基于蒙特卡洛法的活塞有效面积不确定度评定

Uncertainty Evaluation of Piston Gauge Effective Area Using the Monte Carlo Method

不确定度评定是量值传递工作的重要组成部分,随着JJF 1059.2-2012《用蒙特卡洛法评定测量不确定度》的发布,蒙特卡洛法也越来越多地应用于各计量专业的不确定度评定。基于python语言采用蒙特卡洛法和自适应蒙特卡洛法两种方法编写了计算代码,以100 MPa油介质活塞压力计的校准为例,对活塞有效面积校准结果的不确定度进行了评定,分析计算了各输入量不确定度及概率分布模型对输出量不确定度的影响,并与传统评定方法的结果进行了比较。结果表明:活塞有效面积校准不确定度的主要来源为标准活塞有效面积引入的不确定度,采用蒙特卡洛法的结果与传统评定结果一致,相对扩展不确定度(k=2)均为32 ppm,有效面积校准结果的概率密度分布取决于主要影响量的概率密度分布。

Uncertainty evaluation is a crucial component in the dissemination of measurement values.Following the publication of JJF 1059.2-2012"Evaluation of Measurement Uncertainty Using the Monte Carlo Method,"this approach has been increasingly applied across various metrological disciplines.Using Python,calculation codes were developed employing both the Monte Carlo method and the adaptive Monte Carlo method.Taking the calibration of a 100 MPa oilmedium piston gauge as an example,the uncertainty of the calibrated effective area was evaluated.The impact of uncertainties and probability distribution models of various input quantities on the output uncertainty was analyzed and compared with results from traditional evaluation methods.The findings indicate that the primary source of uncertainty in the calibration of the piston's effective area is the uncertainty introduced by the standard piston's effective area.Results obtained using the Monte Carlo method align with those from traditional evaluations,both yielding a relative expanded uncertainty(k=2)of 32 ppm.The probability density distribution of the calibrated effective area is determined by the probability density distribution of the main influencing factors.