Partial Flow System Methods Results Conclusion Mehrphasensimulationen zur Charakterisierung einer Aerosol-Teilprobenahme G. Lindner 8.41, Modellierung und Simulation February 29, 2016
Partial Flow SystemMethods
ResultsConclusion
Mehrphasensimulationen zur Charakterisierung
einer Aerosol-Teilprobenahme
G. Lindner
8.41, Modellierung und Simulation
February 29, 2016
Partial Flow SystemMethods
ResultsConclusion
Review
Partial Flow SystemMethods
ResultsConclusion
Partial Flow SystemMethods
ResultsConclusion
CFD Study for the Design of the Dilution Section
The mixing behaviour described by a scalar ξ depends onangle of inclination
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0.2 0.4 0.6 0.8 1.0 1.2 1.40.0
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z � m
DΞ
400x2x200 l�min
í 130°á 120°ç 115°ô 110°ò 100°ì 90°à 75°æ 60°
G. Lindner et al.: A Computational Fluid Dynamics Study on the Gas Mixing
Capabilities of a Multiple Inlet System.,J. Fluids Eng, 138(3), 2015.
Partial Flow SystemMethods
ResultsConclusion
Partial Flow System
Partial Flow SystemMethods
ResultsConclusion
Scheme of Facility of Aerosol Conditioning (ENV02)
A Mini-CAST (Jing Ltd) to generate ”diesel like” soot aerosols
Provide an ultrafine soot aerosol with high concentrationrange and temporal stability
Partial Flow SystemMethods
ResultsConclusion
Objective - Hyperkinetic Aerosol Inline Sampling
The aerosol sampling is sucked at higher stream velocity - Theflow velocity in the main pipe inferior to the flow velocity in thesampling tube, this regime is defined as hyperkinetic
Estimate the amount of soot particles which are trapped byprobe section (aspiration- or collection efficiency)
Is the size distribution of the particle collective influenced?
Reconstruction of Particle Number Distributions on differentinterfaces of the computational domain
Partial Flow SystemMethods
ResultsConclusion
Boundary Physics, Carrier Fluid
Q in l/min, Qso = −4 l/min, u in m/s (main pipe)
Qex = Qdil + Qca + Qso
Equilibrium mass fraction: ξ∞ = Qca/(Qdil + Qca)
Qdil Qca Qex ξ∞ uch Rech uso Qca/Qdil
# 1 24 24 44 1/2 0.71 1736 5.32 100 %# 2 48 24 68 1/3 1.06 2604 5.32 50 %
Partial Flow SystemMethods
ResultsConclusion
Insight of the Computational Domain
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Unified Treatment of Discrete Data
Discrete value pairs fromexperiment/simulation
Diameter and itsconcentration:(Dpi , Ci) , i = 1 . . . 21
Log-normal distribution
Density estimation by
HistogramKernel density estimate(Kerndichteschatzer)
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5 10 20 500.0
0.1
0.2
0.3
0.4
Dp � nm
CE+
06@ð�c
m3 D
Σ=1.314Dp=14.7135
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5 10 20 50 100 2000.00.20.40.60.81.01.2
Dp � nm
CE+
06@ð�c
m3 D
Σ=1.43961Dp=59.4472
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Unified Treatment of Discrete Data
Discrete value pairs fromexperiment/simulation
Diameter and itsconcentration:(Dpi , Ci) , i = 1 . . . 21
Log-normal distribution
Density estimation by
HistogramKernel density estimate(Kerndichteschatzer)
1.) Nonlinear model fitLevenberg-Marquardt
nlm = a exp−b2(−c+x)2
to estimate Dp, and
2.) Gaussian peak fit
nlm = C + a exp
−(−x − µ)2
2 σ2
to estimate σ, (µ).
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Simplifying Model Assumptions
Reduction of Complexity
Morphologie: Gas-Solid (Particle-laden flow)
The soot loading considered is smaller than 10−7
One-way coupling between gas and particles. In this situationthe carrier fluid is allowed to influence trajectories butparticles do not affect the fluid
drag forces, turbulent dispersion
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Particle Injection on side inlet
Mass flow rate m =∫∫
A ρu dA = 0.1g/h
Zero Slip causes the particles to be injected at the local fluidvelocity of the coupled continuous phase.
Particle locations are equally spaced
2.0 × 105 tracks should be integrated
Taking discrete diameter distribution from experiment
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Modeling the Phase CouplingThe equation of motion reads to mp
dv
dt= Fd + FB + Fvm + . . .
The drag force FD experienced by a spherical particle of diameterDp is given by
Fd =π
8ρ CD D2
p | u − v | (u − v) and CD
where v is the velocity of particles, ρ the density of carrier fluid, u
is the carrier fluid velocity in steady state and CD is the dragcoefficient with
CD =
24(1 + 0.15 Re0.687)
ReRe ≤ 1000 (Schiller & Naumann)
0.44 Re > 1000 .
- assume particle is non-rotating solid sphere
Partial Flow SystemMethods
ResultsConclusion
Uncoupled Discrete Phase Model (DPM)Homogeneous MUSIG Model
Homogeneous MUSIG Model
MUSIG (Multiple Size Group) Model is an Euler-Eulerapproach
Primary developed for polydispersed bubbly flows
Population balance is a well-established method forcalculating the size distribution
Particle size classes share the same velocity field
Computational expensive, + 21 size fraction equations
Breakup-/Coalescence-Terms are excluded
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Results
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Particle tracking can trace the flow behavior
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Particle tracking can trace the flow behavior
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Particle tracking is used to display turbulence induced
properties such as recirculation
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Aspiration Efficiency η and Particle Number Rate pr
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2 3 4 5 6
5
10
15
20
25
30
Qso � l min-1
Η=
prso�
prca%
ç 23 nm 24 l�min
õ 55 nm 24 l�min
ó 23 nm 48 l�min
á 55 nm 48 l�min
The development of prso on sample out divided by prca on inlet(100 %) for a flow rate of Qdil = Qca = 24 l/min respectively48 l/min are shown. Additional two size distribution, 23 nm and55 nm are included.
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Comparison of PAGVs on different Locations
ca Injection region, so Sample out, ex Exhaust
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Discrete Phase Model (DPM)
Normal Distribution Dp = 160 nm, σ = 1.6
Density function
130 140 150 160 170 180 1900.00
0.01
0.02
0.03
0.04
0.05
Dp � nm
ca
sisoex
Cumulative density function
130 140 150 160 170 180 1900.0
0.2
0.4
0.6
0.8
1.0
Dp � nmcd
f
ca
sisoex
location Dp @nmD Σ @nmD mode @nmD
ca 157.1 1.04902 157.4
ex 157.14 1.0492 157.3
so 157.014 1.04833 157.3
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Discrete Phase Model (DPM)
Discrete Diameter Distribution Dp = 59.5 nm, σ = 1.44
Density function
0 50 100 150 2000.000
0.005
0.010
0.015
Dp � nm
ð4ca
sisoex
Cumulative density function
0 50 100 150 2000.0
0.2
0.4
0.6
0.8
1.0
Dp � nmcd
f
ð4ca
sisoex
location Dp @nmD Σ @nmD mode @nmD
case ð4 59.4472 1.43961 54.9
ca 52.6631 1.40523 58.4
ex 52.7621 1.4036 58.4
so 52.2435 1.41437 58.
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Homogeneous MUSIG
Discrete Diameter Distribution Dp = 59.5 nm, σ = 1.44
Density function
0 50 100 150 2000.000
0.005
0.010
0.015
Dp � nm
ð4ca
sisoex
Cumulative density function
0 50 100 150 2000.0
0.2
0.4
0.6
0.8
1.0
Dp � nmcd
f
ð4ca
sisoex
location Dp @nmD Σ @nmD mode @nmD
case ð4 59.4472 1.43961 54.9
ca 59.4468 1.43947 54.8
ex 59.4392 1.44226 55.
so 59.4462 1.4397 55.
√
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Homogeneous MUSIG
Dp = 59.5 nm, σ = 1.44, Coagulation Rate of 3.5E-16 m3 s−1
Density function
0 50 100 150 2000.000
0.005
0.010
0.015
Dp � nm
ð4ca
sisoex
Cumulative density function
0 50 100 150 2000.0
0.2
0.4
0.6
0.8
1.0
Dp � nmcd
f
ð4ca
sisoex
location Dp @nmD Σ @nmD mode @nmD
case ð4 59.4472 1.43961 54.9
ca 59.5142 1.43911 54.9
ex 76.2832 13.5376 71.2
so 75.2995 13.2262 70.2
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Homogeneous MUSIG, analyzing size fractions
On Injection region size fraction fsize was initialized to:fsize = 0.00105 for 16.2 nm (class 9 )fsize = 0.22785 for 57.2 nm (class 15 )
Resulting fsize of size groups on: Sampleout(so)
-202.0 -201.5 -201.0 -200.5-31.5
-31.0
-30.5
-30.0
-29.5
fsize 16.2 nm
0.00112
0.00113
0.00114
-202.0 -201.5 -201.0 -200.5-31.5
-31.0
-30.5
-30.0
-29.5
fsize 57.2 nm
0.22750000
0.22752500
0.22755000
0.22757500
0.22760000
Partial Flow SystemMethods
ResultsConclusion
Particle DiagnosticsAspiration Efficiency η
Comparison of PAGVs
Homogeneous MUSIG, analyzing size fractions
Discrete Diameter Distribution Dp = 59.5 nm, σ = 1.44
Resulting ∆ fsize of size groups SGi , i = (1 . . . 21) on Outlet (ex)
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0 50 100 150 2000.00
0.05
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0.25
SGi
Df s
ize
Partial Flow SystemMethods
ResultsConclusion
Stromungsverhaltnisse in komplexer Geometrie konnten inhoher Auflosung erfasst werden
Ein polydisperses Gas-Feststoffgemisch wird duch eineMehrphasenstromung beschrieben
Modellierung der Kraftwirkungen durch Widerstandskraft undturbulenter Dispersion auf das Partikel-Kollektiv
Rekonstruktion von Partikelgrossenverteilungen (PAGVs)
Aufgrund der Homogenitat (Ruckwirkungsfreiheit) sind nurgeringe Veranderungen der Verteilungen im Rechengebietfestgestellt worden
MUSIG liefert genauere Ergebnisse bei der Rekonstruktion vonPAGVs