Examples related to metrological activities adaptable as template solutions

The aim of this work package is to develop new and updated examples of measurement uncertainty evaluation capable of acting as template solutions that end-users can adapt for their own applications. The examples will include measurement model construction using the JCGM modelling document JCGM 103, the application of the law of propagation of uncertainty and the Monte Carlo method (JCGM 100, JCGM 101, and JCGM 102) , the application of principles for addressing conformity assessments, for example, according to JCGM 106, and taking correlation into account.

Mass calibration: taking account of data and available knowledge using Bayesian methods

Currently, the state of the art in many calibration and testing laboratories is such that a measurement model relating the measurand and the (input) quantities that influence it is not explicitly considered. The starting point is instead only the sources of uncertainty, from which an uncertainty budget is developed. There are dangers in dispensing with the measurement model in that an adequate relationship between the measurand and the input quantities may not have been considered. Moreover, the sensitivity coefficients may not be readily available by other means, except in simple cases. All examples in the project relate to modelling, some in terms of the construction of the model, and some in terms of a comparison of a simple and a sophisticated model, fully in accordance with GUM principles, and will assist in developing the modelling document JCGM 103.

Equally, all examples are concerned with the propagation of uncertainties through a measurement model according to JCGM 100 or JCGM 102. In some instances a Monte Carlo method is also applied, where probability distributions (normal, rectangular, etc.) for the input quantities are propagated through the model. For the Monte Carlo method, the manner in which these probability distributions are selected, based on available metrological knowledge, is emphasised. In most examples a comparison is made of the relative merits of different methods for uncertainty propagation and conclusions drawn, so feeding into the New Perspective to the GUM (see Section B1.d). In some cases a Bayesian approach will be used, for which guidance document is being developed by JCGM/WG1. Inherent advantages such as in cases where there are natural limits on the quantities involved, such as purity (e.g., expressed as mass fraction) not exceeding 100 %, are emphasised.

Calibration examples relate to pH, mass spectrometry, mass measurement and unstable (drifting) reference materials. A testing example concerns the reaction of a test item to adverse fire conditions. Comparison examples relate to (i) gauge blocks and the underpinning of calibration and measurement capabilities, and (ii) linking of comparisons in the area of vibration of accelerometers. Another example fundamentally illustrates aspects of all three of calibration testing and comparison, relating to drift, hysteresis, bias, etc. These examples relate particularly to calibration and testing laboratories.

Examples of conformity assessment relate to multicomponent materials (influenza medication), pressure transducers (with correlation effects), atmospheric particulate matter, nanoparticle height measurement, and measurement traceability provided by a statement of conformance. These examples relate particularly to accreditation and regulatory bodies.

Multicomponent materials such as influenza vaccine: generalization of single component conformance testing