Capillary Rise and Oil Recovery under Primary Bjerknes Force Experienced by Bubbles

A numerical study of forced imbibition into capillary tubes under primary Bjerknes force is presented. A mathematical model is developed to predict the motion of a meniscus while an external force is applied. Remarkable enhancement in liquid flow attributed to the frequency and intensity of a waveform on primary Bjerknes force and to the viscosity of fluid was observed. It was found that imbibition optimal frequency for each equilibrium height depends on the time as ω(xeq)∼emt, where the recovery time is a viscosity function t(xeq)∼μH. The results are presented in a set of curves, which reveal the features of enhanced oil recovery of the system under consideration. Some physical implications are discussed.

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

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

3D steerable, acoustically powered microswimmers for single-particle manipulation

The ability to precisely maneuver micro/nano objects in fluids in a contactless, biocompatible manner can enable innovative technologies and may have far-reaching impact in fields such as biology, chemical engineering, and nanotechnology. Here, we report a design for acoustically powered bubble-based microswimmers that are capable of autonomous motion in three dimensions and selectively transporting individual synthetic colloids and mammalian cells in a crowded group without labeling, surface modification, or effect on nearby objects. In contrast to previously reported microswimmers, their motion does not require operation at acoustic pressure nodes, enabling propulsion at low power and far from an ultrasonic transducer. In a megahertz acoustic field, the microswimmers are subject to two predominant forces: the secondary Bjerknes force and a locally generated acoustic streaming propulsive force. The combination of these two forces enables the microswimmers to independently swim on three dimensional boundaries or in free space under magnetical steering.