Calibration and validation of an air quality finite element model around an electric power plant in Gran Canaria island

Transcription

1 Calibration and validation of an air quality finite element model around an electric power plant in Gran Canaria island J. Ramírez (1), A. Oliver (2), J.E. González (3), R. Montenegro (1) (1) Instituto Universitario SIANI, Ingeniería Computacional, Universidad de Las Palmas de Gran Canaria (2) Laboratori de Càlcul Numèric (LaCàN) Departament de Matemàtica Aplicada III Universitat Politècnica de Catalunya - Barcelonatech (3) Laboratorio de control analítico de fuentes medionambientales (CAFMA), Universidad de Las Palmas de Gran Canaria

2 Motivation Validation of the framework proposed by the authors (Oliver et al. 2013, Energy) through experimental data from an electric power plant Gran Canaria island (Canary Islands) EMS 2013 Reading(UK) 9-13 Sept 2

3 Motivation Two different stages: Modeling and Calibration Two kinds of data are needed: 1) Wind data 2) Pollutant concentration data EMS 2013 Reading(UK) 9-13 Sept 3

4 o WIND DATA: For modeling and calibration Motivation Wind data from 1 station close to power plant Wind data from forecasting model 3 consecutive days of wind data (hourly) Calibration of mode through genetic algorithms EMS 2013 Reading(UK) 9-13 Sept 4

5 o Pollutants data: Some data for modeling and other for calibrating Motivation One emission stack (Electric power plant) (modeling) 4 immission stations (calibration) 3 consecutive days of emission and immission data (hourly) Calibration of model variables attending experimental data from immission EMS 2013 Reading(UK) 9-13 Sept 5

6 Adaptive Finite Element Model Construction of a tetrahedral mesh adapted to the terrain with the Meccano method Wind field modeling from experimental and meteorological data Horizontal and vertical interpolation from HARMONIE data and experimental wind data from station Mass consistent computation Calibration of wind parameters Pollutant dispersion modeling Wind field plume rise perturbation Transport and reaction pollutant simulation Calibration of transport parameters Algorithm EMS 2013 Reading(UK) 9-13 Sept 6

7 Gran Canaria Mesh Mesh construction EMS 2013 Reading(UK) 9-13 Sept 7

8 Mesh construction Gran Canaria Mesh (II) EMS 2013 Reading(UK) 9-13 Sept 8

9 Gran Canaria Mesh Mesh construction EMS 2013 Reading(UK) 9-13 Sept 9

10 Adaptative Mesh Gran Canaria Mesh EMS 2013 Reading(UK) 9-13 Sept 10

11 Wind field modeling Experimental data from 1 station (power plant) Use Harmonie model Harmonie is a non-hidrostatic weather forecasting model U 10 and V 10 data at grid points from Harmonie has been used as measure stations data Geostrophic wind from Harmonie EMS 2013 Reading(UK) 9-13 Sept 11

12 Wind field modeling Horizontal interpolation Weighting inverse to the squared distance and inverse height differences EMS 2013 Reading(UK) 9-13 Sept 12

13 Wind field modeling Vertical interpolation Log-linear wind profile Geostrophic wind Mixing layer EMS 2013 Reading(UK) 9-13 Sept 13

14 Wind field modeling The resulting mass-consistent wind field u verifies: and minimizes the adjusting functional EMS 2013 Reading(UK) 9-13 Sept 14

15 Wind field modeling Introducing a Lagrange multiplier Such that it verifies an elliptic problem in EMS 2013 Reading(UK) 9-13 Sept. 15

16 Wind field Calibration EMS 2013 Reading (UK) 9-13 Sept. 16

17 Wind field Calibration FE solution is needed for each individual EMS 2013 Reading (UK) 9-13 Sept. 17

18 log Error Wind field Calibration Genetic Algorithm evolution 1,00E+000 1,00E Initial population different episodes 100 genetic iterations 1,00E-002 1,00E-003 1,00E-004 1,00E-005 1st 2nd 3rd 4th 5th 6th 1,00E-006 1,00E-007 genetic step EMS 2013 Reading (UK) 9-13 Sept. 18

19 Wind field results 20 m EMS 2013 Reading (UK) 9-13 Sept. 19

20 Plume rise modeling EMS 2013 Reading (UK) 9-13 Sept - 20

21 Plume rise modeling Briggs formula ( 4Vo Buoyant (wc < Driving-force: gas temperature difference Curved trajectory ( 4Vo Momentum (wc > Driving-force: Gas velocity Vertical straight trajectory EMS 2013 Reading (UK) 9-13 Sept. 21

22 Air quality modeling Find concentration c(x,t) for Stack outflow Inlet wind boundaries Outlet wind boundaries Initial condition EMS 2013 Reading (UK) 9-13 Sept. 22

23 Air quality modeling RIVAD reactive model (4 species) EMS 2013 Reading (UK) 9-13 Sept. 23

24 Air quality modeling Splitting (Strang Splitting) Rosembrock 2 J = Jacobian s(c) EMS 2013 Reading (UK) 9-13 Sept. 24

25 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 EMS 2013 Reading (UK) 9-13 Sept. 25

26 Air quality Calibration Calibration Diffusion (K) Choose time step regarding numerical conditions (artificial diffusion) Concentration SO2 at station 1 Measured data at station 1: 6.35 μg EMS 2013 Reading (UK) 9-13 Sept 26

27 Air quality results Concentration S0 2 (g/m 3 ) after 1000 seconds EMS 2013 Reading (UK) 9-13 Sept. 27

28 Air quality results Concentration S0 2 (g/m 3 ) after 1000 seconds EMS 2013 Reading (UK) 9-13 Sept. 28

29 Isosurface evolution 1 µg/m 3 Air quality results EMS 2013 Reading (UK) 9-13 Sept. 29

30 Conclusions 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 calibration Validation comparing model outcomes with experimental data EMS 2013 Reading (UK) 9-13 Sept. 30