Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two phase flow
发布时间：2025-04-30
点击次数：
- 发布时间：
- 2025-04-30
- 论文名称：
- Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two phase flow
- 发表刊物：
- MEASUREMENT SCIENCE AND TECHNOLOGY
- 摘要：
- 摘要
Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability Pc and mutation probability Pm are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.
关键词
compressed sensing , CT reconstruction , two-phase flow , genetic algorithm
Mingfei Yan1, Huasi Hu1,3 , Yoshie Otake1,2, Atsushi Taketani2,
Yasuo Wakabayashi2, Shinzo Yanagimachi2, Sheng Wang1,2, Ziheng Pan1
and Guang Hu1
1 School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049,
People’s Republic of China 2 RIKEN, Wako, Saitama, 351-0198, Japan
E-mail: huasi_hu@mail.xjtu.edu.cn
Received 18 October 2017, revised 5 February 2018
Accepted for publication 12 February 2018
Published 9 April 2018
DOI: 10.1088/1361-6501/aaaea4
- 合写作者：
- Yan, Mingfei , Hu, Huasi , Otake, Yoshie , Taketani, Atsushi , Wakabayashi, Yasu
- 卷号：
- 29(5)
- 页面范围：
- 055404-1-14
- 是否译文：
- 否
- 发表时间：
- 2018-04-09