气泡间二次Bjerknes力的机器学习模型
Machine learning models for the secondary Bjerknes force between two insonated bubbles
摘要：
二次Bjerknes力在气泡团的演化过程中起着重要的作用。然而，由于力对多个参数的复杂依赖性，在气泡簇的模拟中包括该力的影响是非常重要的。在本文中，机器学习被用来开发一个数据驱动的模型，作为气泡的平衡半径，气泡之间的距离，压力的振幅和频率的函数的两个受声波作用的气泡之间的二次Bjerknes力。这种力的变化超过几个数量级，这对通常的机器学习模型构成了严重的挑战。为了克服这个困难，力的大小和符号被分开并单独建模。一个非线性回归得到的前馈网络模型的对数的幅度，而符号是由支持向量机模型建模。介绍了机器模型的训练和验证的原理、实践方面。根据Keller-Miksis方程计算的值检查模型的预测。结果表明，该模型是非常有效的，同时提供准确的估计力。该模型使其计算可行的未来模拟的气泡群，包括二次Bjerknes力的影响。
英文摘要：
The secondary Bjerknes force plays a significant role in the evolution of bubble clusters. However, due to the complex dependence of the force on multiple parameters, it is highly non-trivial to include the effects of this force in the simulations of bubble clusters. In this paper, machine learning is used to develop a data-driven model for the secondary Bjerknes force between two insonated bubbles as a function of the equilibrium radii of the bubbles, the distance between the bubbles, the amplitude and the frequency of the pressure. The force varies over several orders of magnitude, which poses a serious challenge for the usual machine learning models. To overcome this difficulty, the magnitudes and the signs of the force are separated and modelled separately. A nonlinear regression is obtained with a feed-forward network model for the logarithm of the magnitude, whereas the sign is modelled by a support-vector machine model. The principle, the practical aspects related to the training and validation of the machine models are introduced. The predictions from the models are checked against the values computed from the Keller-Miksis equations. The results show that the models are extremely efficient while providing accurate estimate of the force. The models make it computationally feasible for the future simulations of the bubble clusters to include the effects of the secondary Bjerknes force.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
