mehrphasensimulationen zur charakterisierung einer aerosol ... · mehrphasensimulationen zur...

Partial Flow SystemMethods

ResultsConclusion

Mehrphasensimulationen zur Charakterisierung

einer Aerosol-Teilprobenahme

G. Lindner

8.41, Modellierung und Simulation

February 29, 2016


ResultsConclusion

Review


ResultsConclusion


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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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.


ResultsConclusion

Partial Flow System


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


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


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 %


ResultsConclusion

Insight of the Computational Domain


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


ResultsConclusion


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).


ResultsConclusion


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


ResultsConclusion


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


ResultsConclusion


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


ResultsConclusion


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


ResultsConclusion

Particle DiagnosticsAspiration Efficiency η

Comparison of PAGVs

Results


ResultsConclusion


Comparison of PAGVs

Particle tracking can trace the flow behavior


ResultsConclusion


Comparison of PAGVs

Particle tracking is used to display turbulence induced

properties such as recirculation


ResultsConclusion


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.


ResultsConclusion


Comparison of PAGVs

Comparison of PAGVs on different Locations

ca Injection region, so Sample out, ex Exhaust


ResultsConclusion


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

pdf

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


ResultsConclusion


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

pdf

ð4ca

sisoex


0 50 100 150 2000.0

0.2

0.4

0.6

0.8

1.0

Dp � nmcd

f

ð4ca

sisoex


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.


ResultsConclusion


Comparison of PAGVs

Homogeneous MUSIG


Density function

0 50 100 150 2000.000

0.005

0.010

0.015

Dp � nm

pdf

ð4ca

sisoex


0 50 100 150 2000.0

0.2

0.4

0.6

0.8

1.0

Dp � nmcd

f

ð4ca

sisoex


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.

√


ResultsConclusion


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

pdf

ð4ca

sisoex


0 50 100 150 2000.0

0.2

0.4

0.6

0.8

1.0

Dp � nmcd

f

ð4ca

sisoex


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


ResultsConclusion


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


ResultsConclusion


Comparison of PAGVs

Homogeneous MUSIG, analyzing size fractions


Resulting ∆ fsize of size groups SGi , i = (1 . . . 21) on Outlet (ex)

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0.25

SGi

Df s

ize


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

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