MCM Alchimia Methods:

solved examples on computer-aided uncertainty quantification

Authors

DOI:

https://doi.org/10.26461/22.05

Keywords:

MCM Alchimia, GUM, JCGM 100 examples, Monte Carlo

Abstract

MCM Alchimia is a free and multilingual desktop application, which runs on Windows. It is available to download from the Internet. This application implements the processes indicated in the Guide to the Expression of Uncertainty in Measurement (GUM) and the Supplement 1 of this document, easily obtaining measurement results and associated expanded uncertainty, with a detailed uncertainty budget according to GUM, and a summary of statistical parameters of the simulated sample obtained by Monte Carlo Method (MCM). This work establishes an intuitive and rapid guide for estimating measurement uncertainties by GUM and MCM methods with the software MCM Alchimia, through discussion of five examples from document JCGM 100: 2008 and three from JCGM 101: 2008. Some features and algorithms of the software are explained in detail. Particularly, functions and tools that are not available in other similar software applications, for example, the estimation of uncertainties in test models that involve the use of least-square fittings. In addition, more intuitive approaches to some problem than those suggested in the JCGM guides are shown, discussing different features available in the software to perform an easy data treatment of complex measurement models.

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References

Alchimia Project, s.d. MCM Alchimia, MCM & GUM uncertainty estimation engine [On line]. Version 5. [s.l.]: Alchimia Project. [Accessed: June 15, 2020] Available at: http://www.mcm-alchimia.com

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BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, 2008b. JCGM 101:2008 Evaluación of measurement data – Supplement 1 to the Guide to the expression of uncertainty in measurement – Propagation of distributions using a Monte Carlo method. [s.l.]: JCGM

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Published

2021-07-30

How to Cite

Constantino, L. P. (2021). MCM Alchimia Methods:: solved examples on computer-aided uncertainty quantification. INNOTEC, (22 jul-dic), e547. https://doi.org/10.26461/22.05

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Technical notes