Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode

04/19/23

Comparative Analysis of Parameters obtained while

Simulating an Air-Pollution Episode

Ana M. LazarevskaFaculty of Mechanical Engineering, Skopje University “Sv. Kiril i Metodij”, Skopje, R.

[email protected]

04/19/23 Comparative Analysis of Parameters obtained while Simulating an APE

Overview

Application areas of Air Quality, Pollutant Dispersion and Transport Models

Necessary input data for simulating the air pollution episode (APE)

Engaged set of software tools:preprocessors, models, postprocessors, graphical visualization

Conclusion

Problem Description

Results


Application areas of Air Quality, Pollutant Dispersion and Transport Models

1. Regulatory purposes - issuing emission permits

2. Policy support -- air quality assessment studies- forecasting the effect of abatement measures - combined use of AQM with other environmental models

3. Public information - - on-line information

- possible occurrence of smog episodes - on-line forecasting - reciprocal exchange of smog information between countries

4. Scientific research - - description of dynamic effects

- simulation of complex chemical processes involving air pollutants. - for practical applications - high requirement o computational effort

diagnosis, analysis and prognosis


Analyzed Region – Grid, Mesh

Simulation of an SO2 air pollution episode

Selection of an episode for simulation Selection of a simulation tool – CALPUFF vs. FLUENT Numerical simulation of the selected air pollution episode over the region of interest (FLUENT, CALPUFF) Comparative analysis of the parameters engaged

Problem Description


Skopje is the capitol of the R. Macedonia, (30% of the population, main industrial center) - concentration of polluters - represent of APEs

Simulation of an SO2 air pollution episodeAnalyzed region and episode - selection criteria

Simultaneous existence of min.necessary input data

– emission parameters– meteorological parameters – receptor data

– geo-physical parameters


– contains algorithms/modules for near-source effects (building downwash, transitional plume rise, sub-grid scale terrain interactions), longer range effects (pollutant removal, chemical transformation, over-water transport etc.)

– multi layer, multi species, non-steady-state puff dispersion model - simulates effects of varying (xi,t) meteorological conditions on pollutant transport, transformation and removal

Simulation of an SO2 air pollution episodeSelection of a simulation tool

CALPUFF approach


2

2

2

2

222 y

c

x

a

yx

ddg

sQsC

expexp

)()(

n

n z

e

z

nhHg

2

2

21 2

2

2

2

)(

exp)( /

C(s) ground level concentration [g/m3]. Q(s) pollutant mass in the puff [g]. x,y,z(s) standard deviation of the Gaussian distribution (along/across wind and vertical direction) [m] da,c(s) distance from puff center to the receptor (along / across wind direction) [m]g(s) vertical term (multiple reflection from top of ML and surface)

He puff center's effective height above ground [m]. h ML height [m].


FLUENT provides comprehensive modeling capabilities for – incompressible and compressible fluid flow problems, – laminar and turbulent fluid flow problems. – Steady-state or transient analyses– models for transport phenomena (heat transfer and chemical reactions) – combined with ability to model complex geometries.

Simulation of an SO2 air pollution episodeSelection of a simulation tool (cont.)

in order to allow comparison – “try-to-imitate” CALMET/CALPUFF approach


– modeling of pollution transport (here w.o. chem.reac.) – species transport with Discrete Phase Model (DPM), which performs Lagrangian trajectory calculations for dispersed phases (particles, droplets, or bubbles). 1 step: solution of the main flow 2 step: emissions modeled as point injections

3 step: coupling with the continuous phase (possible)

– boundary conditions – main flow: vel. & press. inlet

– fields of meteorological parameters modeled, as much as the solver allows, similarly to the approach in CALMET (“hour–by–hour”)

Simulation of an SO2 air pollution episodeSelection of a simulation tool (cont.)

– grid – horizontally / vertically – same distancing


Mass Conservation Equation Sm mass added to the continuous phase from the dispersed second phase and any user-defined sources.

Transport Equations for the Standard k- Model

Gk generation of turbulence kinetic energy due to mean velocity gradientsGb generation of turbulence kinetic energy due to buoyancy YM contribution of fluctuating dilatation in compressible turbulence to overall dissipation rate,C1, C2, C3 constants. k, turbulent Prandtl numbers for k and , respectively. Sk, S user-defined source terms.


Heat Transfer to the Droplet

Cp droplet heat capacity (J/kg-K) Tp droplet temperature (K) h convective heat transfer coefficient (W/m2 K)T temperature of continuous phase (K)dmp/dt rate of evaporation (kg/s)hfg latent heat (J/kg)p particle emissivity (dimensionless) Stefan-Boltzmann constant (5.67 10-8 W/m2 K4) R radiation temperature,

Species Transport Equations

Yi local mass fraction of each species, Ri net rate of production by chemical reaction Si rate of creation by addition from the dispersed phase plus any user-defined sources.


PRTMETMeteorological postprocessor

CALPOSTPostprocessor

CALPUFFDispersion model

Meteorological and Geophysical Preprocessors

Excel & VBasic

CALMETMeteorological

model

MATLABStatistics, Analyze

Graphical visualization, Animation

FLUENTSolution of the discrete phase

FLUENTSolution of the

main flow

Geophysical PreprocessorGAMBIT

Meteorological boundary conditions

(main flow)

FLUENTAnalyze

Graphical visualization, Animation, Statistics


FLUENT and CALPUFF Mesh of the Region


1. Geophysical - surface elevation, LUC, surface roughness

2. Meteorological - surface and upper air soundings - modeled surface and upper air data

3. Emission data - flow and geometry properties modeled emission data

4. Receptor data - ground concentrations

Simulation of an SO2 air pollution episodeNecessary input data for simulating the APE

5. Mixture properties - species (FLUENT)


z [m]


2. ground concentrations of modeled species

1. 3D hourly fields of meteorological datap, , T, |u|, u(direction), r[%]

3. Development of the APE

Postprocessing


Velocity Vectors Colored By Static Temperature (k)FLUENT 6.0 (3d, segregated, lam)

Oct 31, 2002

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

2.77e+02

ZY X

Jul 12, 2004


Jul 12, 2004


Ground concentrations at receptors [g/m3]

0

200

400

600

800

1000

1200

1400

1600

1800

2000

12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12

Sep10

2000

Sep10

2000

Sep10

2000

Sep10

2000

Sep10

2000

Sep10

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Sep10

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Sep10

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Sep10

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Sep10

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Sep10

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Sep10

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Sep11

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Sep11

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Sep11

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Sep11

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Sep11

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Sep11

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Sep11

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Sep11

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Sep11

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gro

un

d c

on

cen

trat

ion

s

CALPUFF S1 CALPUFF S2 CALPUFF S3 CALPUFF S4

FLUENT S1 FLUENT S2 FLUENT S3 FLUENT S4


1. Formation and Development Trends of the APE are maintained

-Advantages: a. aside of the geometry/mesh preprocessor GAMBIT, FLUENT alone conducts the complete calculation of flow and species parameters b. Particle tracking avlb. within FLUENTc. 3D field of species fraction- Disadvantages: selecting/tuning the proper model in FLUENT might turn out to be a time consuming and difficult task

Results

2. Performance Comparison: FLUENT vs. CALPUFF


Conclusion

1. The comparative analysis implies a possibility of supplementing the both software packages, aiming a better quality of the output

2. However, due to the poor quality of input parameters, the analysis shows that formation and development of an APE can be predicted only qualitatively, i.e. only notification of existence / prediction of an APE

3. The outcome certainty is a function of the input parameters quality

04/19/23Thank you for your attention

Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode

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