To provide a comprehensive understanding of virus transmission inside small indoor spaces, numerical simulation of sneezing droplets spreading in a cafeteria is conducted through computational fluid dynamics. The numerical results show that dining face to face is extremely vulnerable to direct infection by others' respiratory droplets. Different heights of droplet sources are compared, which indicates that sneezing from a standing person results in a longer survival time of droplets in the air. Scenarios with fewer customers without face to face seating and turning off the horizontal supplying air conditioner are examined as well. Various surfaces are still detected with droplets in 300 s after sneezing. The horizontal supplying air conditioner causes increment in the velocities of the droplets and leads to further spreading of the droplets. It is essential to sanitize all surfaces in a cafeteria including the walls, floor, ceiling, and tables that are not occupied by any customer. Keeping a safe distance in small indoor spaces such as cafeterias does not offer sufficient protection for activities without wearing a face mask. It is recommended that cafeterias and canteens only accept take-away orders.

A virus-laden particle movement from urinal flushing is simulated. Similar to the toilet-induced flushing, results indicate that the trajectory of the particles triggered by the urinal flushing manifests an external spread type. Even more alarmingly, the particle can reach 0.84 m (man’s thigh) in 5.5 s when compared with the diffusion performance of the toilet-induced one (around 0.93 m in 35 s). A more violent climbing tendency is discovered in this Letter. Wearing masks should be made mandatory in public washrooms, and anti-diffusion improvements of facilities in public washrooms are urgently needed, especially in the current “SARS-CoV-2” crisis.

The oscillating heat pipe (OHP) is a new member in the family of heat pipes, and it has great potential applications in energy conservation. However, the fluid flow and heat transfer in the OHP as well as the fundamental effects of inner diameter on them have not been fully understood, which are essential to the design and optimization of the OHP in real applications. Therefore, by combining the high-speed visualization method and infrared thermal imaging technique, the fluid flow and thermal performance in the OHPs with inner diameters of 1, 2 and 3 mm are presented and analyzed. The results indicate that three fluid flow motions, including small oscillation, bulk oscillation and circulation, coexist or, respectively, exist alone with the increasing heating load under different inner diameters, with three flow patterns occurring in the OHPs, viz. bubbly flow, slug flow and annular flow. These fluid flow motions are closely correlated with the heat and mass transfer performance in the OHPs, which can be reflected by the characteristics of infrared thermal images of condensers. The decrease in the inner diameter increases the frictional flow resistance and capillary instability while restricting the nucleate boiling in OHPs, which leads to a smaller proportion of bubbly flow, a larger proportion of short slug flow, a poorer thermal performance, and easier dry-out of working fluid. In addition, when compared with the 2 mm OHP, the increasing role of gravity induces the thermosyphon effect and weakens the ‘bubble pumping’ action, which results in a little smaller and bigger thermal resistances of 3 mm OHP under small and bulk oscillation of working fluid, respectively.

Fractal geometry (fractional Brownian motion—FBM) is introduced to characterize the pore distribution of porous material. Based on this fractal characterization, a mathematical model of heat conduction is presented to study heat conduction behaviors in porous material with a focus on effective thermal conductivity. The role of pore structure on temperature distribution and heat flux is examined and investigated for fractal porous material. In addition, the effects of fractal dimension, porosity, and the ratio of solid-matrix-to-fluid-phase thermal conductivity (k_s/k_f) on effective thermal conductivity are evaluated. The results indicate that pore structure has an important effect on heat conduction inside porous material. Increasing porosity lowers thermal conductivity. Even when porosity remains constant, effective thermal conductivity is affected by the fractal dimensions of the porous material. For porous material, the heat conduction capability weakens with increased fractal dimension. Additionally, fluid-phase thermal conduction across pores is effective in porous material only when k_s/k_f < 50. Otherwise, effective thermal conductivity for porous material with a given pore structure depends primarily on the thermal conductivity of the solid matrix.

Size-controllable micro-droplets obtained in microfluidic cross-junctions are significant in microfluidics. Modeling and predictions in microfluidic-based droplet formation characteristics to date using various traditional theoretical or empirical correlations are far from satisfactory. Driven by unprecedented data volumes from microfluidic experiments and simulations, statistical learning can offer a powerful technique to extract data that can be interpreted into underlying fluid physics and modeling. This Letter historically combines the current experimental data and experimental/numerical data from previous publications as a microfluidics-based droplet formation characteristics database. Two supervised statistical learning algorithms, deep neural network and factorization-machine-based neural network (Deep-FM), were established to model and predict the formed droplet size in microfluidic cross-junctions. As a newly developed statistical learning code in 2017, the Deep-FM manifests a better prediction performance, where the average relative error was only 4.09% and nearly 98% of the data points had individual relative errors of 10% or less. Such high accuracy can be attributed to the outstanding interactions between high-order and low-order features of the Deep-FM framework. Another innovation in this Letter lies in the training dataset shrinkage and optimization without sacrificing the prediction accuracy. Such a method pioneers statistical learning algorithms in small-sample modeling problems, which is different from big data modeling and analyses. The improved statistical learning proposed in this Letter provides universal high-accuracy modeling for microfluidic-based droplet characteristics prediction, which can be an influential data-processing framework that can boost and probably transform current lines of microfluidic physics research and industrial applications.