RF ANTENNA DESIGN FOR BUTTON-TYPE BEAM POSITION MONITORS USING BIO-INSPIRED OPTIMIZATION METHODS

Abstract

Accelerator based facilities are in a leading position for crafting many scientific and technical innovations for a wide range of application from aviation to medicine. Beam Position Monitors (BPMs) are critical diagnostics tools for such facilities. This study presents bio-inspired methods known as Particle Swarm Optimization and Evolutionary Algorithms in order to design RF antennas for button-type BPMs. Our results show that the antenna parameters obtained using this multiple objective approaches present suitable SNR and linearity values for signal processing. It is found that using an antenna radius of 5.5 mm and beam-pipe radius of 17.5 mm, we can obtain SNR values around 40 dB which can be electronically processed.

Keywords

• Multiple objective evolutionary algorithms,, RF Antenna Design,Beam position monitors

References

1. Shafer, Robert E., Beam position monitoring, AIP conference proceedings, (1992), 601–636.
2. Smith, Stephen R., Beam position monitor engineering, AIP conference proceedings, (1997), 50–65.
3. Forck, P., Liakin, D. and Kowina, P., Beam position monitors, CERN Accelerator School on Beam Diagnostics, (2009), 187–228.
4. Nosych, A., Iriso, U. and Olle, J., Electrostatic finite-element code to study geometrical nonlinear effects of BPMs in 2D, 4th International Beam Instrumentation Conference (IBIC2015), Melbourne, Australia, (2015) 418-422
5. Yiğit, S. S., Ar, Y. and Bostanci, G. E., Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems, Turkish Journal of Electrical Engineering & Computer Sciences, (2019), 27.3, 2121-2136.
6. Karim, A. M., Güzel, M. S., Tolun, M. R., Kaya, H. and Çelebi, F. V., A new generalized deep learning framework combining sparse auto encoder and Taguchi method for novel data classification and processing, Mathematical Problems in Engineering, (2018).
7. Mohammed, S. N., Guzel, M. S. and Bostanci, E., Classification and Success Investigation of Biomedical Data Sets Using Supervised Machine Learning Models, In 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), IEEE, (2019), 1-5.
8. Aksoy, A., Karsli, O., Aydin, A., Kaya, C., Ketenoglu, B., Ketenoglu, D. and Yavas, O., Current status of Turkish accelerator and radiation laboratory in Ankara: the TARLA facility, Canadian Journal of Physics, 96(7), (2018), 837-842.

9. Aydin, A., Bostanci, E. and Tanriover, Ozgur O., A Multiple Objective Evolutionary Algorithm Approach to Find Optimal Design Parameters for Beam Position Monitoring Systems, International Journal of Modern Physics C, (2019) doi: 10.1142/ S0129183120500382
10. Aydin, A. and Kasap, E., Design studies for the beam position monitor (BPM) front-end electronics of the Turkish accelerator and radiation laboratory in Ankara (TARLA). Turkish Journal of Physics, 41(3), (2017), 269-276.
11. Eberhart, R. C. and Shi, Y., Comparison between genetic algorithms and particle swarm optimization, International conference on evolutionary programming, (1998), 611–616.
12. Nebro, A. J., Durillo, J. J., Garcia-Nieto, J., Coello, C.A., Luna, F. and Alba, E., SMPSO: A new PSO-based metaheuristic for multi-objective optimization, IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), (2009), 66–73.
13. Kollat, J. B. and Reed, P. M., Comparison of multi-objective evolutionary algorithms for long-term monitoring design, World Water and Environmental Resources Congress, (2005).
14. Ketenoğlu, D., Bostanci, G. E., Aydin, A. and Ketenoğlu, B., A hard X-ray self-amplified spontaneous emission free-electron laser optimization using evolutionary algorithms for dedicated user applications, Turkish Journal of Physics, 43(6), (2019), 551-555.