Reducing Electromagnetic Coupling in Shielded Enclosures using a Genetic Algorithm -- Finite-Difference Time-Domain Solver

Authors

  • Russell Iain Macpherson Department of Engineering King’s College University of Aberdeen Aberdeen AB24 3UE United Kingdom
  • Nick J Ryan Department of Engineering King’s College University of Aberdeen Aberdeen AB24 3UE United Kingdom

Keywords:

Reducing Electromagnetic Coupling in Shielded Enclosures using a Genetic Algorithm -- Finite-Difference Time-Domain Solver

Abstract

Comprehensive shielding in modern electronic equipment may lead to resonant behaviour within the equipment enclosure. This paper presents a method for optimising the placement of sources of electromagnetic (EM) energy and susceptors to this EM energy within an enclosed resonant cavity. The source and susceptor are placed on a dielectric slab within a perfectly conducting enclosure to reduce the level of EM coupling between the two. Optimisation is facilitated using a genetic algorithm coupled with a finite-difference time-domain solver. Results are presented for single objective optimisation and multi-objective optimisation cases.

Downloads

Download data is not yet available.

References

J. Mix, G. Haussmann, M. Piket-May, and

K. Thomas, “EMC/EMI design and analysis using

FDTD,” in IEEE Int. EMC Symp., vol. 1, (Denver,

CO), pp. 177–181, 1998.

R. I. Macpherson and N. J. Ryan, “The use of a

genetic algorithm and the FDTD method for lim-

iting resonance in shielded enclosures,” in Proceed-

5e+08 1e+09 1.5e+09 2e+09 2.5e+09 3e+09

Frequency (Hz)

2

4

6

8

Normalised DFT

Figure 7: DFTs of Ez at the susceptor point of the

multi-objective simulation with objective of minimising

the 1.002GHz and 2.004GHz frequency components of

the response. Shown are the initial DFT before optimisa-

tion begins (dashed line) and the final DFT after optimi-

sation (solid line).

ings of EMC Europe 2000, Brugge, 4th European

Symposium on Electromagnetic Compatibility, vol. 2,

pp. 203–208, September 2000.

D. Goldberg, Genetic Algorithms in Search, Op-

timization and Machine Learning. Reading, MA:

Addison-Wesley, 1989.

R. L. Haupt and S. E. Haupt, Practical Genetic Al-

gorithms. John Wiley & Sons, 1998.

J. Holland, Adaption in Natural and Artificial Sys-

tems. The University of Michigan Press, 1975.

K. Krishnakumar, “Micro-genetic algorithms for sta-

tionary and non-stationary function optimization,”

SPIE: Intelligent Control and Adaptive Systems,

vol. 1196, pp. 289–296, 1989.

J. Jiang and G. P. Nordin, “A rigorous unidirectional

method for designing finite aperture diffractive opti-

cal elements,” Optics Express, vol. 7, no. 6, pp. 237–

, 2000.

G. J. E. Rawlins, ed., Foundations of Genetic Algo-

rithms. Morgan Kaufmann, 1991.

D. E. Knuth, The Art of Computer Programming,

vol. 2, Semi-numerical Algorithms. Reading, Mas-

sachusetts: Addison-Wesley, 2 ed., 1981.

P. Hallekalek, “Good random number generators are

(not so) easy to find,” Mathematics and Computers

in Simulation, vol. 46, pp. 485–505, 1998.

P. L’Ecuyer, “Uniform random number generation,”

Ann. Oper. Res., vol. 53, pp. 77–120, 1994.

ACES JOURNAL, VOL. 17, NO. 3, NOVEMBER 2002

P. L’Ecuyer, Random Number Generation. New

York: In Handbook of Simulation, Jerry Banks (ed.)

Wiley, 1997.

A. Taflove, Computational Electrodynamics: The

Finite-Difference Time-Domain Method. Boston,

MA: Artech House, 1995.

K. S. Yee, “Numerical solution of initial bound-

ary value problems involving Maxwell’s equations in

isotropic media,” IEEE Transactions on Antennas

and Propagation, vol. 14, pp. 302–307, Mar. 1966.

B. Archambeault, O. M. Ramahi, and C. Brench,

EMI/EMC Computational Modeling Handbook.

Kluwer Academic Publishers, 1998.

J. B. Schneider and K. Shlager, “Finite-Difference

Time-Domain literature database.” WWW. Avail-

able at http://www.fdtd.org Last Accessed 20th

April 2002.

D. L. Carroll, “Fortran Genetic Algorithm driver.”

WWW. Available at http://cuaerospace.com/

carroll/ga.html Last Accessed 20th April 2002.

M. Mitchell, An Introduction to Genetic Algorithms.

MIT Press, 5 ed., 1999.

D. L. Carroll, Genetic Algorithms and Optimizing

Chemical Oxygen-Iodine Lasers, vol. XVIII of De-

velopments in Theoretical and Applied Mechanics,

pp. 411–424, H. Wilson, R. Batra, C. Bert, A. Davis,

R. Schaper, D. Stewart, and F. Swinson eds. Gun-

tersville, AL: The University of Alabama, 1996.

N. Srinivas and K. Deb, “Multiobjective optimiza-

tion using nondominated sorting in genetic algo-

rithms,” Evolutionary Computation, vol. 2, no. 3,

pp. 221–248, 1994

Downloads

Published

2022-07-09

How to Cite

[1]
R. I. . Macpherson and N. J. . Ryan, “Reducing Electromagnetic Coupling in Shielded Enclosures using a Genetic Algorithm -- Finite-Difference Time-Domain Solver”, ACES Journal, vol. 17, no. 3, pp. 218–224, Jul. 2022.

Issue

Section

General Submission