UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE

Author:

Danacı Erkan1ORCID,Kartal Doğan Aliye1ORCID,Çiçek Engin Can2ORCID,Çetinkaya Anıl3ORCID,Kaya Muhammed Çağrı4ORCID,Oğuztüzün M. S. Halit3ORCID,Tünay Gülsün5ORCID

Affiliation:

1. TÜBİTAK Ulusal Metroloji Enstitüsü

2. ASELSAN

3. ORTA DOĞU TEKNİK ÜNİVERSİTESİ

4. 5Chalmers University of Technology Department of Computer Science and Engineering

5. SPARK KALİBRASYON

Abstract

RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.

Publisher

Konya Muhendislik Bilimleri Dergisi

Reference15 articles.

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2. BIPM, “Evaluation of measurement data — Supplement 1 to the “Guide to the expression of uncertainty in measurement” — Propagation of distributions using a Monte Carlo method”, Bureau Int. des Poids et Measures, JCGM 101:2008, 1st ed., Sep. 2008. [Online]. Available: https://www.bipm.org/documents/20126/2071204/JCGM_101_2008_E.pdf/325dcaad-c15a-407c-1105-8b7f322d651c [Accessed: August 06, 2024].

3. P. R. G. Couto, J. Carreteiro, and S. P. de Oliveira, Monte Carlo Simulations Applied to Uncertainty in Measurement, Theory and Applications of Monte Carlo Simulations. Intech, March 06, 2013. [E-Book]. Available: https://www.intechopen.com/chapters/43533. doi: 10.5772/53014.

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5. G. M. Mahmoud, and R. S. Hegazy, “Comparison of GUM and Monte Carlo methods for the uncertainty estimation in hardness measurements”, International Journal of Metrology and Quality Engineering, vol. 8, no. 9, May 24, Article 14, 2017. https://doi.org/10.1051/ijmqe/2017014

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