Uncertainty evaluation for the quantification of low masses of benzo[a]pyrene: Comparison between the Law of Propagation of Uncertainty and the Monte Carlo method

2016 ◽  
Vol 920 ◽  
pp. 10-17 ◽  
Author(s):  
Michela Sega ◽  
Francesca Pennecchi ◽  
Sarah Rinaldi ◽  
Francesca Rolle
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Uncertainty Evaluation ◽  
Propagation Of Uncertainty ◽  
The Monte Carlo Method ◽  
The Law

scholarly journals Uncertainty Analysis of a Test Bed for Calibrating Voltage Transformers vs. Temperature

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4472 ◽  
Author(s):  
Mingotti ◽  
Peretto ◽  
Tinarelli ◽  
Ghaderi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Uncertainty Analysis ◽  
Random Effects ◽  
Evaluation Method ◽  
Uncertainty Evaluation ◽  
Test Bed ◽  
Uncertainty Sources ◽  
The Monte Carlo Method

The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup.

scholarly journals MEASUREMENT UNCERTAINTY EVALUATION OF BRINELL HARDNESS TEST: GUM AND MONTE CARLO METHOD

2018 ◽  
Vol 15 (30) ◽  
pp. 252-258
Author(s):  
L. TREVISAN ◽  
D. A. K. FABRICIO
Keyword(s):  
Mathematical Method ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Uncertainty ◽  
Brinell Hardness ◽  
Hardness Test ◽  
Mechanical Tests ◽  
Uncertainty Evaluation ◽  
The Monte Carlo Method ◽  
Measurement Uncertainty Evaluation

The Brinell hardness test is one of the most used mechanical tests in the industry to assure the quality of metallurgical processes. Based on the measured values, it is necessary to describe the measurement uncertainty values associated with the mathematical method used. Thus, measurement uncertainty values describe the reliability of the experimental results. The calculation of measurement uncertainty can be performed in several ways, and the method described by ISO/GUM is the most used by ISO/IEC 17025 accredited laboratories. The main objective of this work is to compare measurement uncertainty values based on different sources of uncertainty used in the measurement uncertainty evaluation for two Brazilian laboratories accredited by Cgcre/INMETRO. In addition, uncertainty values obtained by the GUM method and by the Monte Carlo method were compared. The results show that there is no great variation in the measurement uncertainty values as a function of the mathematical method used.

scholarly journals Getting started with uncertainty evaluation using the Monte Carlo method in R

2021 ◽  
Author(s):  
Adriaan M. H. van der Veen ◽  
Maurice G. Cox
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Uncertainty ◽  
Daily Practice ◽  
International Standards ◽  
Iso 15189 ◽  
Laboratory Staff ◽  
Uncertainty In Measurement ◽  
Propagation Of Uncertainty ◽  
The Monte Carlo Method

AbstractThe evaluation of measurement uncertainty is often perceived by laboratory staff as complex and quite distant from daily practice. Nevertheless, standards such as ISO/IEC 17025, ISO 15189 and ISO 17034 that specify requirements for laboratories to enable them to demonstrate they operate competently, and are able to generate valid results, require that measurement uncertainty is evaluated and reported. In response to this need, a European project entitled “Advancing measurement uncertainty—comprehensive examples for key international standards” started in July 2018 that aims at developing examples that contribute to a better understanding of what is required and aid in implementing such evaluations in calibration, testing and research. The principle applied in the project is “learning by example”. Past experience with guidance documents such as EA 4/02 and the Eurachem/CITAC guide on measurement uncertainty has shown that for practitioners it is often easier to rework and adapt an existing example than to try to develop something from scratch. This introductory paper describes how the Monte Carlo method of GUM (Guide to the expression of Uncertainty in Measurement) Supplement 1 can be implemented in R, an environment for mathematical and statistical computing. An implementation of the law of propagation of uncertainty is also presented in the same environment, taking advantage of the possibility of evaluating the partial derivatives numerically, so that these do not need to be derived by analytic differentiation. The implementations are shown for the computation of the molar mass of phenol from standard atomic masses and the well-known mass calibration example from EA 4/02.

scholarly journals Determination of Uncertainties for Correlated Input Quantities by the Monte Carlo Method

10.14311/1590 ◽  
2012 ◽  
Vol 52 (4) ◽  
Author(s):  
Marcel Goliaš ◽  
Rudolf Palenčár
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Direct Measurement ◽  
Correlation Coefficients ◽  
Measurement Model ◽  
The Monte Carlo Method ◽  
The Law ◽  
Output Quantity ◽  
Propagation Of Uncertainties

This paper presents the calculation of the uncertainty for distribution propagation by the Monte Carlo method for a measurement model with one output quantity. The procedure is shown on the basis of an example of the calculation of a rectangle by direct measurement of length by the same caliper. The measurements are correlated, and the uncertainties are calculated for three values of the correlation coefficients. Another part of the paper presents a validation of the law of propagation of uncertainties for distribution propagation by the Monte Carlo method.

Adaptive quasi-Monte Carlo method for uncertainty evaluation in centroid measurement of planetary rovers

2020 ◽  
pp. 014233122095003
Author(s):  
Qiang Na ◽  
Shurong Hu ◽  
Jianguo Tao ◽  
Yang Luo
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Nonlinear Models ◽  
Evaluation Process ◽  
Standard Uncertainty ◽  
Integrated System ◽  
Uncertainty Evaluation ◽  
Quasi Monte Carlo ◽  
The Monte Carlo Method ◽  
Adaptive Monte Carlo Method

The measurement of the centroid is of great significance to improve the control performance and reduce the energy consumption of the planetary rover (PR). The uncertainty is an essential indicator of the reliability of centroid measurement results. The purpose of the current study is to evaluate the uncertainty of centroid measurement in the multi-configuration rover. For the measurement of the centroid, the model with 37 parameters of two measurements as the input and the centroid coordinates as the output is derived. Further, the mechanical and electrical integrated system is developed, which can measure the centroid of PRs in different configurations and sizes. Moreover, to overcome the shortcomings of the Monte Carlo method (MCM) in uncertainty evaluation, an adaptive algorithm that automatically determines the number of input sequences is proposed. On this basis, an adaptive quasi-Monte Carlo method (AQMCM) is presented in order to accelerate the uncertainty evaluation, which is characterized by the randomized Sobol sequence. Besides, experiments are performed to compare the uncertainty evaluation process and results of the AQMCM and the adaptive Monte Carlo method (AMCM) in multiple configurations. The result shows that the standard uncertainty of the AQMCM is almost the same as that of the AMCM, but the sequence size of AQMCM is evidently smaller than that of AMCM. Taken together, we identify that the AQMCM evaluates the uncertainty of CM for the multi-configuration rover in an efficient and fast way. Furthermore, the AQMCM provides a new method for uncertainty evaluation, particularly for nonlinear models in different states.

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