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

ABSTRACT: 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 algo

SUBMITTER: Zhou S 

PROVIDER: S-EPMC9870118 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature