Machine learning models for the secondary Bjerknes force between two insonated bubbles

Abstract

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 its effects 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 center frequency of the ultrasound wave. The sign of the force may change with the phase difference between the oscillating bubbles. Meanwhile, the magnitude of 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. Graphic abstract

Machine learning for soft and liquid molecular materials

“…112 Classic machine learning methods are also relevant for predicting the characteristics of the ow regime, the behavior of a single bubble in a ow, and its deformation. [113][114][115][116][117] Integration of machine learning methods increases the efficiency of simulation models that are hardly performed for bubbles' transformation processes. Then, the bubble condensation simulation, which was previously performed using only the Lee model, was successfully implemented using articial neural networks.…”

“…112 Classic machine learning methods are also relevant for predicting the characteristics of the flow regime, the behavior of a single bubble in a flow, and its deformation. 113–117…”