Application of the Monte Carlo method to estimate the uncertainty of air flow measurement

Abstract

The Guide to the Expression of Uncertainty in Measurement (GUM) approves the use of both the classical approach with partial derivatives and the Monte Carlo technique. The former procedure exhibits two main limitations: Firstly, it requires some mathematical skills to compute the first-order derivatives of each component of the output quantity; secondly, it cannot predict the probability distribution of the output quantity if the input quantities are not normally distributed. The drawbacks, however, are eliminated by the latter concept, namely the Monte Carlo approach. This paper demonstrates that the Monte Carlo simulation method is fully compatible with conventional uncertainty estimation methods. The authors describe application of the Monte Carlo method for the estimation of measurement uncertainty in indirect measurement of air flow with a multiport averaging Pitot tube. The uncertainty of the flowmeter is dependent on the averaging Pitot tube (as a primary element) and on the differential pressure transmitter uncertainty. In this case, the probability distributions of the input quantities are not normal. Matlab is used for the estimation of the air flow measurement uncertainty via the Monte Carlo method.