Vol. 43
Latest Volume
All Volumes
PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2012-08-23
On the Design and Reliability Analysis of Electromagnetic Absorbers Using Real-Coded Genetic Algorithm and Monte Carlo Simulation
By
Progress In Electromagnetics Research B, Vol. 43, 169-187, 2012
Abstract
In this paper, we propose an approach for designing and quantitatively assessing the performance of the multilayered radar-absorbing structure. In our proposed approach, a five layered radarabsorbing materials design is optimized from the predefined materials database. But to determine the optimal choice of the material and thickness of each layer, a combined binary and real-coded genetic algorithm (GA) is used to handle the integer and real variables involved in such designs. Further, the proposed approach employs the Latin hypercube sampling with Monte Carlo Simulation to carry out the performance based reliability analysis of the design. Absorber synthesized results are compared with the published work using other algorithms. The outcomes of our approach show that the combined GA works quite well, and most prominently the reliability analysis provides the decision maker a means to select among the several design alternatives available before him.
Citation
Heeralal Gargama, Sanjay Kumar Chaturvedi, and Awalendra K. Thakur, "On the Design and Reliability Analysis of Electromagnetic Absorbers Using Real-Coded Genetic Algorithm and Monte Carlo Simulation," Progress In Electromagnetics Research B, Vol. 43, 169-187, 2012.
doi:10.2528/PIERB12061107
References

1. Chung, D. D. L., "Materials for electromagnetic interference shielding," Journal of Material Engineering and Performance, Vol. 9, 350-354, 2000.
doi:10.1361/105994900770346042

2. Paul, C. R., Introduction to Electromagnetic Compatibility, 2nd Ed., John Wiley & Sons, Inc., Hoboken, NJ, USA, 2006.

3. Morgan, D. A., Handbook for EMC Testing and Measurement, The Institution of Engineering and Technology, London, 2007.

4. Hoang, N. H., J. L. Wojkiewicz, J. L. Miane, and R. S. Biscarro, "Lightweight electromagnetic shields using optimised polyaniline composites in the microwave band," Polymers for Advanced Technologies, Vol. 18, 257-262, 2007.
doi:10.1002/pat.829

5. Gargama, H., S. K. Chaturvedi, and A. K. Thakur, "Design and optimization of multilayered electromagnetic shield using a real-coded genetic algorithm," Progress In Electromagnetics Research B, Vol. 39, 241-266, 2012.
doi:10.2528/PIERB12011902

6. IEEE Standard 299-1997 (Revision of IEEE Standard 299-1991) IEEE Standard Method for Measuring the Effectiveness of Electromagnetic Shielding Enclosures, The Institute of Electrical and Electronics Engineers, Inc., New York, 1998.

7. Micheli, D., C. Apollo, R. Pastore, et al. "Optimization of multilayer shields made of composite nanostructured materials," IEEE Transactions on Electromagnetic Compatibility, Vol. 54, No. 1, 60-69, 2012.
doi:10.1109/TEMC.2011.2171688

8. Markham, D., "Shielding: Quantifying the shielding requirements for portable electronic design and providing new solutions by using a combination of materials and design," Materials and Design, Vol. 21, No. 1, 45-50, 2000.
doi:10.1016/S0261-3069(99)00049-7

9. Michielssen, E., J.-M. Sajer, S. Ranjithan, et al. "Design of lightweight, broad-band microwave absorbers using genetic algorithms," IEEE Transactions on Microwave Theory and Techniques, Vol. 41, No. 6-7, 1024-1030, 1993.
doi:10.1109/22.238519

10. Cui, S. and D. S. Weile, "Robust design of absorbers using genetic algorithms and the finite element-boundary integral method," IEEE Transactions on Antennas and Propagation, Vol. 51, No. 12, 3249-3258, 2003.
doi:10.1109/TAP.2003.820971

11. Jie, Y., G. Xiao, and M.-S. Cao, "A novel method of computation and optimization for multi-layered radar absorbing coatings using open source software," Materials and Design, Vol. 27, 45-52, 2006.
doi:10.1016/j.matdes.2004.09.009

12. Micheli, D., R. Pastore, C. Apollo, et al. "Broadband electromagnetic absorbers using carbon nanostructure-based composites," IEEE Transactions on Microwave Theory and Techniques, Vol. 59, No. 10, 2633-2646, 2011.
doi:10.1109/TMTT.2011.2160198

13. Goudos, S. K., "A versatile software tool for microwave planar radar absorbing materials design using global optimization algorithms," Materials and Design, Vol. 28, 2585-2895, 2007.
doi:10.1016/j.matdes.2006.10.016

14. Goudos, S. K., "Design of microwave broadband absorbers using a self-adaptive differential evolution algorithm," International Journal of RF and Microwave Computer-aided Engineering, Vol. 19, No. 3, 364-372, 2009.
doi:10.1002/mmce.20357

15. Dib, N., M. Asi, A, and Sabbah, "International Journal of RF and Microwave Computer-aided Engineering," Progress In Electromagnetics Research C, Vol. 13, 171-185, 2010.
doi:10.2528/PIERC10041310

16. Asi, M. J. and N. I. Dib, "Design of multilayer microwave broadband absorbers using central force optimization," Progress In Electromagnetics Research B, Vol. 26, 101-113, 2010.
doi:10.2528/PIERB10090103

17. Deb, K. and R. B. Agrawal, "Simulated binary crossover for continuous search space," Complex Systems, Vol. 9, 115-148, 1995.

18. Deb, K. and M. Goyal, "A combined genetic adaptive search (Gene AS) for engineering design," Computer Science and Informatics, Vol. 26, No. 4, 30-45, 1996.

19. Deb, K., "An efficient constraint handling method for the genetic algorithm," Computer Methods in Applied Mechanics and Engineering, Vol. 186, 311-338, 2000.
doi:10.1016/S0045-7825(99)00389-8

20. Zhang, J. Y., S. Y. Liang, J. Yao, et al. "Evolutionary optimization of machining processes," Journal of Intelligent Manufacturing, Vol. 17, 203-215, 2006.
doi:10.1007/s10845-005-6637-z

21. Deep, K., K. P. Singh, and M. L. Kansal, "A real coded genetic algorithm for solving integer and mixed integer optimization problems," Applied Mathematics and Computation, Vol. 212, 505-518, 2009.
doi:10.1016/j.amc.2009.02.044

22. Gargama, H., S. K. Chaturvedi, and A. K. Thakur, "Electromagnetic interference shielding design using real-coded genetic algorithm and reliability evaluation in X-band," Proceedings of the International Conference on SocProS 2011, Advances in Intelligent and Soft Computing, Vol. 131, 331-342, 2012.

23. Haldar, A. and S. Mahadevan, Reliability Assessment Using Stochastic Finite Element Analysis, John Wiley & Sons Inc., New York, 2000.

24. Páez, E., M. A. Azpúrua, C. Tremolo, and R. C. Callarotti, "Uncertainty estimation in complex permittivity measurements by shielded dielectric resonator technique using the Monte Carlo method," Progress In Electromagnetics Research B, Vol. 41, 101-119, 2012.

25. Azpúrua, M., C. Tremolo, and E. Páez, "Comparison of the gum and Monte Carlo methods for the uncertainty estimation in electromagnetic compatibility testing," Progress In Electromagnetics Research B, Vol. 34, 125-144, 2011.

26. Helton, J. C. and F. J. Davis, "Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems," Reliability Engineering and System Safety, Vol. 81, 23-69, 2003.
doi:10.1016/S0951-8320(03)00058-9

27. Chew, W. C., Waves and Fields in Inhomogeneous Media, Reprinted by IEEE Press, Van Nostrand Reinhold, New York, 1995.

28. Kapur, K. C. and L. R. Lamberson, Reliability in Engineering Design, Reprint, Wiley India Pvt. Ltd., New Delhi, 2009.

29. Foschi, R. O., H. Li, and J. Zhang, "Reliability and performance-based design: A computational approach and applications," Structural Safety, Vol. 24, 205-218, 2002.
doi:10.1016/S0167-4730(02)00025-5

30. Singh, D., A. Kumar, S. Meena, and V. Agarwala, "Analysis of frequency selective surfaces for radar absorbing materials," Progress In Electromagnetics Research B, Vol. 38, 297-314, 2012.

31. Schucany, W. R., "Kernel smoothers: An overview of curve estimators for the first graduate course in nonparametric statistics," Statistical Science, Vol. 19, No. 4, 663-675, 2004.
doi:10.1214/088342304000000756