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.
|Number of pages||7|
|Journal||Optoelectronics, Instrumentation and Data Processing|
|Publication status||Published - 1 Mar 2019|
- foil flow
- machine learning
- tonal noise
- 1.03 PHYSICAL SCIENCES AND ASTRONOMY