ORIGINAL PAPER Uncertainty Analysis of Distortion Coefficient of Piston Gauge Using Monte Carlo Method J. Singh 1,2 *, L. A. Kumaraswamidhas 2 , K. Kaushik 3 , N. Bura 1,4 and N. D. Sharma 1 1 Pressure, Vacuum and Ultrasonic Metrology Section, CSIR-National Physical Laboratory, New Delhi 110012, India 2 Department of Mining Machinery Engineering, Indian Institute of Technology (ISM), Dhanbad, India 3 Himachal Futuristic Communications Ltd., Gurugram, Haryana, India 4 Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India Received: 11 December 2018 / Accepted: 07 February 2019 Ó Metrology Society of India 2019 Abstract: Uncertainty quantification is the integral part of any measurement and metrological activity. Apart from the conventional method of uncertainty estimations, Monte Carlo method is increasingly being applied for such estimations. In the present work, the Monte Carlo simulation is applied to evaluate the measurement uncertainty in distortion coefficient of one of the secondary pressure standards. In addition, the uncertainty is also estimated by taking the various correlation coefficients into account. The results obtained from these Monte Carlo calculations are compared with the experimental results, and the uncertainty obtained from the law of propagation method is validated by comparison with the Monte Carlo simulation. Keywords: Distortion coefficient; Monte Carlo method; Measurement uncertainty 1. Introduction Pressure is an important parameter in many fields, ranging from day-to-day life to high-end technological applications. The accurate measurement of pressure is equally necessary, be it a sophisticated satellite launching, altitude of airplane, blood pressure or as mundane as tire pressure. The losses due to inaccuracy in these pressure measurement may be huge not only in terms of monetary effects but also as enormous as life- threatening consequences. There comes the need of uncer- tainty quantification in all these measurements. Convention- ally, the uncertainties in measurement are estimated using the tried and tested, uniform and consistent methodology across the globe for uncertainty estimation, i.e., the ‘‘Evaluation of Measurement Data—Guide to the Expression of Uncertainty in Measurement’’ [1], generally referred to as GUM as stip- ulated by the Joint Committee for Guides in Metrology (JCGM). In India, the National Accreditation Board for Testing and Calibration Laboratories (NABL) has issued a document, ‘‘Guidelines for estimation and expression of uncertainty in measurement’’ (NABL 141) [2] to harmonize the methods for estimation of measurement uncertainty across all calibration laboratories in India, in line with GUM method. However, JCGM introduced JCGM 101:2008 [3] as an alternative method for uncertainty estimation in 2008 based on the propagation of probability distribution which can be realized through Monte Carlo simulation (MCS). Although simple and reliable, this method has not disseminated to the grass route levels in India. If the Indian regulatory bodies were to issue a document based on the propagation of probability distributions mentioned above, then MCS pro- cedure for uncertainty estimation would find greater visi- bility and adoption among calibration laboratories in India. Recently, many authors from India have used Monte Carlo method for uncertainty estimations [4–7]. There is a need to disseminate the knowledge of MCS to evaluate the uncertainty to calibration laboratories, industries and other stakeholders of calibration industry. It is known that the conventional GUM approach has various restrictions for uncertainty estimation [8, 9]. However, MCS overcomes these limitations [1, 8]. The Monte Carlo simulation requires generation of random *Corresponding author, E-mail: singhjs@nplindia.org M  APAN-Journal of Metrology Society of India https://doi.org/10.1007/s12647-019-00305-z 123