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Modeling of the Tonal Noise Characteristics in a Foil Flow by using Machine Learning

Optoelectronics, Instrumentation and Data Processing Aims and scope

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.

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Correspondence to S. S. Abdurakipov.

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Russian Text © The Author(s), 2019, published in Avtometriya, 2019, Vol. 55, No. 2, pp. 123–131.

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Abdurakipov, S.S., Tokarev, M.P., Pervunin, K.S. et al. Modeling of the Tonal Noise Characteristics in a Foil Flow by using Machine Learning. Optoelectron.Instrument.Proc. 55, 205–211 (2019). https://doi.org/10.3103/S8756699019020134

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  • DOI: https://doi.org/10.3103/S8756699019020134

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