The physical parameters are important factors affecting the bubble-collapse strength. In this paper, single factor analysis and multiple regression analysis are applied to study the relationship among the physical parameters and bubbles maximum dimensionless radius (R_max). There is a very weak linear correlation between the density, viscosity, sound velocity, surface tension, saturated vapor pressure and R_max, respectively. Furthermore, the regression models based on two types of machine-learning algorithms, namely random forest and neural network, are established to predict the bubble-collapse strength affected by combined physical parameters, the results show that all of them are feasible and efficient. In addition, the viscosity and surface tension have obvious influence on the collapse strength, and the influence of density and sound velocity are the least. Those results are helpful in understanding the bubble-collapse strength under different host liquids published in previous researches.

物理参数是影响气泡破裂强度的重要因素。在本文中，单因素分析和多元回归分析被 施加 到 研究的物理参数之间的关系和气泡最大无量纲半径（[R_最大）。密度，粘度，声速，表面张力，饱和蒸气压和R _max之间的线性关系非常弱。此外，建立了基于随机森林和神经网络两类机器学习算法的回归模型，以预测受组合物理参数影响的气泡坍塌强度，结果表明它们都是可行的 ， 高效的。另外，粘度和表面张力对塌陷强度有明显的影响，而密度和声速的影响最小。这些结果有助于理解先前研究中发表的不同基质液体下的气泡破裂强度。