Nonlinear Dynamics of Particles Excited by an Electric Curtain

The use of the electric curtain (EC) has been proposed for manipulation and control of particles in various applications. The EC studied in this paper is called the 2-phase EC, which consists of a series of long parallel electrodes embedded in a thin dielectric surface. The EC is driven by an oscillating electric potential of a sinusoidal form where the phase difference of the electric potential between neighboring electrodes is 180 degrees. We investigate the one- and two-dimensional nonlinear dynamics of a particle in an EC field. The form of the dimensionless equations of motion is codimension two, where the dimensionless control parameters are the interaction amplitude ($A$) and damping coefficient ($\beta$). Our focus on the one-dimensional EC is primarily on a case of fixed $\beta$ and relatively small $A$, which is characteristic of typical experimental conditions. We study the nonlinear behaviors of the one-dimensional EC through the analysis of bifurcations of fixed points. We analyze these bifurcations by using Floquet theory to determine the stability of the limit cycles associated with the fixed points in the Poincar\'e sections. Some of the bifurcations lead to chaotic trajectories where we then determine the strength of chaos in phase space by calculating the largest Lyapunov exponent. In the study of the two-dimensional EC we independently look at bifurcation diagrams of variations in $A$ with fixed $\beta$ and variations in $\beta$ with fixed $A$. Under certain values of $\beta$ and $A$, we find that no stable trajectories above the surface exists; such chaotic trajectories are described by a chaotic attractor, for which the the largest Lyapunov exponent is found. We show the well-known stable oscillations between two electrodes come into existence for variations in $A$ and the transitions between several distinct regimes of stable motion for variations in $\beta$

Acoustic streaming, fluid mixing, and particle transport by a Gaussian ultrasound beam in a cylindrical container

A computational study is reported of the acoustic streaming flow field generated by a Gaussian ultrasound beam propagating normally toward the end wall of a cylindrical container. Particular focus is given to examining the effectiveness of the acoustic streaming flow for fluid mixing within the container, for deposition of particles in suspension onto the bottom surface, and for particle suspension from the bottom surface back into the flow field. The flow field is assumed to be axisymmetric with the ultrasound transducer oriented parallel to the cylinder axis and normal to the bottom surface of the container, which we refer to as the impingement surface. Reflection of the sound from the impingement surface and sound absorption within the material at the container bottom are both accounted for in the computation. The computation also accounts for thermal buoyancy force due to ultrasonic heating of the impingement surface, but over the time period considered in the current simulations, the flow is found to be dominated by the acoustic streaming force, with only moderate effect of buoyancy force

Towards Efficient Deep Learning: From Compression, Search to Unification

Deep learning has gained considerable interest due to its record-breaking performance in a variety of different domains, including computer vision, natural language processing, multimodal understanding, etc. Meanwhile, deep neural networks are usually parameter-heavy, inefficient, and highly specialized. As a result, there has been a growing demand to improve the efficiency and interoperability of deep neural networks motivated by different needs. In this dissertation, we proposed to address those problems via serial of approaches, including (a) reducing the memory storage and energy footprint via parameter sharing (b) improving the trade-off between performance and computation via neural architecture search (c) unifying neural architectures across different modalities via cross-modality gradient harmonization

Principled Architecture-aware Scaling of Hyperparameters

Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or design principles of hyperparameters, such that they can generalize to diverse unseen scenarios. However, most designs or optimization methods are agnostic to the choice of network structures, and thus largely ignore the impact of neural architectures on hyperparameters. In this work, we precisely characterize the dependence of initializations and maximal learning rates on the network architecture, which includes the network depth, width, convolutional kernel size, and connectivity patterns. By pursuing every parameter to be maximally updated with the same mean squared change in pre-activations, we can generalize our initialization and learning rates across MLPs (multi-layer perception) and CNNs (convolutional neural network) with sophisticated graph topologies. We verify our principles with comprehensive experiments. More importantly, our strategy further sheds light on advancing current benchmarks for architecture design. A fair comparison of AutoML algorithms requires accurate network rankings. However, we demonstrate that network rankings can be easily changed by better training networks in benchmarks with our architecture-aware learning rates and initialization

Acoustic Streaming Near Albunex@ Spheres

Abstract: Acoustic streaming near bubbles in an external standing-wave sound field has been an interesting researeh topic for decades, It was relatively difficult to visualize the acoustic streaming pattern experimentally especially for small bubbles due to the instability of tbe bubbles and difficulties associated with the optical technique, Using Albunex" spheres (a contrast agent for ultrasonic imaging), a specially designed microscope devise and a video peak store technique, several acoustic streaming patterns around a single and a pair of AlbunexQ spheres of tens micrometer radius in a 160 kHz ultrasonic standing wave field have been captured. Spatial peak pressure amplitude of the standing wave was estimated to be in the order of 0,5 MPa by using optical interferometer. me acoustic streaming velocity was in the range of 50-100~m/s