Estimation of hygrometry uncertainties by propagation of distributions

Carpentier, Vincent; Megharfi, M.; Quint, Jacques R.; Priel, Marc; Désenfant, Michèle; Morice, R.

doi:10.1088/0026-1394/41/6/011

Cited by 6 publications

(4 citation statements)

References 1 publication

(2 reference statements)

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“…In line with the results reported by other researchers, we observe that the value of u(t dew/frost ) is very close to the value of u(t s ) [3,4,6].…”

Section: Resultssupporting

confidence: 93%

“…In Sect. 3, two alternative methods for the estimation of the humidity uncertainty are developed, on one hand, the propagation of the uncertainties [2][3][4], and on the other hand, the propagation of the distributions [5,6]. Both methods are evaluated for different values of relative humidity and different values of dew/frostpoint temperatures, and the analysis of the uncertainties of input variables is shown.…”

Section: List Of Symbolsmentioning

confidence: 99%

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Uncertainty Estimations for Standard Humidity Generator of INTI

Skabar

2015

Int J Thermophys

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Two methods for the uncertainty estimation in the dew/frost-point temperature and relative humidity are shown. The uncertainty estimation was performed propagating the uncertainties of pressure and temperature measurements in the corresponding equations in the classic method, and propagating the distributions of the input variables, pressure and temperature, (Monte Carlo simulation) in an alternative method. The results obtained by the different methods are compared for analyzing the validity of the uncertainty calculations, and the assumption of negligible correlations between the input variables. The agreement between the presented uncertainty analysis and the outcomes of the comparison with INMETRO is discussed. List of symbolsrh Relative humidity t dew Dew-point temperature t frost Frost-point temperature e w (t) Vapor pressure f (t, P) Enhancement factor P s Total pressure in saturator P c Total pressure in test chamber t s Temperature in saturator t c Temperature in test chamber 123 Int J Thermophys u(rh) Standard uncertainty in relative humidity u(t dew ) Standard uncertainty in dew-point temperature u(t frost ) Standard uncertainty in frost-point temperature u(e w ) Standard uncertainty in vapor pressure equation u( f ) Standard uncertainty in enhancement factor equation u(P s ) Standard uncertainty of saturation pressure measurement u(P c )Standard uncertainty of chamber pressure measurement u(t s ) Standard uncertainty of saturation temperature measurement u(t c )Standard uncertainty of chamber temperature measurement

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“…In line with the results reported by other researchers, we observe that the value of u(t dew/frost ) is very close to the value of u(t s ) [3,4,6].…”

Section: Resultssupporting

confidence: 93%

Section: List Of Symbolsmentioning

confidence: 99%

Uncertainty Estimations for Standard Humidity Generator of INTI

Skabar

2015

Int J Thermophys

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“…2. In an earlier work it was also found that the expanded uncertainties found by MCM and GUM agree well [15].…”

Section: Resultsmentioning

confidence: 52%

“…There have been reports of using MCM for uncertainty calculation [4,5,[14][15][16][17][18] many of them emphasizing its advantages over the GUM uncertainty framework. There has, however also been a report on an opposite finding [19].…”

Section: Introductionmentioning

confidence: 99%

Can coverage factor 2 be interpreted as an equivalent to 95% coverage level in uncertainty estimation? Two case studies

et al. 2010

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Measurement Uncertainty of Dew-Point Temperature in a Two-Pressure Humidity Generator

Martins

Ribeiro

Sousa³

et al. 2011

Int J Thermophys

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This article describes the measurement uncertainty evaluation of the dewpoint temperature when using a two-pressure humidity generator as a reference standard. The estimation of the dew-point temperature involves the solution of a non-linear equation for which iterative solution techniques, such as the Newton-Raphson method, are required. Previous studies have already been carried out using the GUM method and the Monte Carlo method but have not discussed the impact of the approximate numerical method used to provide the temperature estimation. One of the aims of this article is to take this approximation into account. Following the guidelines presented in the GUM Supplement 1, two alternative approaches can be developed: the forward measurement uncertainty propagation by the Monte Carlo method when using the Newton-Raphson numerical procedure; and the inverse measurement uncertainty propagation by Bayesian inference, based on prior available information regarding the usual dispersion of values obtained by the calibration process. The measurement uncertainties obtained using these two methods can be compared with previous results. Other relevant issues concerning this research are the broad application to measurements that require hygrometric conditions obtained from two-pressure humidity generators and, also, the ability to provide a solution that can be applied to similar iterative models. The research also studied the factors influencing both the use of the Monte Carlo method (such as the seed value and the convergence parameter) and the inverse

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Estimation of hygrometry uncertainties by propagation of distributions

Cited by 6 publications

References 1 publication

Uncertainty Estimations for Standard Humidity Generator of INTI

Uncertainty Estimations for Standard Humidity Generator of INTI

Can coverage factor 2 be interpreted as an equivalent to 95% coverage level in uncertainty estimation? Two case studies

Measurement Uncertainty of Dew-Point Temperature in a Two-Pressure Humidity Generator

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