Radioactive Source-Detector System: Design and Monte Carlo Opinion
DOI:
https://doi.org/10.21123/bsj.2024.8822Keywords:
Central limit theorem, Large numbers law, Monte Carlo simulation, NaI(Tl) detector efficiency, Radiation counting statistics.Abstract
In the current research, a computer simulation program was designed and written according to the Monte Carlo method to serve as a virtual practical system instead of a real one. The program has been statistically, geometrically and numerically tested for virtual radioactive source-detector setup. The simulation program is carried out for NaI(Tl) detector, and once for Gieger-Muller counter, for a range of energy up to 10 MeV. The Law of Large Numbers and the Central Limit Theorem were used to test the accuracy and precision of the program’s workflow and an indication of how the results are close to their averages and, statistically, how they tend to a normal distribution. Generally, results of a number of detector efficiency types showed a high agreement with published experimental and several global codes results within a percentage error of ~ 0.02-5% (i.e. the accuracy ~ 95-99.98%) and the significance level reflects the precise of the algorithm of simulation. The accurate and precise estimation of the current simulation gives it the desired reliability. The current simulation program also showed flexibility and effectiveness in designing any nuclear source-detector system and providing the relevant workers or experimenters with indicators that help in the optimal design of a system in terms of equipment and geometrical configuration with the least time. It may take a few seconds to a few minutes of execution time for a personal computer with normal specifications. Unlike laboratory experiments which may take from several minutes to several hours. In addition, it provides an ideal work environment that is completely free of radiation hazards. Also, the current simulation provides a deep understanding of the interactions that occur in a real physical practical system.
Received 30/03/2023,
Revised 23/10/2023,
Accepted 25/10/2023,
Published Online First 20/03/2024
References
Fielding AL. Monte Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte Carlo codes. J Radiother Pract. 2023; 22(e80): 1–6. https://doi.org/10.1017/S1460396923000079
Shapiro A. Monte Carlo Sampling Approach to Stochastic Programming. ESAIM: Proceedings, December 2003; 13: 65-73. https://doi.org/10.1051/proc:2003003
Gobet E. Monte Carlo Methods and Stochastic Processes From Linear to Non Linear. CRC Press, Taylor & Francis Group, LLC, 2016. https://doi.org/10.1080/14697688.2024.2310568
Paxton P, Curran PJ, Bollen KA, Kirby J, Chen F. Monte Carlo Experiments: Design and Implementation. Struct Equ Modeling: A Multidiscip J. 2001; 8(2): 287-312. https://doi.org/10.1207/S15328007SEM0802_7
Hameed BS, Kaddoori FF, Fzaa WT. Concentrations and Radiation Hazard Indices of Naturally Radioactive Materials for Flour Samples in Baghdad Markets. Baghdad Sci J. 2021; 18(3): 649-654. https://doi.org/10.21123/bsj.2021.18.3.0649
Kim J, Park K, Hwang J, Kim H, Kim J, Kim H, et al. Efficient design of a ∅2×2 inch NaI(Tl) scintillation detector coupled with a SiPM in an aquatic environment. Nucl Eng Technol. 2019; 51: 1091-1097. https://doi.org/10.1016/j.net.2019.01.017
Pepperosa A, Remetti R, Perondi F. ARMAX Forecast Model for Estimating the Annual Radon Activity Concentration in Confined Environment by Short Measurements Performed by Active Detectors. Int J Environ. Res. Public Health. 2022; (19): 5229. https://doi.org/10.3390/ijerph19095229
Zambonino SS, Barahona T, Roque S. Simulation of a HPGe Detector with GEANT4. Rev Politéc.. 2023; 50(2): 7-14. https://doi.org/10.33333/rp.vol50n2.01
Akkurt İ, Waheed F, Akyildirim H, Gunoglu K. Performance of NaI(Tl) detector for gamma-ray spectroscopy. Ind J Phys.. 2022; 96: 2941–2947. https://doi.org/10.1007/s12648-021-02210-1
Demir N, Kuluöztürk ZN. Determination of energy resolution for a NaI(Tl) detector modeled with FLUKA code. Nucl Eng Technol. 2021; 53: 3759-3763. https://doi.org/10.1016/j.net.2021.05.017
Pilakouta M, Pappa FK, Patiris DL, Tsabaris C, Kalfas CA. A methodology for expanding the use of NaI(Tl) based spectrometry in environmental radioactivity measurements. Appl. Radiat. Isot.. 2018; 139: 159-168. https://doi.org/10.1016/j.apradiso.2018.04.032
DePaiva JDS, Sousa EE, DeFarias EEG, DoCarmo AM, Filho CAS, De Franc EJ. Applied tools for determining low-activity radionuclides in large environmental samples. J Radioanal Nucl Chem. 2015; 306(3): 631-636. https://doi.org/10.1007/s10967-015-4219-x
Hinrichsen Y, Finck R, Martinsson J, Rääf C. Monte‑carlo simulations of external dose contributions from the surrounding ground areas of residential homes in a typical northern european suburban area after a radioactive fallout scenario. Sci Rep. 2020; 10: 14764. https://doi.org/10.1038/s41598-020-71446-4
Baldoncini M, Albéri M, Bottardi C, Chiarelli E, Raptis KGC, Strati V, et al. Investigating the potentialities of Monte Carlo simulation for assessing soil water content via proximal gamma-ray spectroscopy. J Environ Radioact. 2018; 192: 105-116. https://doi.org/10.1016/j.jenvrad.2018.06.001
Külahci F, Aközcan S, Günay O. Monte Carlo simulations and forecasting of Radium 226, Thorium 232, and Potassium 40 radioactivity concen-trations. J Radioanal Nucl Chem.. 2020; 324(1): 55-70. https://doi.org/10.1007/s10967-020-07059-y
Kyriakou I, Sakata D, Tran HN, Perrot Y, Shin WG, Lampe N, et al. Review of the Geant4-DNA Simulation Toolkit for Radiobiological Applications at the Cellular and DNA Level. Cancers. 2022; 14: 35. https://doi.org/10.3390/cancers14010035
Gao Y, Jiaming L, Jichen L, Liu L. Simulation of the cement measurement based on the pulse DT neutron generator: A Monte Carlo study. PLoS One, 2021. https://doi.org/10.1371/journal.pone.0252078
Niu F. Application of position sensitive detector in nuclear well logging tools. PhD. Thesis. Simon Fraser University, Canada. 2020.
Sarkar R, Roy A, Chakrabarti S. Simulation of Cosmic Rays in the Earth’s Atmosphere and Interpretation of Observed Counts in an X-ray Detector at Balloon Altitude Near Tropical Region. Adv Space Res.. 2020; 1: 189-197. https://doi.org/10.1016/j.asr.2019.09.046
Churchill CW. Radiative transfer. New Mexico State University. Chap 4: 2009. https://doi.org/10.1007/978-3-030-95247-1
Bielajew AF. Fundamentals of the Monte Carlo method for neutral and charged particle transport. The University of Michigan. 2020.
Ljungberg M, Strand SE, King MA. Monte Carlo Calculations in Nuclear Medicine: Applications in Diagnostic Imaging. IOP Publishing Ltd 1998 https://doi.org/10.1201/b13073
Berger MJ, Hubbell JM. XCOM Program V.3.1: Photon Cross Sections Database. NIST standard reference database. 1999. https://dx.doi.org/10.18434/T48G6X
Rubinstein RY, Kroese DP. Simulation and the Monte Carlo Method. 3rd ed., John Wiley & Sons. Inc., 2017. https://doi.org/10.1002/9781118631980
Tam HD, Thanh TT, Tao CV. Evaluation of the total and intrinsic efficiencies of a 3 in 3 in NaI(Tl) crystal by using the hybrid Monte Carlo method. J Sci Technol Dev.. 2013; 16(4): 26-34. https://doi.org/10.32508/stdj.v16i4.1593
Tarım UA, Gürler1 O, Yalçın S. A Quick Method to Calculate NaI(Tl) Detector Efficiency Depending on Gamma ray Energy and Source-to-detector Distance. CBUJOS. 2018; 14(2): 195-199. https://doi.org/10.18466/cbayarfbe.396704
Ogundare FO, Oniya EO, Balogun FA. Dependence of NaI(Tl) detector intrinsic efficiency on source-detector distance, energy and off-axis distance:Their implications for radioactivity measurement. Pramana. 2008; 70: 863-874. https://doi.org/10.1007/s12043-008-0095-z
Jehouani A., Ichaoui R., Boulkheir M. Study of the NaI(Tl) efficiency by Monte Carlo method. Appl. Radiat Isot. 2000; 53(4-5): 887-891. https://doi.org/10.1016/S0969-8043(00)00254-2
Yavuzkanat N, Güngür D, Yalçın S. The Determination of the Total Efficiency for NaI(Tl) Detector by GATE Simulation. BEU J Sci. Technol. 2019; 8 (Special Issue): 37-45.https://doi.org/10.17798/bitlisfen.649129
Hu J, Liu F, Ouyang X. Monte Carlo Simulated the Detection Efficiency of Different H/R Ratio of NaI:TI Crystal. Appl Mech Mater.. 2014; 529: 391-394. https://doi.org/10.4028/www.scientific.net/AMM.529.391
Downloads
Issue
Section
License
Copyright (c) 2024 Zainab kareem Ali, Ali N. Mohammed
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
