Search for: All records

Direct ink writing (DIW) process is a facile additive manufacturing technology to fabricate three-dimensional (3D) objects with various materials. Its versatility has attracted considerable interest in academia and industry in recent years. As such, upsurging endeavors are invested in advancing the ink flow behaviors in order to optimize the process resolution and the printing quality. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and its underlying theories. Here, we present a comprehensive analytical study of non-Newtonian ink flow behavior during the DIW process. Different syringe-nozzle geometries are modeled for the comparative case studies. By using the computational fluid dynamics (CFD) simulation method, we reveal the shear-thinning property during the ink extrusion process. Besides, we study the viscosity, shear stress, and velocity fields, and analyze the advantages and drawbacks of each syringe-nozzle model. On the basis of these investigations and analyses, we propose an improved syringe-nozzle geometry for stable extrusion and high printing quality. A set of DIW printing experiments and rheological characterizations are carried out to verify the simulation studies. The results developed in this work offer an in-depth understanding of the ink flow behavior in the DIW process, providing valuable guidelines for optimizing the physical DIW configuration toward high-resolution printing and, consequently, improving the performance of DIW-printed objects.

 

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. 

Additive manufacturing (AM) has emerged as a promising approach to achieve energetic materials (EMs) with intricate geometries and controlled microstructures, which are crucial for safety and performance optimization. However, current AM methods still face limitations such as limited densities and inadequate solids loading. To overcome these limitations, we have developed a pressure‐assisted binder jet (PBJ) process that has the potential to allow for the fabrication of intricate EMs while preserving their desired properties. This study aims to investigate the effects of printing parameters on the microstructures and properties of EMs, including density, solids loading, mechanical properties, and heterogeneity. Our results demonstrate that the PBJ process achieves exceptional properties in EMs, including densities up to 83.4 % and solids loading up to 95.4 %, surpassing those achieved by existing AM processes. Furthermore, the mechanical properties of the fabricated EMs are comparable to those achieved using conventional fabrication techniques, including a compressive strength of 3.32 MPa, a Young's modulus of 16.68 MPa, a Poisson's ratio of 0.45, a shear modulus of 5.73 MPa, and a bulk modulus of 21.01 GPa. Various test cases were printed to showcase the ability of the PBJ process to create EMs with complex structures and exceptional properties. Micro‐computed tomography was employed to analyze the influence of printing parameters on the internal composition and microstructures of the printed specimens.

 

As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW.

 

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.