Aerodynamic probe calibration using Gaussian process regression

Heckmeier, Florian M.; Breitsamter, Christian

doi:10.1088/1361-6501/aba37d

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…Sensor calibration and cross-sensitivity compensation are receiving growing attention in the wider field of ML [33]- [35], [38], [39], [42]- [45]. In this section, we position our work based on Bayesian learning, regression, and design of experiment within the context of these studies, which have relied on support vector machines (SVMs) [34], random forests (RFs) [34], [38], [39], Gaussian process regression (GPR) [39], [42], [43], and artificial neural networks (ANNs) taking most often the form of multilayered perceptrons (MLP) [33], [34], [44], [45] and fuzzy neural networks (FNNs) [35], among other methodologies [34].…”

Section: B Comparison With Other ML Methodsmentioning

confidence: 99%

“…As an alternative, random forests have successfully been applied to the calibration of multi-pollutant sensors [38] and of NO 2 and particulate matter (PM 10 ) sensors [39]. Thirdly, GPR has been applied to the calibration of thermal and differential-pressure anemometers [42], NO 2 and particulate matter (PM 10 ) sensors [39], and capacitive artificial skin sensors [43].…”

Section: B Comparison With Other ML Methodsmentioning

confidence: 99%

“…3.1.4], and total least squares [28], the least absolute shrinkage and selection operator [29]- [31], and ridge regularization [30], [32], among others, have been used. Besides polynomial regression, a variety of machine learning (ML) methods have been successfully used to calibrate liquid mixture sensors [33]- [35], gas sensors [36]- [40], pH sensors [41], thermal and differential-pressure anemometers [42], a tactile sensor [43], and a photonic sensor [44]. Some of these sensors were successfully compensated against the influence of temperature [33], [34], [38], [45], relative humidity [38], [45], and chemical cross-sensitivities [38].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Sensor Calibration

Berger

Schott²,

Oliver

2022

IEEE Sensors J.

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The calibration of multisensor systems can cause significant costs in terms of time and resources, in particular when crosssensitivities to parasitic influences are to be compensated. Successful calibration ensures the trustworthy subsequent operation of a sensor system, guaranteeing that one or several measurands of interest can be inferred from its output signals with specified uncertainty. As the present study shows, this goal can be reached by reduced calibration procedures with fewer calibration conditions than parameters needed to model the device response. This is achieved using Bayesian inference by combining calibration data of a sensor system with statistical prior information about the ensemble to which it belongs. Optimal reduced sets of calibration conditions are identified by the method of Bayesian experimental design. The method is demonstrated on a Hall-temperature sensor system whose nonlinear response model requires seven parameters in the temperature range between -30 °C and 150 °C and for magnetic field values B between -25 mT and 25 mT. For the prior, a multivariate normal distribution of the model parameters is acquired using 14 specimens of the sensor ensemble. I-optimal calibration at one, two, and three temperatures reduces the rms standard deviation of B inferred from sensor output signals from 203 µT before calibration down to 78 µT, 41 µT, and 34 µT, respectively. Similar conclusions apply to G-optimal calibration. The paper describes how to implement the Bayesian prior acquisition, inference, and experimental design. The proposed approach can help save resources and cut costs in sensor calibration.

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Section: B Comparison With Other ML Methodsmentioning

confidence: 99%

Section: B Comparison With Other ML Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Sensor Calibration

Berger

Schott²,

Oliver

2022

IEEE Sensors J.

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“…For the reconstruction of the flow-field properties, the measured pressures are post-processed taking the calibration data of both, temporal and spatial, calibrations into account. A high reconstruction accuracy below 0.2 • in both flow angles and 0.1 m/s in the reconstructed velocity can be achieved, as shown in Heckmeier and Breitsamter (2020). Pretests have shown that the temporal characteristics of the probe are limited due to bandwidth restrictions of the transfer function in the temporal calibration.…”

Section: Methodsmentioning

confidence: 88%

Spatial and temporal resolution of a fast-response aerodynamic pressure probe in grid-generated turbulence

2021

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The spatial and temporal resolution of a fast-response aerodynamic pressure probe (FRAP) is investigated in a benchmark flow of grid-generated turbulence. A grid with a mesh size of $$M=6.4$$ M = 6.4 mm is tested for two different free-stream velocities, hence, resulting in Reynolds numbers of $$Re_M= \{4300,12800\}$$ R e M = { 4300 , 12800 } . A thorough analysis of the applicability of the underlying assumptions with regard to turbulence isotropy and homogeneity is carried out. Taylor’s frozen turbulence hypothesis is assumed for the calculation of deducible flow quantities, like the turbulent kinetic energy or the dissipation rate. Furthermore, besides the examination of statistical quantities, velocity spectra of measurements downstream of the grid are quantified. Results of a small fast-response five-hole pressure probe equipped with piezo-resistive differential pressure sensors are compared to single-wire hot-wire constant temperature anemometry data for two different wire lengths. Estimates of temporal and spatial turbulent scales (e.g., Taylor micro scale and Kolmogorov length scale) show good agreement to data in the literature but are affected by filtering effects. Especially in the energy spectra, very high bandwidth content cannot be resolved by the FRAP, which is mainly due to bandwidth limits in the temporal calibration of the FRAP and the minimal resolution of the integrated sensors. Graphic abstract

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“…Given that pressure varies with altitude, probes used in aircraft applications are equipped with an additional series of reading ports, called the static ring, where static pressure is measured. The probes are calibrated before their usage in flight conditions and can resolve flow angles up to

with high precision [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Development of an Analytic Convection Model for a Heated Multi-Hole Probe for Aircraft Applications

Muro

Heckmeier

Jenkins³

et al. 2021

Sensors

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Ice accretion or icing is a well-known phenomenon that entails a risk for the correct functioning of an aircraft. One of the areas more vulnerable to icing is the air data measuring system. This paper studies the icing protection offered by a heating system installed inside a multi-hole probe. The problem is initially solved analytically, creating a tool that can be used in order to predict the heating performance depending on the flying conditions. Later, the performance of the real system is investigated with a heated five-hole probe prototype in a wind tunnel experiment. The measured results are compared with the predictions made by the analytical model. Last, the icing protection provided by the system is estimated with respect to flying altitude and speed. As a result, a prediction tool that can be used in order to make quick icing risk predictions for straight cylindrical probes is delivered. Furthermore, the study provides some understanding about how parameters like altitude and air speed affect the occurrence of ice accretion.

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Aerodynamic probe calibration using Gaussian process regression

Cited by 13 publications

References 16 publications

Bayesian Sensor Calibration

Bayesian Sensor Calibration

Spatial and temporal resolution of a fast-response aerodynamic pressure probe in grid-generated turbulence

Development of an Analytic Convection Model for a Heated Multi-Hole Probe for Aircraft Applications

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