Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids

被引:5
|
作者
Yakupov, Bulat [1 ]
Smirnov, Ivan [1 ]
机构
[1] St Petersburg State Univ, Math & Mech Fac, Univ Skaya Nab 7-9, St Petersburg 199034, Russia
基金
俄罗斯科学基金会;
关键词
acoustic cavitation; cavitation threshold; ultrasound; machine learning; PRESSURE; WATER; SONOCHEMISTRY; NUCLEATION; ULTRASOUND;
D O I
10.3390/fluids8060168
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
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.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] THE APPLICATION OF MACHINE LEARNING TO PREDICT PATIENT TRANSFER FOR ENDOVASCULAR TREATMENT
    Han, J. H.
    Cora, E.
    Kamal, N.
    INTERNATIONAL JOURNAL OF STROKE, 2023, 18 (03) : 400 - 400
  • [22] Application of a machine learning algorithm to predict malignancy in thyroid cytopathology
    Range, Danielle D. Elliott
    Dov, David
    Kovalsky, Shahar Z.
    Henao, Ricardo
    Carin, Lawrence
    Cohen, Jonathan
    CANCER CYTOPATHOLOGY, 2020, 128 (04) : 287 - 295
  • [23] Application of GIS and Machine Learning to Predict Flood Areas in Nigeria
    Ighile, Eseosa Halima
    Shirakawa, Hiroaki
    Tanikawa, Hiroki
    SUSTAINABILITY, 2022, 14 (09)
  • [24] Application of machine learning techniques to predict entrepreneurial firm valuation
    Zhang, Ruling
    Tian, Zengrui
    McCarthy, Killian J.
    Wang, Xiao
    Zhang, Kun
    JOURNAL OF FORECASTING, 2023, 42 (02) : 402 - 417
  • [25] Application of machine learning methods to predict drought cost in France
    Heranval, Antoine
    Lopez, Olivier
    Thomas, Maud
    EUROPEAN ACTUARIAL JOURNAL, 2023, 13 (02) : 731 - 753
  • [26] On the Application of Machine Learning Models to Assess and Predict Software Reusability
    Yeow, Matthew Yit Hang
    Chong, Chun Yong
    Lim, Mei Kuan
    PROCEEDINGS OF THE 6TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING TECHNIQUES FOR SOFTWARE QUALITY EVALUATION, MALTESQUE 2022, 2022, : 17 - 22
  • [27] Application of machine learning methods to predict drought cost in France
    Antoine Heranval
    Olivier Lopez
    Maud Thomas
    European Actuarial Journal, 2023, 13 : 731 - 753
  • [28] Application of machine learning to predict the fluoride removal capability of MgO
    Fan, Lin
    Wang, Dexi
    Yu, Honglei
    Gong, Ze
    He, Yan
    Guo, Jinyuan
    JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2025, 13 (01):
  • [29] Xanthene dyes for reducing acoustic cavitation threshold in aqueous solution
    Kawabata, K
    Umemura, S
    ULTRASONICS, 1997, 35 (06) : 469 - 474
  • [30] Application of machine learning techniques to predict biodiesel iodine value
    Valbuena, G. Diez
    Tuero, A. Garcia
    Diez, J.
    Rodriguez, E.
    Battez, A. Hernandez
    ENERGY, 2024, 292