Ye K X, Zhang T, Wang Y M, Wen F, Wu M F, Huang J, Li G S, Geng K N, Zhou Z, Zhong F B, Liu Y K, Xiang H M, Zhang S B
Institute of Plasma Physics, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, People's Republic of China.
Advanced Energy Research Center, Shenzhen University, Shenzhen 518060, People's Republic of China.
Rev Sci Instrum. 2021 Apr 1;92(4):043521. doi: 10.1063/5.0035962.
Microwave reflectometry diagnostics have been widely used to measure density profiles in fusion plasma. However, the high sensitivity of the diagnostics to plasma turbulence often results in large radial deviations in the edge density profile and causes difficulty in profile evaluation. To improve the performance of profile evaluation, a modified RANdom SAmple Consensus (RANSAC) method has been applied to fit the density profiles measured by reflectometry on the experimental advanced superconducting tokamak. Compared with the traditional least-squares method, the modified RANSAC method is much more efficient and robust in fitting the experimental profiles. Furthermore, a combination of RANSAC and a genetic algorithm (GA-RANSAC) is used to further optimize the profile evaluation procedure. The results show that this GA-RANSAC method yields better performance and stabler convergence than the modified RANSAC alone.
微波反射测量诊断技术已被广泛用于测量聚变等离子体中的密度分布。然而，该诊断技术对等离子体湍流的高灵敏度常常导致边缘密度分布出现较大的径向偏差，并给分布评估带来困难。为了提高分布评估的性能，一种改进的随机抽样一致性（RANSAC）方法已被应用于拟合在实验先进超导托卡马克上通过反射测量法测得的密度分布。与传统的最小二乘法相比，改进的RANSAC方法在拟合实验分布时效率更高且更稳健。此外，将RANSAC与遗传算法相结合（GA-RANSAC）用于进一步优化分布评估过程。结果表明，这种GA-RANSAC方法比单独使用改进的RANSAC方法具有更好的性能和更稳定的收敛性。
