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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,

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Presentation on theme: "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,"— Presentation transcript:

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 Laboratori de Càlcul Numèric (LaCàN) Departament de Matemàtica Aplicada III Universitat Politècnica de Catalunya - Barcelonatech Instituto Universitario SIANI Ingeniería Computacional Universidad de Las Palmas de Gran Canaria

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

3 Motivation  One emission stack (Electric power plant)  4 imission stations  3 consecutive days of emission and imission data CMN 2013 · Bilbao · June · 3

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 CMN 2013 · Bilbao · June · 4

5 Mesh creation CMN 2013 · Bilbao · June · 5 Meccano Method

6 Mesh creation CMN 2013 · Bilbao · June · 6 Meccano Method

7 Mesh creation CMN 2013 · Bilbao · June · 7 Meccano Method

8 Mesh creation CMN 2013 · Bilbao · June · 8 Gran Canaria Mesh

9 Mesh creation CMN 2013 · Bilbao · June · 9 Gran Canaria Mesh

10 Mesh creation CMN 2013 · Bilbao · June · 10 Gran Canaria Mesh

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

12 Wind field modeling  Horizontal interpolation Weighting inverse to the squared distance and inverse height differences CMN 2013 · Bilbao · June · 12

13 Wind field modeling  Vertical interpolation Log-linear wind profile CMN 2013 · Bilbao · June · 13 Gesostrophic wind Mixing layer

14 Wind field modeling  Mass-consistent model  Lagrange multiplier CMN 2013 · Bilbao · June · 14

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 , [DOI: /j.advengsoft ] CMN 2013 · Bilbao · June · 15

16 Wind field modeling CMN 2013 · Bilbao · June · m

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

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

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

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

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

22 Air quality modeling CMN 2013 · Bilbao · June · 22 Concentration after 1000 seconds

23 Air quality modeling CMN 2013 · Bilbao · June · 23 Concentration after 1000 seconds

24 Air quality modeling CMN 2013 · Bilbao · June · 24

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

26 Conclusions and future work CMN 2013 · Bilbao · June · 26  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 ,  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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