| 引用本文: | 高飞,董璐琪,张昕宇,刘蕴韬,刘佳瑞,王菲菲.采用机器学习的X、γ辐射剂量仪现场校准技术[J].哈尔滨工业大学学报,2024,56(12):49.DOI:10.11918/202308041 |
| GAO Fei,DONG Luqi,ZHANG Xinyu,LIU Yuntao,LIU Jiarui,WANG Feifei.Study of on-site calibration technique of X/gamma radiation dosimeter based on machine learning algorithm[J].Journal of Harbin Institute of Technology,2024,56(12):49.DOI:10.11918/202308041 |
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| 摘要: |
| 为解决固定式X、γ辐射剂量仪现场校准的散射和剂量率定值难题,保障核设施安全稳定运行,基于X光机研究建立了便携式X射线照射装置和自屏蔽式X射线照射装置。基于蒙特卡罗模拟及机器学习算法和实验方法完成了X射线辐射场散射辐射修正并开展辐射场剂量率定值技术研究。首先,使用MCNP对便携式X射线参考辐射场及自屏蔽式X射线参考辐射场的均匀性、散射辐射和能谱分布等辐射特性进行了模拟,将得到的结果与实验数据进行对比,验证了数值模拟方法的有效性以及该装置应用于现场校准的可行性；其次,基于机器学习算法搭建现场校准环境散射辐射修正系统,使用平均绝对值误差对机器学习模型效果进行评价；最后,结合照射装置及次级电离室利用搭建的环境散射辐射修正系统开展现场校准实验。研究结果表明,便携式X射线照射装置及自屏蔽X射线照射装置均能提供满足GB/T 12162.1—2000标准要求的参考辐射场,基于环境散射辐射修正系统开展的固定式X、γ辐射剂量仪现场校准的校准因子和实验室校准因子的相对误差不大于6.2%,满足现场校准工作要求。 |
| 关键词: 机器学习 散射辐射 蒙特卡罗 X射线照射装置 现场校准 |
| DOI：10.11918/202308041 |
| 分类号:TL72 |
| 文献标识码:A |
| 基金项目:国家重点研发计划项目(2022YFF0607300) |
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| Study of on-site calibration technique of X/gamma radiation dosimeter based on machine learning algorithm |
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GAO Fei,DONG Luqi,ZHANG Xinyu,LIU Yuntao,LIU Jiarui,WANG Feifei
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(China National Nuclear Corporation Key Laboratory of Metrology and Measurement Technology(China Institute of Atomic Energy), Beijing 102413, China)
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| Abstract: |
| To solve the problem of scattering and dose-rate setting for on-site calibration of fixed X, gamma radiation dosimeter, and to ensure the safe and stable operation of nuclear facilities, a portable X-ray irradiation device and self-shielded X-ray irradiation device were established based on X-ray machine. Based on Monte Carlo simulation, machine learning algorithms and experimental methods, the scattering radiation correction of X-ray radiation field was completed and the radiation field dose rate setting technology was studied. Firstly, MCNP was used to simulate the radiation characteristics of portable X-ray reference radiation field and self-shielded X-ray reference radiation field, such as uniformity, scattered radiation and energy spectrum distribution. The obtained results were compared with experimental data to validate the effectiveness of the numerical simulation method and the feasibility of applying the device to field calibration. Secondly, an ambient scattered radiation correction system for on-site calibration was built based on machine learning algorithm, and the mean absolute error (MAE) was used to evaluate the performance of the machine learning model. Finally, on-site calibration experiments were carried out by combining the irradiation device and secondary ionization chamber with the ambient scattered radiation correction system built. The results show that both the portable X-ray irradiation device and self-shielded X-ray irradiation device can provide reference radiation field meeting the requirements of GB/T 12162.1―2000. The relative error of calibration factor and laboratory calibration factor of fixed X, gamma radiation dosimeter calibration based on environmental scattered radiation correction system is less than 6.2%, meeting the requirements of on-site calibration work. |
| Key words: machine learning scattering radiation Monte Carlo X-ray irradiation device on-site calibration |
