Transient Response of Particle Distribution in a Chamber to Transient Particle Injection

2009 ◽  
Vol 26 (4) ◽  
pp. 199-209 ◽  
Author(s):  
Ning Zhang ◽  
Zhongquan Zheng ◽  
Steven Eckels ◽  
Venkata B. Nadella ◽  
Xiaoyang Sun
Keyword(s):  
Transient Response ◽  
Particle Distribution ◽  
Particle Injection

Study on flow field of gas–solid two-phase pulsed jet

2019 ◽  
Vol 33 (24) ◽  
pp. 1950279
Author(s):  
Xinhua Song ◽  
Xiaojie Li ◽  
Yang Wang ◽  
Honghao Yan
Keyword(s):  
Numerical Simulation ◽  
Flow Field ◽  
Particle Distribution ◽  
Starch Granule ◽  
Injection Pressure ◽  
Two Phase ◽  
Particle Injection ◽  
Pulsed Jet ◽  
Simulation Based ◽  
Simulation Results

In this paper, a computational fluid dynamics–discrete element method (CFD–DEM) coupling method is established to simulate the starch granule injection by coupling CFD and DEM. Then a gas–solid two-phase pulsed jet system is designed to capture the flow field trajectory of particle injection (colored starch with a mean diameter of 10.67 [Formula: see text]m), and the image is processed by color moment and histogram. Finally, the simulation results are compared with the experimental results, and the following conclusions are drawn. The numerical simulation results show that with the increase of injection pressure, the injection height increases gradually. When the injection pressure reaches above 0.4 MPa, the increase of injection height decreases. The experimental images show that the larger the pressure (i.e., the greater the initial velocity), the faster the velocity of particle distribution in the space, and the injection heights with the injection pressures of 0.4 MPa and 0.5 MPa are close, which is consistent with the result from the FLUENT numerical simulation based on CFD–DEM.

Particle Deposition in a Room-Scale Chamber With Particle Injection

2005 ◽  
Author(s):  
N. Zhang ◽  
Z. Charlie Zheng ◽  
L. Glasgow ◽  
B. Braley
Keyword(s):  
Particle Deposition ◽  
Particle Number ◽  
Particle Distribution ◽  
Turbulent Transport ◽  
Distribution Patterns ◽  
Particle Number Density ◽  
Number Density ◽  
Small Particles ◽  
Particle Injection ◽  
Simulation Results

A model simulating the deposition of small particles with turbulent transport, sedimentation, and coagulation, is presented. Experimental measurements were conducted in a room-scale chamber using a specially designed sequential sampler. The measured deposition-rate data are compared with the simulation results. Distributions of particle-number density at different times are plotted in several viewing planes to facilitate discussion of the particle distribution patterns.

scholarly journals Optimization of the Tracer Particle Addition Method for PIV Flowmeters

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1614
Author(s):  
Yilong Qiu ◽  
Huiyu Chen ◽  
Wangxu Li ◽  
Feng Wu ◽  
Zhenggui Li
Keyword(s):  
Natural Gas ◽  
Flow Field ◽  
Measurement Accuracy ◽  
Particle Distribution ◽  
Tracer Particle ◽  
Distribution Area ◽  
Field Fluctuation ◽  
Particle Injection ◽  
Injection Flow ◽  
Tracer Particles

When a PIV flowmeter is used to measure a large flow of natural gas, the flow field fluctuation and particle distribution have a significant influence on the measurement accuracy and the particle injection mode plays a key role in the flow field fluctuation and particle distribution. To improve the measurement accuracy of PIV flowmeters, the method of filling tracer particles in single pipes, multiple pipes, and L pipes of a natural gas DN100 pipeline under high-pressure working conditions was compared and analyzed through numerical calculation and testing. The results show that the disturbance distance of filling particles in L pipes was the shortest, but the particle distribution area was small, whereas the flow metering error was large. By shortening the intersection distance between the L tube injection flow field and the main flow field, the problem that the particles failed to fill the test area was effectively solved, and the peak turbulence intensity at the intersection of the flow field decreased from 13.4% to 8%. Furthermore, the optimized structure was used to measure a flow of 100–600 m3/h with different flow rates. The relative error between the flowmeter and the ultrasonic flowmeter was approximately 2%, and the metering deviation was significantly improved.

Computational Study of the Effects of Particle Size, Particle Injection Configuration, and Operating Pressure Gradient on Turbulent Dispersion of Spherical Micron-Sized Particles in a Generic Mockup Aircraft Cabin

2012 ◽  
Author(s):  
Khosrow Ebrahimi ◽  
Zhongquan C. Zheng ◽  
Mohammad H. Hosni
Keyword(s):  
Particle Concentration ◽  
Particle Distribution ◽  
Computational Study ◽  
Turbulent Dispersion ◽  
Optimum Number ◽  
Airflow Velocity ◽  
Particle Injection ◽  
Aircraft Cabin ◽  
Gauge Pressure ◽  
Turbulent Airflow

Computational study of dispersion of particles is one way to evaluate the spread of contaminants and viruses amongst occupants of an enclosure, such as an aircraft cabin. In this investigation, the turbulent dispersion of particles in a ventilated generic cabin is studied. The generic cabin resembles one-half of a Boeing 767-300 aircraft cabin. In the first phase, the turbulent dispersion of particles injected through stainless steel straight vertical tube is simulated. A Lagrangian approach is used to predict the particle concentration in specified monitoring location inside the cabin. The steady RANS solutions for the airflow velocity data are used to initialize the particle-tracking calculations through the Discrete Phase Model (DPM). To calculate the effects of turbulence on the dispersion behavior of particles, a Discrete Random Walk (DRW) model is employed. The particle concentration field under steady-state, zero-gauge-pressure conditions for 3 μm and 10 μm spherical liquid particles are calculated. Through the comparisons between the measured and the calculated particle concentration data for the two examined sizes of mono-disperse particles, the effect of particle size on distribution behavior of micron-sized particles is investigated and discussed. In the second phase, in order to reduce the effect of initial injection velocity for 10 μm particles on their distribution, the straight injection tube is replaced by a cone diffuser while maintaining the upstream primary flow conditions. Using the same RANS model and under the new particle injection configuration, the characteristics of turbulent airflow in the cabin are found to be very similar to those of turbulent airflow without particle injection. A grid independency study is performed for the airflow velocity data prior to validation of the particle distribution results. The steady-state DPM simulations are performed initially for the zero-gauge-pressure condition and then the effect of pressurizing the cabin on particle distribution is investigated by increasing the gauge-pressure up to 0.025 inches of water. Through a detailed study, carried out to obtain an optimum number for the number of tries in the DRW, it is realized that the optimum number of tries is 175 for both cases of pressurized and non-pressurized cabin.

Separable cortico-basal ganglia networks support self-imposed transient response inhibition

2012 ◽  
Vol 43 (01) ◽  
Author(s):  
A Franz ◽  
O Granert ◽  
M Rijntjes ◽  
HR Siebner ◽  
C Weiller ◽  
...  
Keyword(s):  
Basal Ganglia ◽  
Transient Response ◽  
Response Inhibition

Transient Response of Electrostatic Field due to Collision ESD between Spherical Electrodes by using Bare-chip Optical Electric Field Sensor

2020 ◽  
Vol 140 (12) ◽  
pp. 599-600
Author(s):  
Kento Kato ◽  
Ken Kawamata ◽  
Shinobu Ishigami ◽  
Ryuji Osawa ◽  
Takeshi Ishida ◽  
...  
Keyword(s):  
Electric Field ◽  
Transient Response ◽  
Electrostatic Field ◽  
Electric Field Sensor ◽  
Field Sensor

Characteristic Analysis of a Micro DC-DC Converter for Portable Electronic Devices and an Improvement of Transient Response Characteristic

2008 ◽  
Vol 128 (12) ◽  
pp. 1373-1380
Author(s):  
Satoshi Sugahara ◽  
Kouhei Yamada ◽  
Haruhiko Nishio ◽  
Masaharu Edo ◽  
Toshiro Sato ◽  
...  
Keyword(s):  
Transient Response ◽  
Electronic Devices ◽  
Response Characteristic ◽  
Characteristic Analysis
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