Back to Search Start Over

Optimization of source pencils loading plan with genetic algorithm for gamma irradiation facility.

Authors :
Jiang, Shan
Tian, Heng
Wang, Yiwei
Jin, Long
Rong, Jian
Kang, Siqing
Cao, Li'an
Gao, Dongbin
Li, Huasheng
Liu, Juntao
Liu, Zhiyi
Source :
Radiation Physics & Chemistry. Jun2023, Vol. 207, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The arrangement pattern of source pencils on the source rack determines the quality of the dose field in a gamma irradiation facility. An optimum source loading plan can achieve a uniform dose distribution and utilize the radiation energy with high efficiency. This study proposed a methodology to optimize source loading plans using genetic algorithms. In a case study of a planar source rack which is typically used in most gamma irradiation facilities, the single objective optimization of source loading plans for minimizing dose uniformity ratio was accomplished with simple genetic algorithm, while the multi-objective one for minimizing dose uniformity ratio and maximizing average dose simultaneously was performed with NSGA-II. The loading plans gained by the genetic algorithms resulted in a 16.0% reduction in dose uniformity ratio and an increase of 5.4% in average absorbed dose compared to the probable random plan. The improvement allows the products to absorb the gamma dose more uniformly and utilizing cobalt-60's energy with higher efficiency. • A method for optimizing the loading plan with genetic algorithm is proposed for the first time. • Accomplish the process of single objective and multi-objective optimization for a planar source rack as a study case. • The results show a 16.0% reduction in dose uniformity ratio and an increase of 5.4% in average dose to the probable random plan. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0969806X
Volume :
207
Database :
Academic Search Index
Journal :
Radiation Physics & Chemistry
Publication Type :
Academic Journal
Accession number :
162391239
Full Text :
https://doi.org/10.1016/j.radphyschem.2023.110839