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
  • [41] Application of machine learning to predict obstructive sleep apnea syndrome severity
    Mencar, Corrado
    Gallo, Crescenzio
    Mantero, Marco
    Tarsia, Paolo
    Carpagnano, Giovanna E.
    Foschino Barbaro, Maria P.
    Lacedonia, Donato
    HEALTH INFORMATICS JOURNAL, 2020, 26 (01) : 298 - 317
  • [42] Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue
    Gerazov, Branislav
    Caligari Conti, Daphne Anne
    Farina, Laura
    Farrugia, Lourdes
    Sammut, Charles V.
    Schembri Wismayer, Pierre
    Conceicao, Raquel C.
    SENSORS, 2021, 21 (20)
  • [43] A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning
    Jimenez-Serrano, Santiago
    Tortajada, Salvador
    Miguel Garcia-Gomez, Juan
    TELEMEDICINE AND E-HEALTH, 2015, 21 (07) : 567 - 574
  • [44] Application of machine learning to predict the outcome of pediatric traumatic brain injury
    Tunthanathip, Thara
    Oearsakul, Thakul
    CHINESE JOURNAL OF TRAUMATOLOGY, 2021, 24 (06) : 350 - 355
  • [45] Application of machine learning to predict the recurrence-proneness for cervical cancer
    Chih-Jen Tseng
    Chi-Jie Lu
    Chi-Chang Chang
    Gin-Den Chen
    Neural Computing and Applications, 2014, 24 : 1311 - 1316
  • [46] Application of machine learning algorithms to predict permeability in tight sandstone formations
    Topor, Tomasz
    NAFTA-GAZ, 2021, (05): : 283 - 292
  • [47] Application of Machine Learning to Predict Diseases Based on Symptoms in Rural India
    Biswal, Suvasree S.
    Amarnath, T.
    Panigrahi, Prasanta K.
    Biswal, Nrusingh C.
    BIOLOGICALLY INSPIRED TECHNIQUES IN MANY-CRITERIA DECISION MAKING, 2020, 10 : 55 - 61
  • [48] Application of machine learning to predict the recurrence-proneness for cervical cancer
    Tseng, Chih-Jen
    Lu, Chi-Jie
    Chang, Chi-Chang
    Chen, Gin-Den
    NEURAL COMPUTING & APPLICATIONS, 2014, 24 (06): : 1311 - 1316
  • [49] Application of machine learning techniques to predict anomalies in water supply networks
    Vries, D.
    van den Akker, B.
    Vonk, E.
    de Jong, W.
    van Summeren, J.
    WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY, 2016, 16 (06): : 1528 - 1535
  • [50] Machine learning application to predict the Mechanical properties of Glass Fiber mortar
    Nakkeeran, G.
    Krishnaraj, L.
    Bahrami, Alireza
    Almujibah, Hamad
    Panchal, Hitesh
    Zahra, Musaddak Maher Abdul
    ADVANCES IN ENGINEERING SOFTWARE, 2023, 180