PLoS ONE (Jan 2023)

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

  • Shuni Zhou,
  • Guangxing Wu,
  • Yehong Dong,
  • Yuanxiang Ni,
  • Yuheng Hao,
  • Yunhe Jiang,
  • Chuang Zhou,
  • Zhiyu Tao

DOI
https://doi.org/10.1371/journal.pone.0277672
Journal volume & issue
Vol. 18, no. 1
p. e0277672

Abstract

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Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe is an important technique widely used in flow vector measurement. It is a new attempt to integrate machine learning method into multi-hole probe measurement. In this work, six typical supervised learning methods in scikit-learn library are selected for parameter adjustment at first. Based on the optimal parameters, a comprehensive evaluation is conducted from four aspects: prediction accuracy, prediction efficiency, feature sensitivity and robustness on the failure of some hole port. As results, random forests and K-nearest neighbors' algorithms have the better comprehensive prediction performance. Compared with the in-house traditional algorithm, the machine learning algorithms have the great advantages in the computational efficiency and the convenience of writing code. Multi-layer perceptron and support vector machines are the most time-consuming algorithms among the six algorithms. The prediction accuracy of all the algorithms is very sensitive to the features. Using the features based on the physical knowledge can obtain a high accuracy predicted results. Finally, KNN algorithm is successfully applied to field measurements on the angle of attack of a wind turbine blades. These findings provided a new reference for the application of machine learning method in multi-hole probe calibration and measurement.