Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode Ana M. Lazarevska Faculty of Mechanical Engineering, Skopje University.

Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode
Ana M. Lazarevska Faculty of Mechanical Engineering, Skopje University “Sv. Kiril i Metodij”, Skopje, R. Macedonia 12/2/2018

Comparative Analysis of Parameters obtained while Simulating an APE
Overview Application areas of Air Quality, Pollutant Dispersion and Transport Models Problem Description Engaged set of software tools: preprocessors, models, postprocessors, graphical visualization Necessary input data for simulating the air pollution episode (APE) Results Conclusion 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

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 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Problem Description Simulation of an SO2 air pollution episode Analyzed Region – Grid, Mesh 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 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Simulation of an SO2 air pollution episode Analyzed region and episode - selection criteria Skopje is the capitol of the R. Macedonia, (30% of the population, main industrial center) concentration of polluters represent of APEs Simultaneous existence of min.necessary input data emission parameters meteorological parameters receptor data geo-physical parameters 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Simulation of an SO2 air pollution episode Selection of a simulation tool CALPUFF approach – multi layer, multi species, non-steady-state puff dispersion model simulates effects of varying (xi,t) meteorological conditions on pollutant transport, transformation and removal – 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.) 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

C(s) ground level concentration [g/m3]. Q(s) pollutant mass in the puff [g]. sx,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]. 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Simulation of an SO2 air pollution episode Selection of a simulation tool (cont.) 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. in order to allow comparison – “try-to-imitate” CALMET/CALPUFF approach 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Simulation of an SO2 air pollution episode Selection of a simulation tool (cont.) – grid – horizontally / vertically – same distancing – fields of meteorological parameters modeled, as much as the solver allows, similarly to the approach in CALMET (“hour–by–hour”) – boundary conditions – main flow: vel. & press. inlet – 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) step: solution of the main flow step: emissions modeled as point injections 3 step: coupling with the continuous phase (possible) 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

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-e Model Gk generation of turbulence kinetic energy due to mean velocity gradients Gb generation of turbulence kinetic energy due to buoyancy YM contribution of fluctuating dilatation in compressible turbulence to overall dissipation rate, C1e, C2e, C3e constants. sk, se turbulent Prandtl numbers for k and e, respectively. Sk, Se user-defined source terms. 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

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. 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) ep particle emissivity (dimensionless) s Stefan-Boltzmann constant ( W/m2 K4) qR radiation temperature, 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Geophysical Preprocessor GAMBIT
PRTMET Meteorological postprocessor CALPOST Postprocessor CALPUFF Dispersion model Meteorological and Geophysical Preprocessors Excel & VBasic CALMET Meteorological model MATLAB Statistics, Analyze Graphical visualization, Animation Meteorological boundary conditions (main flow) FLUENT Solution of the main flow FLUENT Solution of the discrete phase FLUENT Analyze Graphical visualization, Animation, Statistics 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

FLUENT and CALPUFF Mesh of the Region 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Simulation of an SO2 air pollution episode Necessary input data for simulating the APE 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 5. Mixture properties - species (FLUENT) 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

z [m] 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Postprocessing 1. 3D hourly fields of meteorological data p, r, T, |u|, u(direction), r[%] 2. ground concentrations of modeled species 3. Development of the APE 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Jul 12, 2004 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Results 1. Formation and Development Trends of the APE are maintained 2. Performance Comparison: FLUENT vs. CALPUFF 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 FLUENT c. 3D field of species fraction - Disadvantages: selecting/tuning the proper model in FLUENT might turn out to be a time consuming and difficult task 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Conclusion The comparative analysis implies a possibility of supplementing the both software packages, aiming a better quality of the output 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 The outcome certainty is a function of the input parameters quality 12/2/2018 Comparative Analysis of Parameters obtained while Simulating an APE

Thank you for your attention
12/2/2018

Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode Ana M. Lazarevska Faculty of Mechanical Engineering, Skopje University.

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Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode Ana M. Lazarevska Faculty of Mechanical Engineering, Skopje University.

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Presentation on theme: "Comparative Analysis of Parameters obtained while Simulating an Air-Pollution Episode Ana M. Lazarevska Faculty of Mechanical Engineering, Skopje University."— Presentation transcript:

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