Phase discrimination inside a spray: LDV measurements using fluorescent seeding particles (FLDV)

2002 ◽  
pp. 511-525 ◽  
Author(s):  
G. Rottenkolber ◽  
R. Meier ◽  
O. Schäfer ◽  
S. Wachter ◽  
K. Dullenkopf ◽  
...  
Keyword(s):  
Phase Discrimination

scholarly journals Spatial Resolution in ACOM - What Will Come After EBSD

2008 ◽  
Vol 16 (1) ◽  
pp. 34-37 ◽  
Author(s):  
R.A. Schwarzer
Keyword(s):  
Grain Boundaries ◽  
Spatial Resolution ◽  
Crystal Orientation ◽  
High Sensitivity ◽  
Polycrystalline Materials ◽  
Phase Identification ◽  
Specific Level ◽  
Orientation Microscopy ◽  
Orientation Contrast ◽  
Phase Discrimination

Automated Crystal Orientation Microscopy (ACOM) on a grain specific level has proved to be an invaluable new tool for characterizing polycrystalline materials. It is usually based on scanning facilities using electron diffraction , due to its high sensitivity and spatial resolution, but also attempts have been made which rely upon X-ray or hard synchrotron radiation diffraction. The grain orientations are commonly mapped in pseudo-colors on the scanning grid to construct Crystal Orientation Maps (COM), which represent “images” of the microstructure with the advantage of providing quantitative orientation contrast. In a similar way, misorientations across grain boundaries, Σ values of grain boundaries, or other microstructural characteristics are visualized by mapping the grains in the micrograph with specific colors. The principal objectives are the determination of quantitative, statistically meaningful data sets of crystal orientations, misorientations, the CSL character (Σ) of grain boundaries, local crystal texture (pole figures, ODF, MODF, OCF) and derived entities, phase discrimination and phase identification.

Gait Phase Discrimination during Kinematically Constrained Walking on Slackline*

2019 ◽  
Author(s):  
Rajat Emanuel Singh ◽  
Kamran Iqbal ◽  
Safi Ullah ◽  
Ahmed Alazzawi ◽  
Gannon White
Keyword(s):  
Phase Discrimination ◽  
Gait Phase

A Fluidic Technique for Measuring the Average Temperature in a Gas Turbine Exhaust Duct

1968 ◽  
Vol 90 (3) ◽  
pp. 265-270 ◽  
Author(s):  
C. G. Ringwall ◽  
L. R. Kelley
Keyword(s):  
Gas Turbine ◽  
Wave Velocity ◽  
Test Data ◽  
Output Pressure ◽  
Average Temperature ◽  
Phase Discrimination ◽  
Exhaust Duct ◽  
Average Wave ◽  
Gas Turbine Exhaust ◽  
Turbine Exhaust

Circuit concepts and test data for a fluidic system to sense the average temperature in a gas turbine exhaust duct are presented. Phase discrimination techniques are used to sense the average wave velocity in a long tube and to produce an output pressure differential proportional to temperature error.

scholarly journals Temporal limit of phase discrimination in infants

2010 ◽  
Vol 9 (8) ◽  
pp. 1067-1067
Author(s):  
F. Farzin ◽  
S. Rivera ◽  
S. Sakai ◽  
D. Whitney
Keyword(s):  
Phase Discrimination

scholarly journals PHIPS-HALO: the airborne Particle Habit Imaging and Polar Scattering probe – Part 3: Single-particle phase discrimination and particle size distribution based on the angular-scattering function

2021 ◽  
Vol 14 (4) ◽  
pp. 3049-3070
Author(s):  
Fritz Waitz ◽  
Martin Schnaiter ◽  
Thomas Leisner ◽  
Emma Järvinen
Keyword(s):  
Particle Size ◽  
Single Particle ◽  
Large Data ◽  
The Arctic ◽  
Scattering Data ◽  
Scattering Function ◽  
Data Set ◽  
Phase Discrimination ◽  
Angular Scattering

Abstract. A major challenge for in situ observations in mixed-phase clouds remains the phase discrimination and sizing of cloud hydrometeors. In this work, we present a new method for determining the phase of individual cloud hydrometeors based on their angular-light-scattering behavior employed by the PHIPS (Particle Habit Imaging and Polar Scattering) airborne cloud probe. The phase discrimination algorithm is based on the difference of distinct features in the angular-scattering function of spherical and aspherical particles. The algorithm is calibrated and evaluated using a large data set gathered during two in situ aircraft campaigns in the Arctic and Southern Ocean. Comparison of the algorithm with manually classified particles showed that we can confidently discriminate between spherical and aspherical particles with a 98 % accuracy. Furthermore, we present a method for deriving particle size distributions based on single-particle angular-scattering data for particles in a size range from 100 µm ≤ D ≤ 700 µm and 20 µm ≤ D ≤ 700 µm for droplets and ice particles, respectively. The functionality of these methods is demonstrated in three representative case studies.

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