Investigation of contribution of number of trials in Monte Carlo simulation for uncertainty estimation for a pressure balance

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.