Abstract
During the calibration of an aerodynamic probe, the correlation between the present representative flow quantities of the fluid and the measurand is determined. Thus, a large number, sometimes several thousands, of different calibration points are set and measured, making this a very time-consuming process. The differences in the calibration data of similar constructed probes are very small. With the help of statistical methods, more precisely Gaussian process regressions, this similarity is exploited in order to use existing calibration data of different probes reducing the calibration time with sufficient reconstruction accuracy. Data from single-wire hot-wire probes and from five-hole probes are tested and show a very high reconstruction accuracy compared to the full calibration data set. The number of calibration points in the five-hole probe case is reduced by at least one order of magnitude with comparable reconstruction accuracy.
Highlights
IntroductionObtained data of flow phenomena are still of great interest for academic and industrial research, despite the ongoing development and optimization of CFD (computational fluid dynamics) simulations
Obtained data of flow phenomena are still of great interest for academic and industrial research, despite the ongoing development and optimization of CFD simulations
With the help of statistical methods, more precisely Gaussian process regressions, this similarity is exploited in order to use existing calibration data of different probes reducing the calibration time with sufficient reconstruction accuracy
Summary
Obtained data of flow phenomena are still of great interest for academic and industrial research, despite the ongoing development and optimization of CFD (computational fluid dynamics) simulations. Rasmussen applies Gaussian processes GP to machine learning problems, both for regression and classification problems [5]. Since aerodynamic probe calibrations a) require a regression within all measured data points, and b) show similarity among themselves, the idea of applying Bayesian statistics, viz. Gaussian process regression, on aerodynamic probe calibration arises. Hypothesis 2 Bayesian statistics and machine learning algorithms, more precisely Gaussian process regression, are applicable on aerodynamic probe calibration data. In the last part of the paper (see section 4), investigations on the applicability of the Gaussian process regression on real calibration data is demonstrated. Apart from single-wire hot-wire data, the focus lies on the application of Gaussian process regression on the calibration of multi-hole pressure probes. General information on pattern recognition and machine learning is given by Bishop [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
