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Magnetic particle tracking (MPT) is a recently developed non-invasive measurement technique that has gained popularity for studying dense particulate or granular flows. This method involves tracking the trajectory of a magnetically labeled particle, the field of which is modeled as a dipole. The nature of this method allows it to be used in opaque environments, which can be highly beneficial for the measurement of dense particle dynamics. However, since the magnetic field of the particle used is weak, the signal-to-noise ratio is usually low. The noise from the measuring devices contaminates the reconstruction of the magnetic tracer’s trajectory. A filter is then needed to reduce the noise in the final trajectory results. In this work, we present a neural network-based framework for MPT trajectory reconstruction and filtering, which yields accurate results and operates at very high speed. The reconstruction derived from this framework is compared to the state-of-the-art extended Kalman filter-based reconstruction. 

Coherent structures are ubiquitous in unsteady flows. They can be regarded as certain kinds of spatial-temporal patterns that interact with the neighboring field. Although they play a key role in convection and mixing, there is no consensus on how to define them, and their dynamics are complicated. In the past decades, many methods are developed to identify coherent structures based on instantaneous velocity fields (e.g., vortex identification) or long-time statistics (e.g., proper orthogonal decomposition), but the evolution process of individual structures is not well considered in the identification. In this paper, we propose a new method to classify coherent motions according to their evolution dynamics. Specifically, the evolutions are represented by trajectories in the phase space. We define a distance between two trajectories and use it to construct a network that characterizes all evolution patterns. Using spectrum clustering, we categorize these patterns into various groups. This method is applied to a low Reynolds number wake flow downstream of two cylinders-in-tandem, where one of the cylinders oscillates in the transverse direction. The flow is quasi-periodic, and four types of recurrent spatial-temporal patterns can be identified. It is a useful tool to investigate low Reynolds number unsteady flows. 

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction. 

Abstract Electrohydrodynamic jet (e‐jet) printing is a high‐resolution printed electronics technique that uses an electric field to generate droplets. It has great application potential with the rapid development of flexible and wearable electronics. Triboelectric nanogenerators (TENG), which can convert mechanical motions into electricity, have found many high‐voltage applications with unique merits of portability, controllability, safety, and cost‐effectiveness. In this work, the application of a TENG is extended to printed electronics by employing it to drive e‐jet printing. A rotary freestanding TENG is applied as the high‐voltage power source for generating stable ink droplet ejection. The TENG‐driven droplet generation and ejection process and printed features with varied operation parameters are investigated. Results reveal that the jetting frequency could be controlled by the TENG's operation frequency, and high‐resolution printing with feature size smaller than nozzle size is achieved using the setup. Notably, TENG as the power source for e‐jet printing supplies a limited amount of current, which leads to better safety for both equipment and personnel compared to conventional high‐voltage power supplies. With the superiority of TENG in the sense of safety and cost, the work presents a promising solution for the next‐generation of high‐resolution printed electronics and broadens the scope of TENG application.