Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids
The acoustic cavitation of fluids, as well as related physical and chemical phenomena, causes a variety of effects that are highly important in technological processes and medicine. Therefore, it is important to be able to control the conditions that allow cavitation to begin and progress. However,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Fluids |
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| Online Access: | https://www.mdpi.com/2311-5521/8/6/168 |
| _version_ | 1827737369752633344 |
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| author | Bulat Yakupov Ivan Smirnov |
| author_facet | Bulat Yakupov Ivan Smirnov |
| author_sort | Bulat Yakupov |
| collection | DOAJ |
| description | The acoustic cavitation of fluids, as well as related physical and chemical phenomena, causes a variety of effects that are highly important in technological processes and medicine. Therefore, it is important to be able to control the conditions that allow cavitation to begin and progress. However, the accurate prediction of acoustic cavitation is dependent on a complex relationship between external influence parameters and fluid characteristics. The multiparameter problem restricts the development of successful theoretical models. As a result, it is critical to identify the most important parameters influencing the onset of the cavitation process. In this paper, the ultrasonic frequency, hydrostatic pressure, temperature, degassing, density, viscosity, volume, and surface tension of a fluid were investigated using machine learning to determine their significance in predicting acoustic cavitation strength. Three machine learning models based on support vector regression (SVR), ridge regression (RR), and random forest (RF) algorithms with different input parameters were trained. The results showed that the SVM algorithm performed better than the other two algorithms. The parameters affecting the active cavitation nuclei, namely hydrostatic pressure, ultrasound frequency, and outgassing degree, were found to be the most important input parameters influencing the prediction of the cavitation threshold. Other parameters have a minor impact when compared to the first three, and their role can be compensated for by alternative variables. The further development of the obtained results provides a new way to optimize and improve existing theoretical models. |
| first_indexed | 2024-03-11T02:29:04Z |
| format | Article |
| id | doaj.art-b633058488944522a3982db509246903 |
| institution | Directory Open Access Journal |
| issn | 2311-5521 |
| language | English |
| last_indexed | 2024-03-11T02:29:04Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fluids |
| spelling | doaj.art-b633058488944522a3982db5092469032023-11-18T10:23:13ZengMDPI AGFluids2311-55212023-05-018616810.3390/fluids8060168Application of Machine Learning to Predict the Acoustic Cavitation Threshold of FluidsBulat Yakupov0Ivan Smirnov1Mathematics and Mechanics Faculty, Saint Petersburg State University, Universitetskaya Nab. 7/9, Saint Petersburg 199034, RussiaMathematics and Mechanics Faculty, Saint Petersburg State University, Universitetskaya Nab. 7/9, Saint Petersburg 199034, RussiaThe acoustic cavitation of fluids, as well as related physical and chemical phenomena, causes a variety of effects that are highly important in technological processes and medicine. Therefore, it is important to be able to control the conditions that allow cavitation to begin and progress. However, the accurate prediction of acoustic cavitation is dependent on a complex relationship between external influence parameters and fluid characteristics. The multiparameter problem restricts the development of successful theoretical models. As a result, it is critical to identify the most important parameters influencing the onset of the cavitation process. In this paper, the ultrasonic frequency, hydrostatic pressure, temperature, degassing, density, viscosity, volume, and surface tension of a fluid were investigated using machine learning to determine their significance in predicting acoustic cavitation strength. Three machine learning models based on support vector regression (SVR), ridge regression (RR), and random forest (RF) algorithms with different input parameters were trained. The results showed that the SVM algorithm performed better than the other two algorithms. The parameters affecting the active cavitation nuclei, namely hydrostatic pressure, ultrasound frequency, and outgassing degree, were found to be the most important input parameters influencing the prediction of the cavitation threshold. Other parameters have a minor impact when compared to the first three, and their role can be compensated for by alternative variables. The further development of the obtained results provides a new way to optimize and improve existing theoretical models.https://www.mdpi.com/2311-5521/8/6/168acoustic cavitationcavitation thresholdultrasoundmachine learning |
| spellingShingle | Bulat Yakupov Ivan Smirnov Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids Fluids acoustic cavitation cavitation threshold ultrasound machine learning |
| title | Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids |
| title_full | Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids |
| title_fullStr | Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids |
| title_full_unstemmed | Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids |
| title_short | Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids |
| title_sort | application of machine learning to predict the acoustic cavitation threshold of fluids |
| topic | acoustic cavitation cavitation threshold ultrasound machine learning |
| url | https://www.mdpi.com/2311-5521/8/6/168 |
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