Optimal Calibration of Directional Velocity Probes

Shaw-Ward, Samantha; McParlin, Stephen C.; Nathan, Paul; Birch, David M.

doi:10.2514/1.j056762

Cited by 6 publications

(9 citation statements)

References 12 publications

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“…Regression models were trained using sample sizes ranging from 0.25N to 4N, with little difference in the convergence for N > 28 (so that the inclusion of more data did not improve the accuracy). This is broadly consistent with the number of calibration points required if the locations of these are selected optimally (15).…”

Section: E Simple Step and Markov Chain Monte-carlo Regressionssupporting

confidence: 76%

“…For the purposes of comparing and assessing the different calibration techniques, data from the experiments of Shaw-Ward et al (15) have been used. In these experiments, calibration data at known angles (α, β ) were obtained using a hemispherical-tipped seven-hole probe The probe used seven differential pressure sensors referenced directly the local static pressure, and had a full-scale range of ±2.5 kPa and an estimated uncertainty of less than 0.25% full-scale.…”

Section: Methodology a Experimental Datamentioning

confidence: 99%

“…(3). In all cases, it was assumed that the sensor outputs satisfied the generalized responsesurface model for directional velocity probes to the sixth order (15). The calibrations were implemented using the MATLAB Statistics and Machine Learning Toolbox (release 2018a) on a laptop PC with an 2.60 GHz i5 processor.…”

Section: B Sensor Data Fusion Approachesmentioning

confidence: 99%

“…The validation error is 0.89%, while the test error was 0.84%. By comparison, a generalized look-up calibration algorithm yields an overall uncertainty of 1.88% (15). The error between the output and the target vectors is Gaussian, with a mean of −0.0094 and σ = 0.0529.…”

Section: A Standard Feedforward Backpropagation Networkmentioning

confidence: 99%

“…Following the recommendations of Shaw-Ward et al (15), the response surfaces were modelled as sixth-order polynomials in two variables, resulting in N = 28 terms; consequently, a minimum of 28 measurements are required in order to obtain a unique solution. Regression models were trained using sample sizes ranging from 0.25N to 4N, with little difference in the convergence for N > 28 (so that the inclusion of more data did not improve the accuracy).…”

Section: E Simple Step and Markov Chain Monte-carlo Regressionsmentioning

confidence: 99%

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Neural-Network-Based Sensor Data Fusion for Multi-Hole Fluid Velocity Probes

2020

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For measuring three components of velocity in unknown flow fields, multi-hole pressure probes possess a significant advantage. Unlike methods such as hot-wire anemometry, laser-Doppler velocimetry and particle-image velocimetry, multi-hole pressure probes can provide not only the three components of local velocity, but also static and stagnation pressures. However, multi-hole probes do require exhaustive calibration. The traditional technique for calibrating these probes is based on either look-up tables or polynomial curve fitting, but with the low cost and easy availability of powerful computing resources, neural networks are increasingly being used. Here, we explore the possibility to further reduce measurement uncertainty by implementing neural-networkbased methods that have not been previously used for probe calibration, including supervised and unsupervised learning neural networks, regression models and elastic-map methods. We demonstrate that calibrating probes in this way can reduce the uncertainty in flow angularity by as much as 50% compared to conventional techniques.

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Section: E Simple Step and Markov Chain Monte-carlo Regressionssupporting

confidence: 76%

Section: Methodology a Experimental Datamentioning

confidence: 99%

Section: B Sensor Data Fusion Approachesmentioning

confidence: 99%

Section: A Standard Feedforward Backpropagation Networkmentioning

confidence: 99%

Section: E Simple Step and Markov Chain Monte-carlo Regressionsmentioning

confidence: 99%

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Neural-Network-Based Sensor Data Fusion for Multi-Hole Fluid Velocity Probes

2020

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Measurement of Local Flow Velocity

2024

Measurement in Fluid Mechanics

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Calibration model and numerical simulation of a 7-hole probe based on pressure–velocity parameterization equation

Yao¹,

Zhou²,

Wang³

et al. 2021

Meas. Sci. Technol.

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As a sensor of a flow field detection system, a 7-hole probe can detect the flow field velocity and retrieve three-dimensional (3D) information of the flow field. Owing to its simple structure and strong environmental adaptability, it is particularly important to calibrate it when it is widely used in turbine machinery, aerospace, and other fields. To detect the 3D flow field in the middle atmosphere, a novel calibration method based on the potential flow theory is designed using a hemispherical 7-hole probe. The hemispherical 7-hole probe was numerically calibrated through numerical simulation, and the coefficients of the calibration equation are provided. In comparison with the traditional 7-hole probe calibration method, the calibration process is significantly shortened while maintaining good measurement accuracy. The velocity error was less than 5% and the angle error was approximately 0.5°.

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Optimal Calibration of Directional Velocity Probes

Cited by 6 publications

References 12 publications

Neural-Network-Based Sensor Data Fusion for Multi-Hole Fluid Velocity Probes

Neural-Network-Based Sensor Data Fusion for Multi-Hole Fluid Velocity Probes

Measurement of Local Flow Velocity

Calibration model and numerical simulation of a 7-hole probe based on pressure–velocity parameterization equation

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