On the aerodynamic fog collection efficiency of fog water collectors via three-dimensional numerical simulations

2020 ◽  
Vol 245 ◽  
pp. 105123
Author(s):  
Danilo Carvajal ◽  
Luis Silva-Llanca ◽  
Dante Larraguibel ◽  
Bastián González
Keyword(s):  
Numerical Simulations ◽  
Collection Efficiency ◽  
Three Dimensional ◽  
Fog Water ◽  
Fog Collection

scholarly journals Aerodynamics-assisted, efficient and scalable kirigami fog collectors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jing Li ◽  
Ranjiangshang Ran ◽  
Haihuan Wang ◽  
Yuchen Wang ◽  
You Chen ◽  
...  
Keyword(s):  
Collection Efficiency ◽  
Three Dimensional ◽  
Surface Characteristics ◽  
Water Shortage ◽  
Viscous Drag ◽  
Flow Forming ◽  
Low Wind Speed ◽  
Fog Collection ◽  
Outdoor Setting ◽  
Structural Length

AbstractTo address the global water shortage crisis, one of the promising solutions is to collect freshwater from the environmental resources such as fog. However, the efficiency of conventional fog collectors remains low due to the viscous drag of fog-laden wind deflected around the collecting surface. Here, we show that the three-dimensional and centimetric kirigami structures can control the wind flow, forming quasi-stable counter-rotating vortices. The vortices regulate the trajectories of incoming fog clusters and eject extensive droplets to the substrate. As the characteristic structural length is increased to the size of vortices, we greatly reduce the dependence of fog collection on the structural delicacy. Together with gravity-directed gathering by the folds, the kirigami fog collector yields a collection efficiency of 16.1% at a low wind speed of 0.8 m/s and is robust against surface characteristics. The collection efficiency is maintained even on a 1 m2 collector in an outdoor setting.

scholarly journals Importance of Body Stance in Fog Droplet Collection by the Namib Desert Beetle

Biomimetics ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 59 ◽  
Author(s):  
Unmeelan Chakrabarti ◽  
Roberto Paoli ◽  
Souvick Chatterjee ◽  
Constantine M. Megaridis
Keyword(s):  
Gas Flow ◽  
Volume Fraction ◽  
Collection Efficiency ◽  
Three Dimensional ◽  
Stream Velocity ◽  
Feedback Effects ◽  
Carrier Fluid ◽  
Microphysical Properties ◽  
Fog Collection ◽  
Biomimetic Approach

The fog-basking behavior of the Onymacris unguicularis, a beetle species living in the coastal regions of the Namibian desert, has recently caught the attention of the engineering community, as suggesting a viable biomimetic approach to address the problem of harvesting water in arid regions of the globe. Previous research has focused on observation and analysis of the beetle’s elytron properties and how these affect fog-collection rates. The head stance taken by the Onymacris unguicularis when fog basking is well documented. However, how this stance affects droplet collection has not been studied up to now. The present paper addresses this problem from a computational fluid dynamics perspective, where three-dimensional numerical simulations are used to characterize the fog flow properties around a simplified geometry mimicking the beetle’s body. The simulations employ two-way coupling between the gas flow and the dispersed fog phase to account for feedback effects of fog droplets on the carrier fluid (air), and assume that droplets are captured after hitting the elytron surface. The study considers several combinations of free-stream velocity and droplet volume fraction. The analysis reveals that there is a range of head-stance angles, corresponding to an inclination of the beetle between 35 deg and 45 deg with respect to the horizon, that maximizes water collection on the beetle’s back, in qualitative agreement with observations in nature and laboratory experiments. A rationale is proposed to explain this phenomenon, finding that the specific head stance corresponds to the maximum residence time of fluid particles above the beetle’s elytron surface. This, in turn, designates the maximum likelihood for water droplets to be captured in the boundary layer developing over the beetle and subsequently hit the surface where they get captured. The results reveal the importance of the fluid flow pattern around the beetle’s body in addition to the microphysical properties of the elytron when reliable predictions of the water droplet collection efficiency are sought.

Two-Photon Excitation Imaging of Glucose Metabolism in Living Tissue

1997 ◽  
Vol 3 (S2) ◽  
pp. 305-306
Author(s):  
David W. Piston
Keyword(s):  
Confocal Microscopy ◽  
Collection Efficiency ◽  
Three Dimensional ◽  
Spatial Filter ◽  
Input Power ◽  
Photon Absorption ◽  
Focal Volume ◽  
Two Photon ◽  
Photon Excitation ◽  
Two Photon Excitation

Two-photon excitation microscopy (TPEM) provides attractive advantages over confocal microscopy for three-dimensionally resolved fluorescence imaging and photochemistry. It provides three-dimensional resolution and eliminates background equivalent to an ideal confocal microscope without requiring a confocal spatial filter, whose absence enhances fluorescence collection efficiency. This results in inherent submicron optical sectioning by excitation alone. In practice, TPEM is made possible by the very high local instantaneous intensity provided by a combination of diffraction-limited focusing of a single laser beam in the microscope and the temporal concentration of 100 femtosecond pulses generated by a mode-locked laser. Resultant peak excitation intensities are 106 times greater than the CW intensities used in confocal microscopy, but the pulse duty cycle of 10−5 limits the average input power to less than 10 mW, only slightly greater than the power normally used in confocal microscopy. Because of the intensity-squared dependence of the two-photon absorption, the excitation is limited to the focal volume.

scholarly journals Three-Dimensional Numerical Simulations of Twisted Stacked Tape Cables

2015 ◽  
Vol 25 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Philipp A. C. Kruger ◽  
Victor M. R. Zermeno ◽  
Makoto Takayasu ◽  
Francesco Grilli
Keyword(s):  
Numerical Simulations ◽  
Three Dimensional

Ice-Phase Precipitation

2017 ◽  
Vol 58 ◽  
pp. 6.1-6.36 ◽  
Author(s):  
I. Gultepe ◽  
A. J. Heymsfield ◽  
P. R. Field ◽  
D. Axisa
Keyword(s):  
Collection Efficiency ◽  
Numerical Models ◽  
Mass Density ◽  
Three Dimensional ◽  
Phase Precipitation ◽  
Microphysical Parameters ◽  
Mass Size ◽  
Ice Water ◽  
Ice Phase

AbstractIce-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.

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