Mehrphasensimulationen zur Charakterisierung einer Aerosol-Teilprobenahme

Transcription

1 Mehrphasensimulationen zur Charakterisierung einer Aerosol-Teilprobenahme G. Lindner 8.41, Modellierung und Simulation February 29, 2016

2 Review

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4 CFD Study for the Design of the Dilution Section The mixing behaviour described by a scalar ξ depends on angle of inclination Ξ x2x200 lmin zm 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.

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6 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 concentration range and temporal stability

7 Objective - Hyperkinetic Aerosol Inline Sampling The aerosol sampling is sucked at higher stream velocity - The flow velocity in the main pipe inferior to the flow velocity in the sampling tube, this regime is defined as hyperkinetic Estimate the amount of soot particles which are trapped by probe section (aspiration- or collection efficiency) Is the size distribution of the particle collective influenced? Reconstruction of Particle Number Distributions on different interfaces of the computational domain

8 Boundary Physics, Carrier Fluid Q in l/min, Q so = 4 l/min, u in m/s (main pipe) Q ex = Q dil + Q ca + Q so Equilibrium mass fraction: ξ = Q ca /(Q dil + Q ca ) Q dil Q ca Q ex ξ u ch Re ch u so Q ca /Q dil # / % # / %

9 Insight of the Computational Domain

10 Uncoupled Discrete Phase Model (DPM) Homogeneous MUSIG Model Unified Treatment of Discrete Data Discrete value pairs from experiment/simulation Diameter and its concentration: (Dp i, C i ), i = Log-normal distribution Density estimation by Histogram Kernel density estimate (Kerndichteschätzer) C E06cm 3 C E06cm D p Σ D p nm Σ D p D p nm

11 Uncoupled Discrete Phase Model (DPM) Homogeneous MUSIG Model Unified Treatment of Discrete Data Discrete value pairs from experiment/simulation Diameter and its concentration: (Dp i, C i ), i = Log-normal distribution Density estimation by Histogram Kernel density estimate (Kerndichteschätzer) 1.) Nonlinear model fit Levenberg-Marquardt nlm = a exp b2 ( c+x) 2 to estimate D p, and 2.) Gaussian peak fit nlm = C + a exp to estimate σ, (µ). ( x µ) 2 2 σ 2

12 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 situation the carrier fluid is allowed to influence trajectories but particles do not affect the fluid drag forces, turbulent dispersion

13 Uncoupled Discrete Phase Model (DPM) Homogeneous MUSIG Model Particle Injection on side inlet Mass flow rate ṁ = A ρu da = 0.1g/h Zero Slip causes the particles to be injected at the local fluid velocity of the coupled continuous phase. Particle locations are equally spaced tracks should be integrated Taking discrete diameter distribution from experiment

14 Uncoupled Discrete Phase Model (DPM) Homogeneous MUSIG Model Modeling the Phase Coupling The equation of motion reads to m p dv dt = F d + F B + F vm +... The drag force F D experienced by a spherical particle of diameter D p is given by F d = π 8 ρ C D D 2 p u v (u v) and C D where v is the velocity of particles, ρ the density of carrier fluid, u is the carrier fluid velocity in steady state and C D is the drag coefficient with 24( Re ) Re 1000 C D = Re 0.44 Re > (Schiller & Naumann) - assume particle is non-rotating solid sphere

15 Uncoupled Discrete Phase Model (DPM) Homogeneous MUSIG Model Homogeneous MUSIG Model MUSIG (Multiple Size Group) Model is an Euler-Euler approach Primary developed for polydispersed bubbly flows Population balance is a well-established method for calculating the size distribution Particle size classes share the same velocity field Computational expensive, + 21 size fraction equations Breakup-/Coalescence-Terms are excluded

16 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs

17 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Particle tracking can trace the flow behavior

18 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Particle tracking can trace the flow behavior

19 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Particle tracking is used to display turbulence induced properties such as recirculation

20 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Aspiration Efficiency η and Particle Number Rate pr Η pr so pr ca Q so l min 1 23 nm 24 lmin 55 nm 24 lmin 23 nm 48 lmin 55 nm 48 lmin The development of pr so on sample out divided by pr ca on inlet (100 %) for a flow rate of Q dil = Q ca = 24 l/min respectively 48 l/min are shown. Additional two size distribution, 23 nm and 55 nm are included.

21 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Comparison of PAGVs on different Locations ca Injection region, so Sample out, ex Exhaust

22 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Discrete Phase Model (DPM) Normal Distribution D p = 160 nm, σ = 1.6 Density function Cumulative density function pdf ca si so ex cdf ca si so ex D p nm D p nm location D p nm Σ nm mode nm ca ex so

23 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Discrete Phase Model (DPM) Discrete Diameter Distribution D p = 59.5 nm, σ = 1.44 Density function Cumulative density function pdf D p nm 4 ca si so ex cdf D p nm location D p nm Σ nm mode nm case ca ex so ca si so ex

24 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Homogeneous MUSIG Density function pdf Discrete Diameter Distribution D p = 59.5 nm, σ = D p nm 4 ca si so ex Cumulative density function cdf location D p nm Σ nm mode nm D p nm case ca ex so ca si so ex

25 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Homogeneous MUSIG D p = 59.5 nm, σ = 1.44, Coagulation Rate of 3.5E-16 m 3 s 1 Density function Cumulative density function pdf D p nm 4 ca si so ex cdf D p nm location D p nm Σ nm mode nm case ca ex so ca si so ex

26 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Homogeneous MUSIG, analyzing size fractions On Injection region size fraction f size was initialized to: f size = for 16.2 nm (class 9 ) f size = for 57.2 nm (class 15 ) Resulting f size of size groups on: Sampleout(so) 29.5 f size 16.2 nm f size 57.2 nm

27 Particle Diagnostics Aspiration Efficiency η Comparison of PAGVs Homogeneous MUSIG, analyzing size fractions Discrete Diameter Distribution D p = 59.5 nm, σ = 1.44 Resulting f size of size groups SG i, i = ( ) on Outlet (ex) fsize SG i

28 Strömungsverhältnisse in komplexer Geometrie konnten in hoher Auflösung erfasst werden Ein polydisperses Gas-Feststoffgemisch wird duch eine Mehrphasenströmung beschrieben Modellierung der Kraftwirkungen durch Widerstandskraft und turbulenter Dispersion auf das Partikel-Kollektiv Rekonstruktion von Partikelgrössenverteilungen (PAGVs) Aufgrund der Homogenität (Rückwirkungsfreiheit) sind nur geringe Veränderungen der Verteilungen im Rechengebiet festgestellt worden MUSIG liefert genauere Ergebnisse bei der Rekonstruktion von PAGVs