一种基于数据筛选的导热系数计算方法

导热系数测量过程中实验数据的准确性是导热系数计算的关键。实验数据不准确，直接影响导热系数的精度。现有的导热系数测量计算中对数据是否准确没有很好的评判准则或方法。鉴于此，本文提出了一种以斜率相对标准差为依据筛选数据的导热系数计算新方法。该方法以斜率相对标准差为准则判断并筛选实验数据，利用最小二乘法拟合筛选后的数据求解导热系数。将该方法应用于乙二醇标定溶液的导热系数计算中，同传统算法相比，新方法数据处理简单、计算导热系数精度提高明显、能够广泛地应用到导热系数测量实验中，测量结果明显优于传统算法。

A Method to Calculate Heat Conductivity Coefficient Based on Data Sieving

The accuracy of the experimental data is the key to the measurement of thermal conductivity. The accuracy of thermal conductivity is directly affected by the inaccurate experimental data. There is no good criterion or method for judging the accuracy of the data in the existing thermal conductivity measurement and calculation. In view of this, a new method for calculating thermal conductivity based on slope relative standard deviation is presented in this paper. In this method, the slope relative standard deviation is used as the criterion to judge and select the experimental data, and the least square method is used to fit the selected data to solve the thermal conductivity. This method is applied to the calculation of thermal conductivity of ethylene glycol calibration solution. Compared with the traditional algorithm, the new method is more effective for experimental data processing. and it can be widely applied in the measurement experiment of thermal conductivity. The result of the new method is better than that of traditional algorithm.