick, MA (1999).

    Google Scholar 

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Correspondence to Jaemyung Ahn.

Additional information

Recommended by Associate Editor Gil Ho Yoon

Jihun Kim earned B.S. and M.S. in mechanical design and production engineering from Hanyang University in 1997 and 1999. He is currently a Ph.D. student in the Department of Aerospace Engineering at the Korea Advanced Institute of Technology (KAIST; Daejeon, South Korea) and has been working as a Senior Researcher for the Korea Aerospace Research Institute (2002–present). His research interests are the design and test evaluation of an electromagnetic launcher and a space launch vehicle.

Jaemyung Ahn is currently an Associate Professor of aerospace engineering at the Korea Advanced Institute of Science and Technology (KAIST; Daejeon, South Korea). His research interests include systems engineering, Design of experiments (DOE), and aerospace vehicle controls. He previously worked for the Korea Aerospace Research Institute (1999–2004) as a System Engineer and for Bain & Company (2008–2010) as a Management Consultant. He received his B.S. and M.S. degrees from Seoul National University in 1997 and 1999, and a Ph.D. degree in aeronautics and astronautics from MIT in 2008.

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Kim, J., Ahn, J. Modeling and optimization of a reluctance accelerator using DOE-based response surface methodology. J Mech Sci Technol 31, 1321–1330 (2017). https://doi.org/10.1007/s12206-017-0231-0

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  • DOI: https://doi.org/10.1007/s12206-017-0231-0

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