Evaluation of Measurement Uncertainty in Multiphase Flow by Monte Carlo Simulation

2010 ◽  
Author(s):  
Tesi Isaac Marcus Arubi
Metrologia ◽  
2006 ◽  
Vol 43 (3) ◽  
pp. 306-310 ◽  
Author(s):  
J C Damasceno ◽  
R M H Borges ◽  
P R G Couto ◽  
A P Ordine ◽  
M A Getrouw ◽  
...  

2019 ◽  
Vol 55 ◽  
pp. 390-396 ◽  
Author(s):  
Yuka Miura ◽  
Shoichi Nakanishi ◽  
Eiichi Higuchi ◽  
Kiyoshi Takamasu ◽  
Makoto Abe ◽  
...  

Author(s):  
Jasveer Singh ◽  
Neha Bura ◽  
Kapil Kaushik ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Nita Dilawar Sharma

It is well established that the estimation of measurement uncertainty is vital for the validation of any measurement and is an essential parameter of quality assurance. Apart from the conventional technique of law of propagation of uncertainty (LPU), which has many limitations, Monte Carlo simulation (MCS) technique has become an essential tool for the estimation of measurement uncertainty in various fields of metrology. The most critical factor in MCS is the generation of random numbers of the input quantities according to their probability distributions. The number of Monte Carlo trials to generate these random numbers significantly affects the results. In particular, the required number of trials is also affected by the parameter for which the uncertainty is to be estimated. Hence, in the current paper, the effect of selection of the number of trials on the random number generation and the resulting output in terms of standard deviation (SD) is investigated for the uncertainty in the effective area of a pneumatic reference pressure standard (NPLI-4) at the CSIR-National Physical Laboratory of India. The simulation results thus obtained are compared amongst themselves, with an adaptive approach as well as with the experimental results. The outcomes are analyzed and discussed in detail.


2018 ◽  
Vol 101 (4) ◽  
pp. 1205-1211
Author(s):  
Saad Alaoui Sossé ◽  
Taoufiq Saffaj ◽  
Bouchaib Ihssane

Abstract Recently, a novel and effective statistical tool called the uncertainty profile has been developed with the purpose of graphically assessing the validity and estimating the measurement uncertainty of analytical procedures. One way to construct the uncertainty profile is to compute the β-content, γ-confidence tolerance interval. In this study, we propose a tolerance interval based on the combination of the generalized pivotal quantity procedure and Monte-Carlo simulation. The uncertainty profile has been applied successfully in several fields. However, in order to further confirm its universality, this newer approach has been applied to assess the performance of an alternative procedure versus a reference procedure for counting of Escherichia coli bacteria in drinking water. Hence, the aims of this research were to expose how the uncertainty profile can be powerfully applied pursuant to ISO 16140 standards in the frame of interlaboratory study and how to easily make a decision concerning the validity of the procedure. The analysis of the results shows that after the introduction of a correction factor, the alternative procedure is deemed valid over the studied range because the uncertainty limits lie within the acceptability limits set at ±−0.3 log unit/100 ml for a β = 66.7% and γ = 90%.


2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Eun-Kyung Lee ◽  
Joon-Ho Lee

The performance of the conventional beamforming for angle-of-arrival (AOA) estimation algorithm under measurement uncertainty is analyzed. Gaussian random variables are used for modeling measurement noises. Analytic expression of the mean square error (MSE) is obtained via Taylor series expansion. In traditional performance analysis, estimation accuracy in terms of the MSEs is usually obtained from the Monte Carlo simulation, which is computationally intensive especially for large number of repetitions in the Monte Carlo simulation. For reliable MSE in the Monte Carlo simulation, the number of repetitions should be very large. To circumvent this problem, analytic performance analysis which is less computationally intensive than the Monte Carlo simulation-based performance analysis is proposed in this paper. After some approximations, we derive the closed form expression of the mean square error (MSE) for each incident signal. The validity of the derived expressions is shown by comparing an analytic MSE with an empirical MSEs. The Cramer–Rao bound is also used to further validate the derived analytic expression.


Sign in / Sign up

Export Citation Format

Share Document