Accurate prediction of gaseous isotopes Xe and Kr concentrations from the burnup of nuclear fuel using simple regression algorithm.
PLoS One
; 18(7): e0288329, 2023.
Article
en En
| MEDLINE
| ID: mdl-37440512
ABSTRACT
Mathematical techniques for modeling and simulating dangerous or complex systems, such as nuclear technology systems, often require high-performance computing to process and analyze available data. In this paper, simple and quick method to support studies and research related to nuclear fuel is presented. This reasonably simple method helps to predict different concentrations of actinides and fission products in nuclear fuels without the need for expensive specialized programs and highly-trained researchers. The great importance of this approach is the speed of predicting the components of nuclear fuel concentrations, which in turn leads to quick decision-making, such as the possibility of operating fuel at higher burnup values, predicting the amount of gases resulting from nuclear fission (which may accumulate and cause problems in nuclear fuel such as volume swells), and other important decisions in nuclear fuel technology. The predicted equations have been generalized for higher values of burnup and compared with comparable results from MCNP codes. The equations deduced in calculating the different concentrations of xenon and krypton isotopes resulting from fission in burnup of nuclear fuel showed very precise results with discrepancies (magnitude of an error between the data points and the corresponding predicted ones) less than 2%. The suggested method offers a great advantage for researchers, which are the use one of any simple or common computational programs available to most researchers and do not need much experience to deal with, such as MATLAB, Excel that are easy to use for regression analyses. In this paper, the advantages of the proposed method are explained along with the limitations of its use.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Gases
/
Isótopos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2023
Tipo del documento:
Article