Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A critical component in a good digital-twin system is real-time, accurate 3D object tracking. Most existing works solve 3D object tracking through the lens of robotic grasping, employ older generations of depth sensors, and measure performance metrics that may not apply to other digital-twin applications such as in AR. In this work, we create a novel RGB-D dataset, called Digital Twin Tracking Dataset (DTTD), to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy. To reduce point cloud noise from the input source, we select the latest Microsoft Azure Kinect as the state-of-the-art time-of-flight (ToF) camera. In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial motion capturing system. Through extensive experiments with model-level and dataset-level analysis, we demonstrate that DTTD can help researchers develop future object tracking methods and analyze new challenges. The dataset, data generation, annotation, and model evaluation pipeline are made publicly available as open source code at: https://github.com/augcog/DTTDv1.

Rebounding phenomena occurring during the interaction between dust particles and droplets are a significant factor contributing to the inefficacy of dust reduction methods. However, existing literature has not quantitatively elucidated the mechanics of rebounding during collisions between micrometer-sized suspended droplets and particles. To address this gap, the present study developed a multiphase numerical simulation model employing the volume-of-fluid (VOF)–level set method and dynamic mesh techniques to investigate rebounding dynamics. A theoretical model was proposed to predict the rebounding threshold, integrating factors such as sample size, droplet surface tension, droplet viscosity, droplet–particle diameter ratio, droplet–particle density ratio, and contact angle hysteresis. By applying specific parameters to this model, the study examined the impact of these key factors on the effectiveness of dust reduction. Additionally, the research proposed several strategies to optimize spray parameters and additives. The findings provide a crucial foundation for advancing dust suppression technologies and other industrial applications, including spray drying, coating technologies, and inkjet printing.

As ethanol-gasoline is being widely used as an energy-saving alternative around the world, its fire/explosion problems needed to be solved, but few research studies involve its explosion inhibition. Additionally, although dodecafluoro-2-methylpentan-3-one (Novec 1230) was a long-term replacement of Halon, its use in explosion inhibitions has not been found. This work aims to investigate synergy inhibition effects of Novec 1230 and venting on ethanol-gasoline (E10) vapor explosion. Flame propagation and pressure data were recorded using a high-speed camera and three pressure transducers, respectively. The chemical principle of E10 explosion inhibition using Novec 1230 was inferred and verified by reaction product analysis using a gas chromatography-mass spectrometer (GC-MS). The results indicate that synergy of venting and Novec 1230 can effectively inhibit flame propagation and explosion overpressure of E10. As $\phi$ increased from 0 to 1.5, all typical overpressure peak structures ( $P_b$, $P_{fv}$, $P_{cv}$, $P_{ext}$) were inhibited and the best $P_{cv}$ reached 74.5%. For vented ducts, $\phi$ showed negative linear correlations with $P_{max}$ and $K_G$, $P_{max}$, and $K_G$ were quadratic increments monotonously with the vent coefficient ( $Kv$). Two formulas were obtained, which can be used to quantitatively calculate $P_{max}$ and $K_G$ at various $\phi$ and $Kv$ values. These results were useful for conditions that actual engineering required two explosion inhibition means simultaneously and Novec 1230 explosion inhibition system design.

Collisions between droplets and solid particles may result in rebounding, coalescence, or droplet rupture. In spray-based dust suppression, coalescence is the most desirable outcome because it promotes particle mass increase and gravitational settling. Therefore, identifying the governing factors and critical conditions for coalescence is essential for improving spray dust mitigation efficiency. Although previous studies have made significant progress in establishing transition criteria between rebounding and coalescence, quantitative predictive models distinguishing coalescence from rupture remain limited, particularly for suspended particle–droplet interactions at the micro-scale. In this study, a computational fluid dynamics (CFD) model employing a dynamic mesh technique and the coupled level-set/volume of fluid (VOF) method is used to investigate the collision dynamics between suspended particles and droplets. The results demonstrate that the collision outcome is jointly governed by the solid–liquid density ratio, contact angle, relative impact velocity, particle-to-droplet size ratio, droplet viscosity, and surface tension. Based on momentum and energy conservation principles, a predictive criterion model for the transition between coalescence and rupture is proposed, revealing the underlying dynamic mechanisms controlling collision behavior. Based on the proposed model, the influence of key parameters on the coalescence regime is quantified, providing guidance for optimizing spray operating parameters and additive formulations. The present findings not only promote the development of spray dust suppression technology but also provide theoretical support for related fields, including particle surface modification, fire smoke scrubbing, and pharmaceutical engineering.