Analysis of Plane-Parallel Electron Beam Propagation in Different Media by Numerical Simulation Methods
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Simulation by the Monte Carlo method is widely used to calculate the character of ionizing radiation interaction with substance. A wide variety of programs based on the given method allows users to choose the most suitable package for solving computational problems. In turn, it is important to know exactly restrictions of numerical systems to avoid gross errors. Results of estimation of the feasibility of application of the program PCLab (Computer Laboratory, version 9.9) for numerical simulation of the electron energy distribution absorbed in beryllium, aluminum, gold, and water for industrial, research, and clinical beams are presented. The data obtained using programs ITS and Geant4 being the most popular software packages for solving the given problems and the program PCLab are presented in the graphic form. A comparison and an analysis of the results obtained demonstrate the feasibility of application of the program PCLab for simulation of the absorbed energy distribution and dose of electrons in various materials for energies in the range 1–20 MeV.
Keywords
electron beam numerical simulation Monte Carlo method absorbed energy distribution ITS Geant4 PCLabPreview
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References
- 1.J. Allison et al., Nucl. Instrum. Methods Phys. Res. A, 835, 186–225 (2016).ADSCrossRefGoogle Scholar
- 2.X. Rong, Y. Du, M. Ljungberg, et al., Med. Phys., 39, No. 5, 2346–2358 (2012).CrossRefGoogle Scholar
- 3.V. A. Astapenko, V. V. Berezovskii, P. L. Men’shikov, and I. L. Men’shikov, Russ. Phys. J., 53, No. 4, 389–395 (2010).CrossRefGoogle Scholar
- 4.T. Tabata, P. Andreo, and R. Ito, Atomic Data and Nuclear Data Tables, 56, No. 1, 105–131 (1994).ADSCrossRefGoogle Scholar
- 5.B. Yu. Bogdanovich, A. V. Nesterovich, L. A. Sukhanova, and Yu. A. Khlestkov, Russ. Phys. J., 57, No. 4, 474–481 (2014).CrossRefGoogle Scholar
- 6.A. W. Chao, K. H. Mess, M. Tigner, and F. Zimmermann, eds., Handbook of Accelerator Physics and Engineering, World Scientific Publ. Co., New York (2013).Google Scholar
- 7.L. I. Musabaeva, V. A. Lisin, and V. V. Velikaya, Radiats. Biol. Radioekol., 54, No. 5, 474–478 (2014).Google Scholar
- 8.H. Jafari, H. Chopan, and R. Taleei, World Congress on Medical Physics and Biomedical Engineering, Munich (2009), pp. 883–886.Google Scholar
- 9.V. I. Bespalov, Izv. Vyssh. Uchebn. Zaved. Fiz., Suppl., 43, No. 4, 159–165 (2000).Google Scholar
- 10.S. G. Stuchebrov, I. A. Miloichikova, and A. A. Krasnykh, J. Phys.: Conf. Ser. IOP Publishing, 732, No. 1, 012033 (2016).Google Scholar
- 11.L. Storm and H. I. Israel, Atomic Data and Nuclear Data Tables, 7, No. 6, 565–681 (1970).ADSCrossRefGoogle Scholar
- 12.The Geant4 Collaboration, Physics Reference Manual, Vol. 9 (2005).Google Scholar
- 13.H. Yoriyaz et al., Med. Phys., 36, No. 11, 5198–5213 (2009).CrossRefGoogle Scholar
- 14.J. A. Halbleib, R. P. Kensek, T. A. Mehlhorn, et al., ITS version 3.0: The Integrated TIGER Series of Coupled Electron/Photon Monte Carlo Transport Codes, Sandia National Laboratories No. SAND91-1634 (1992).Google Scholar
- 15.International Commission on Radiation Units and Measurements ICRU, Stopping powers for electrons and positrons, Report 37 (1984).Google Scholar