Abstract
A machine learning approach for prediction the characteristics of tonal noise formed in a foil flow is tested. Experimental data are used to construct and analyze the mathematical models of pressure amplitude regression and models of classification of regimes of high-level tonal noise coming from the dimensionless parameters of the flow. Different families of algorithms are considered: from linear models to artificial neural networks. It is shown that a gradient boosting model with a determination coefficient 95% is the most accurate for describing and predicting the spectral curves of acoustic pressure on the entire interval of values of amplitudes and characteristic frequencies.
Original language | English |
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Pages (from-to) | 205-211 |
Number of pages | 7 |
Journal | Optoelectronics, Instrumentation and Data Processing |
Volume | 55 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Mar 2019 |
Keywords
- foil flow
- machine learning
- tonal noise
OECD FOS+WOS
- 1.03 PHYSICAL SCIENCES AND ASTRONOMY