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Air quality simulation in Gran Canaria using finite element method and experimental validation

This study presents a simulation framework for air quality in Gran Canaria, Canary Islands. The method utilizes finite element modeling and is validated with experimental data. The study focuses on emission and imission data from an emission stack and multiple stations over a three-day period.

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Air quality simulation in Gran Canaria using finite element method and experimental validation

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  1. Simulación de la calidad del aire en la isla de Gran Canaria mediante el método de los elementos finitos y su validación con datos experimentales A. Oliver, R. Montenegro, A. Perez-Foguet, E. Rodríguez, J.M. Escobar, G. Montero Instituto Universitario SIANI Ingeniería Computacional Universidad de Las Palmas de Gran Canaria Laboratori de Càlcul Numèric (LaCàN) Departament de Matemàtica Aplicada III Universitat Politècnica de Catalunya - Barcelonatech

  2. CMN 2013 · Bilbao · 25-28 June · 2 Motivation • Validation of the framework proposed by the authors (Oliver et al. 2013, Energy) • Gran Canaria island (Canary Islands)

  3. CMN 2013 · Bilbao · 25-28 June · 3 Motivation • One emission stack (Electric power plant) • 4 imission stations • 3 consecutive days of emission and imission data

  4. CMN 2013 · Bilbao · 25-28 June · 4 Algorithm Adaptive Finite Element Model • Construction of a tetrahedral mesh • Mesh adapted to the terrain using Meccano method • Wind field modeling • Horizontal and vertical interpolation from HARMONIE data • Mass consistent computation • Calibration • Pollutant dispersion modeling • Wind field plume rise perturbation • Transport and reaction pollutant simulation • Calibration

  5. CMN 2013 · Bilbao · 25-28 June · 5 Mesh creation Meccano Method

  6. CMN 2013 · Bilbao · 25-28 June · 6 Mesh creation Meccano Method

  7. CMN 2013 · Bilbao · 25-28 June · 7 Mesh creation Meccano Method

  8. CMN 2013 · Bilbao · 25-28 June · 8 Mesh creation Gran Canaria Mesh

  9. CMN 2013 · Bilbao · 25-28 June · 9 Mesh creation Gran Canaria Mesh

  10. CMN 2013 · Bilbao · 25-28 June · 10 Mesh creation Gran Canaria Mesh

  11. CMN 2013 · Bilbao · 25-28 June · 11 Wind field modeling • Experimental data from 1 station (10 m over terrain) • Use Harmonie model • Harmonie is a non-hidrostatic model • U10 and V10 data from Harmonie has been used as measure stations data • Geostrophic wind from Harmonie

  12. CMN 2013 · Bilbao · 25-28 June · 12 Wind field modeling • Horizontal interpolation • Weighting inverse to the squared distance and inverse height differences

  13. CMN 2013 · Bilbao · 25-28 June · 13 Wind field modeling • Vertical interpolation • Log-linear wind profile Gesostrophic wind Mixing layer

  14. CMN 2013 · Bilbao · 25-28 June · 14 Wind field modeling • Mass-consistent model • Lagrange multiplier

  15. CMN 2013 · Bilbao · 25-28 June · 15 Wind field modeling • Calibration • ε (Horizontal interpolation weight) • Tv Th (Mass consistent factors) • Genetic algorithms • G. Montero, E. Rodriguez, R. Montenegro, J.M. Escobar, J.M. Gonzalez-Yuste, Genetic algorithms for na improved parameter estimation with local refinement of tetrahedral meshes in a wind model, Advances in Engineering Software, Volume 36, Issue 1, January 2005, Pages 3-10, ISSN 0965-9978, [DOI:10.1016/j.advengsoft.2004.03.011]

  16. CMN 2013 · Bilbao · 25-28 June · 16 Wind field modeling 20 m

  17. CMN 2013 · Bilbao · 25-28 June · 17 Plume rise modeling • Briggs formula • Buoyant (wc < 4Vo)‏ • Driving-force: gas temperature difference • Curved trajectory • Momentum (wc > 4Vo)‏ • Driving-force: Gas velocity • Vertical straight trajectory

  18. CMN 2013 · Bilbao · 25-28 June · 18 Air quality modeling Stack outflow Inlet wind boundaries Outlet wind boundaries Initial condition

  19. CMN 2013 · Bilbao · 25-28 June · 19 Air quality modeling RIVAD reactive model (4 species)

  20. CMN 2013 · Bilbao · 25-28 June · 20 Air quality modeling Splitting (Strang Splitting) Rosembrock 2 J = Jacobian s(c)

  21. CMN 2013 · Bilbao · 25-28 June · 21 Air quality modeling • Temporal discretization: Cranck-Nicolson • Spatial discretization: Least Squares FEM • System solver: Conjugate gradient preconditioned with an Incomplete Cholesky Factorization • Matrix storage: sparse MCS

  22. CMN 2013 · Bilbao · 25-28 June · 22 Air quality modeling Concentration after 1000 seconds

  23. CMN 2013 · Bilbao · 25-28 June · 23 Air quality modeling Concentration after 1000 seconds

  24. CMN 2013 · Bilbao · 25-28 June · 24 Air quality modeling

  25. CMN 2013 · Bilbao · 25-28 June · 25 Air quality modeling • Calibration • Diffusion (K) • Time step (artificial diffusion) Concentration SO2 at station 1 Measured data at station 1: 6.35 μg

  26. CMN 2013 · Bilbao · 25-28 June · 26 Conclusions and future work • Suitable approach for modeling air transport and reaction over complex terrains • A. Oliver, G. Montero, R. Montenegro, E. Rodríguez, J.M. Escobar, A. Pérez-Foguet, Adaptive finite element simulation of stack pollutant emissions over complex terrains, Energy, Volume 49, 1 January 2013, Pages 47-60, ISSN 0360-5442, http://dx.doi.org/10.1016/j.energy.2012.10.051. • Genetic algorithms useful for wind field calibration • Automatic calibration of diffusion and artificial diffusion for the transport and reaction of pollutants

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