Metrological traceability of Polycyclic Aromatic Hydrocarbons (PAHs) measurements in green tea and mate

The development of suitable analytical methods to obtain metrologically traceable results in the determination of toxicants in food matrices is an important issue, as food represents the main way of assumption of many contaminants, among which the Polycyclic Aromatic Hydrocarbons (PAHs). The present work deals with the set up and internal validation of an analytical method carried out at INRiM for the quantification by gascromatography coupled with mass spectrometry (GC–MS) of some priority PAHs in green tea (Camellia sinensis) and yerba mate (Ilex paraguariensis), in order to obtain metrologically traceable results. Two approaches for the quantification were applied: an external calibration, for determining the GC–MS calibration curves by means of standard reference solutions and an internal calibration by using perdeuterated standards. For the external calibration, Weighted (WLS) and Weighted Total (WTLS) Least Squares fitting procedures were applied. The measurement uncertainty evaluation was carried out by applying the Law of Propagation of Uncertainty

Generation of CO2 gas mixtures by dynamic dilution for the development of gaseous certified reference materials

The use of Certified Reference Materials (CRMs) is of utmost importance to achieve the comparability and traceability of data, which are essential features of measurement results in environmental and climate fields. The present paper focuses on the generation of gas mixtures at known composition of carbon dioxide at atmospheric amount-of-substance fraction in synthetic air by means of a dynamic dilution system, designed and implemented at the Istituto Nazionale di Ricerca Metrologica (INRiM). The validation of the dynamic system in terms of amount-of-substance fraction is presented. The system was also used to verify the carbon dioxide amount-ofsubstance fraction of a suite of gas mixtures gravimetrically prepared at INRiM in the framework of the EMPIR Joint Research Project 19ENV05 – STELLAR. Dynamic dilution proved to be an effective tool for the preparation and certification of CRMs for gaseous pollutants (i.e. carbon dioxide, nitrogen oxides) relevant for monitoring environmental pollution and climate changes

Uncertainty propagation in field inversion for turbulence modelling in turbomachinery

The simulation of turbulent flows in turbomachinery requires to describe a wide range of scales and non-linear phenomena. Since the cost of scale resolving simulations is prohibitive for several configurations, turbulence closure models are still widely used in the framework of Reynolds-averaged Navier- Stokes (RANS) equations. In order to improve the prediction capability of these models, several machine learning strategies have been proposed. Among them, the field inversion approach allows to find a correction field which can be applied to the source term of the turbulence model in order to match experimental data: the correction field can then be generalised and expressed as a function of some flow features in order to extract modelling knowledge from the data. However, the reference experimental data are affected by uncertainty and this propagates to the correction field and to the final data-augmented model. In this work, the uncertainty propagation from the reference experimental data to the correction field is investigated. In particular, the flow field around a low pressure gas turbine cascade is studied in a challenging working condition characterised by laminar separation and transition to turbulence. The original RANS results are improved by the application of the field inversion algorithm in which the required gradients are computed by means of an adjoint approach. A sensitivity analysis is performed in order to provide a linearised propagation of the uncertainty from the experimental wall isentropic Mach number to the correction field

Non-linear models and best estimates in the GUM

A non-linear function of a sample average is different from the average of that function evaluated for each element of the sample. However, in the Guide to the Expression of Uncertainty in Measurement (GUM) the second approach to calculating an average is considered potentially preferable to the first in order to obtain a measurand estimate. In this paper the issue is discussed, and it is shown that the second approach is inconsistent with the GUM framework. However, it is indeed preferable for input quantities having an intrinsically random behaviour. We comment on the approach adopted in this respect in Supplement 1 of the GUM and show that it is consistent with the first method

Comparison of calibration curves using the Lp norm

Interlaboratory comparisons are a fundamental task in order to provide measurements with traceability. The simplest possible scenario implies that a single traveling standard of a quantity is measured at various laboratories. A more complex scenario arises when the laboratories measure a large set of standard values pertaining to a given physical quantity or when the traveling standard is not a realization of the quantity of interest but a measuring instrument. In the last case, it might be convenient to globally compare the calibration curves provided by the laboratories. We will introduce a distance between two generic analytical curves based on the Least Power L (p) norm of their difference. The properties of such distance will be presented, with particular attention to its dependence on the parameter p