Mixture distribution modelling of the sensitivities of a digital 3-axis MEMS accelerometers large batch

Huge quantities of low-cost analogue or digital MEMS sensors, in the order of millions per week, are produced by manufacturers. Their use is broad, from consumer electronic devices to Industry 4.0, Internet of Things and Smart Cities. In many cases, such sensors have to be calibrated by accredited laboratories to provide traceable measurements. However, at present, such a massive number of sensors cannot be calibrated and large-scale calibration systems and procedures are still missing. A first step to implementing these methods can be based on the distribution of the sensitivities of the large batches produced. Such distribution is also useful for sensor network end-users who need a single sensitivity, with the associated uncertainty, to be attributed to the whole network. Recently, a large batch of 100 digital 3-axis MEMS accelerometers was calibrated with a primary calibration system developed at INRiM and suitable for 3-axis accelerometers. Distributions of their sensitivities as a function of axis and frequency were analyzed and their non-normal behaviour was shown. However, in the preliminary phase of the study, the calibration uncertainties were not considered in these distributions. Therefore, in this paper, a mixture distribution modelling, based on Monte Carlo simulations and aimed at including the calibration uncertainties in the sensitivity distributions, is implemented and the resulting distributions are compared to the previous ones in histogram form. These distributions are also fitted with Johnson's unbounded and bimodal functions to get continuous distributions. This paper represents a further step towards the development of large-scale statistical calibration methods

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

EMUE-D2-3-TSPConcentration

Conformity assessment of mass concentration of total suspended particulate matter in air. The example shows how to calculate risks of false decisions in the conformity assessment of test results, according to the framework of JCGM 106:2008, in the case in which a normal distribution is not a valid assumption for modelling prior information on the measurand. As a case study, test results of mass concentration of Total Suspended Particulate Matter (TSPM) in ambient air, collected in the proximity of three stone quarries located in Israel, are considered; for each quarry, a log-normal distribution is chosen as the prior distribution. [Activity A1.2.3]

Towards large-scale calibrations: A statistical analysis on 100 digital 3-axis MEMS accelerometers

Given the growing development and production of low-cost digital MEMS sensors, e.g. accelerometers, gyroscopes, microphones, humidity, pressure and temperature sensors, large-scale measurements are nowadays a possible reality in many different fields, from industry 4.0 to environmental monitoring and smart cities. However, in most of cases, digital MEMS sensors still lack the required metrological traceability needed to provide traceable measurements. As a matter of fact, at present, a preliminary sensitivity value of these sensors is provided by the manufacturers by performing a simple adjustment, without a proper traceable calibration. This is basically due to the impossibility, nowadays, to guarantee large-scale calibration procedures at costs comparable to those of the sensors. For this purpose, it is first of all necessary to know their current technical performances, in terms of sensitivity and associated uncertainties, and then to define possible large-scale calibration methods. In this work, 100 nominally equal 3-axis MEMS digital accelerometers are calibrated with a recently-developed calibration setup at INRiM. Sensitivity values, together with their calibration expanded uncertainties, are compared to statistically analyze their dispersion and distribution within the considered sample. This is the first necessary step towards the development of large-scale calibration methods

EMUE-D3-2-LowMassBaPEvaluation

The example shows the uncertainty evaluation for the quantification of low masses of benzo[a]pyrene (BaP), which is an important Polycyclic Aromatic Hydrocarbon (PAH) for ambient air monitoring. Comparison between the results obtained according to the GUM uncertainty framework and the Monte Carlo method for the propagation of distributions applied to both real and simulated data sets are shown and discussed. [Activity A2.1.2]

EMUE-D2-1-MulticomponentMaterials

The example shows how to calculate risks of false decisions in the conformity assessment of a multicomponent material, taking into account both the measurement uncertainties and the covariances for the measured content values of the components. An influenza medication (NyQuil tablets) is considered as an example of multicomponent material [Activity A1.2.1]