The Monte Carlo Method for evaluating measurement uncertainty: Application for determining the properties of materials

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• 作者：H.B. Motra^a ; ^{hbmotra@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; J. Hildebrand^b ; F. Wuttke^a
• 关键词：Measurement uncertainty ; Metrology ; Model quality ; Monte Carlo Method ; Quality ; Reliability
• 刊名：Probabilistic Engineering Mechanics
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：45
• 期：Complete
• 页码：220-228
• 全文大小：1198 K

文摘

Many statements about the quality of a numerical model can only be made by including the appropriate experiments (e.g. the quantification of the statistical uncertainties of model input parameters while calibrating the confidence level estimator model, which is heavily dependent on the definition of the experiment and the quality of its implementation). Focus is thus placed on developing methodology for quantitatively assessing the quality of the results of experiments and their exemplary implementation.

This paper presents a qualitative evaluation method for experimental results. The probabilistic approach in particular can provide substantial information during the evaluation. Therefore, methodology for predicting the uncertainty in the qualitative evaluation of experimental models. An appropriate way to propagate probability density functions through an experimental model is based on the Monte Carlo Method (MCM). The probability distributions are obtained by applying the MCM coupled with appropriate definitions for the total measurement uncertainty. This paper elaborates on the computational aspects of calculating measurement uncertainty of experimental models. The MCM has a higher conversion rate, generates narrower intervals, and produces more stable (evaluation) results. This method should reduce the analytical effort required for complicated or nonlinear models, especially because partial derivatives of the first or higher order (used in providing sensitivity coefficients for the law of propagation of uncertainty) are required. This method thus provides a mathematical and a computational tool for quantifying the uncertainty of models. Moreover, it can be used to improve measurements in order to promote quality and capacity with respect to decision-making.