Applying Monte Carlo and artificial intelligence techniques for 235U mass prediction in samples with different enrichments
| Autor: | M. H. Hazzaa, Sameh El-Sayed Shaban, R. A. El-Tayebany |
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| Rok vydání: | 2019 |
| Předmět: |
Physics
Nuclear and High Energy Physics Isotope Artificial neural network business.industry Monte Carlo method chemistry.chemical_element Uranium 010403 inorganic & nuclear chemistry 01 natural sciences 030218 nuclear medicine & medical imaging 0104 chemical sciences Semiconductor detector 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine chemistry Uranium oxide Artificial intelligence business Hpge detector Instrumentation |
| Zdroj: | Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 916:322-326 |
| ISSN: | 0168-9002 |
| DOI: | 10.1016/j.nima.2018.10.008 |
| Popis: | Monte Carlo calculations and artificial intelligence prediction techniques were executed to estimate the uranium content of the isotope U-235 of uranium oxide (U3O8) standards. Five uranium oxide (U3O8 ) standards with different enrichments were used in this study. The count rate was measured experimentally using Hyper-Pure Germanium detector (HPGe) and MCNP-5 Monte Carlo transport code to estimate 235 U mass of standard nuclear materials. The acquired results were compared with declared values. Finally, Artificial Neural Network (ANN) simulation was used to get the results of count rate and U-235 mass. The results were matched with the experiment within an accuracy of less than 2%. |
| Databáze: | OpenAIRE |
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