针对风速、流量、探源距、测试管管长和管径等因素对核设施退役中管道内α污染测量造成的非线性影响,采用控制变量法开展长距离α测量(Long range alpha detector,LRAD)模拟装置下的多参数影响实验,初步分析了各种因素对系统测量值的影...针对风速、流量、探源距、测试管管长和管径等因素对核设施退役中管道内α污染测量造成的非线性影响,采用控制变量法开展长距离α测量(Long range alpha detector,LRAD)模拟装置下的多参数影响实验,初步分析了各种因素对系统测量值的影响特征,建立了以影响因素和测量值为输入、放射源活度为输出的BP神经网络模型,分别对948和100组数据进行了模型建立和实例检验,结果说明,预测平均相对误差为3.4218×10–4,实例平均相对误差为2.217×10–2。应用BP网络模型模拟LRAD装置下的α活度是可行且有效的。展开更多
针对多参数影响长距离α测量(Long range alpha detector,LRAD)系统准确度的问题,通过模拟现场管道,开展管道内LRAD敞开式测量关键参数特征研究试验,获得影响系统收集离子的特征。针对系统输入输出的非线性关系,用BP神经网络对多参数影...针对多参数影响长距离α测量(Long range alpha detector,LRAD)系统准确度的问题,通过模拟现场管道,开展管道内LRAD敞开式测量关键参数特征研究试验,获得影响系统收集离子的特征。针对系统输入输出的非线性关系,用BP神经网络对多参数影响下系统的输出进行网络训练与预测,预测平均百分误差在5%以内。研究表明,满足放射性测量统计涨落规律条件下,BP网络对LRAD分析结果预测有较好的准确度,基本克服了系统非线性的影响。展开更多
主要介绍了以长距离α探测技术(Long Range Alpha Detection,简称LRAD)为实验基础的关于二次灰色关联分析算法的预测,以及被测管管径、管长、测量距离、管道、风速、空气流量6大因素对实验的影响,利用C语言编程完成整个预测过程,最后根...主要介绍了以长距离α探测技术(Long Range Alpha Detection,简称LRAD)为实验基础的关于二次灰色关联分析算法的预测,以及被测管管径、管长、测量距离、管道、风速、空气流量6大因素对实验的影响,利用C语言编程完成整个预测过程,最后根据测量数据检验程序的完整性及正确性。展开更多
Long-range alpha detectors （LRADs） are attracting much attention in the decommissioning of nuclear facilities because of some problems in obtaining source positions on an interior surface during pipe decommissioning...Long-range alpha detectors （LRADs） are attracting much attention in the decommissioning of nuclear facilities because of some problems in obtaining source positions on an interior surface during pipe decommissioning. By utilizing the characteristic that LRAD detects alphas by collecting air-driving ions, this article applies a method to localize the radioactive source by ions＇ fluid property. By obtaining the ion travel time and the airspeed distribution in the pipe, the source position can be determined. Thus this method overcomes the ion＇s lack of periodic characteristics. Experimental results indicate that this method can approximately localize the source inside the pipe. The calculation results are in good agreement with the experimental results.展开更多
文摘针对风速、流量、探源距、测试管管长和管径等因素对核设施退役中管道内α污染测量造成的非线性影响,采用控制变量法开展长距离α测量(Long range alpha detector,LRAD)模拟装置下的多参数影响实验,初步分析了各种因素对系统测量值的影响特征,建立了以影响因素和测量值为输入、放射源活度为输出的BP神经网络模型,分别对948和100组数据进行了模型建立和实例检验,结果说明,预测平均相对误差为3.4218×10–4,实例平均相对误差为2.217×10–2。应用BP网络模型模拟LRAD装置下的α活度是可行且有效的。
文摘针对多参数影响长距离α测量(Long range alpha detector,LRAD)系统准确度的问题,通过模拟现场管道,开展管道内LRAD敞开式测量关键参数特征研究试验,获得影响系统收集离子的特征。针对系统输入输出的非线性关系,用BP神经网络对多参数影响下系统的输出进行网络训练与预测,预测平均百分误差在5%以内。研究表明,满足放射性测量统计涨落规律条件下,BP网络对LRAD分析结果预测有较好的准确度,基本克服了系统非线性的影响。
基金Supported by National Science Fund for Distinguished Young Scholars of China (41025015)National Natural Science Foundation of China (41274108, 41274109)Technological Innovation Research Team Foundation of Sichuan Province (2011JTD0013)
文摘Long-range alpha detectors （LRADs） are attracting much attention in the decommissioning of nuclear facilities because of some problems in obtaining source positions on an interior surface during pipe decommissioning. By utilizing the characteristic that LRAD detects alphas by collecting air-driving ions, this article applies a method to localize the radioactive source by ions＇ fluid property. By obtaining the ion travel time and the airspeed distribution in the pipe, the source position can be determined. Thus this method overcomes the ion＇s lack of periodic characteristics. Experimental results indicate that this method can approximately localize the source inside the pipe. The calculation results are in good agreement with the experimental results.
