Atmospheric CFD modelling for environmental applications at local scale

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Cornelius Richards
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1 Atmospheric CFD modelling for environmental applications at local scale B. Carissimo, S. Lacour, H. Foudhil, L. Musson-Genon, E. Dupont, D. Wendum, M. Milliez, B. Albriet, E. Demael, L. Laporte,

2 Outline Obectives Focus : concentration fluctuations Overview : other activities

3 Animation Wind tunnel Obectives Develop an integrated modelling tool for studies of the atmospheric environment at local scale Dispersion of pollutants and its impact on population and environment in urban and around industrial areas. Other impact studies : reactive pollutants, particle formation and dispersion, noise propagation Accidental releases, Population comfort (wind and turbulence prediction), extreme winds, wind energy...

4 The model: Mercure_Saturne Developed by CEREA 3-D model adapted to atmospheric flow and dispersion simulation Core of the model: CFD model Code_Saturne (EDF) which can handle complex geometry and complex physics Unstructured grid, finite volumes Simulations: Full scale, fine resolution, complex terrain Large scale meteo.. conditions taken into account k-ε turbulence closure model neutral gaz releases, point source, continuous releases Chemistry, aerosols

grid, finite volumes Simulations: Full scale, fine resolution, complex terrain Large scale meteo.

5 Concentration time series (wind tunnel measurements) At 300 m C i -C BF en ppm High fluctuations near the source T site en seconde 300 Ci Concentration moyenne At 1000 m C i -C BF en ppm T site en seconde

200 400 600 800 1000 T site en seconde 300 Ci Concentration moyenne At

6 Focus : concentration fluctuations Two approaches : Hybrid RANS / PDF (PhD Thesis A. Radicchi) Pure RANS (PhD Thesis M. Milliez)

7 Hybrid approach RANS/PDF RANS (k-ε) u i, P k, PDF (SLM¹) f (PDF Eulérienne de la concentration) ¹ Simplified Langevin Model

8 [ RANS / PDF ]a Résolution des EDS pour la traectoire dun échantillon de particules de fluide, du p,i 1 P x i u i, P, dx p,i dt u p,i dt u p,i u i T L dt C 0 dw i Champs moyens (RANS) x p,i,u p,i W i,t L Champs instantanés (EDS) Proccessus de Wiener, Temp characterisitique de la turbulence

i T L dt C 0 dw i Champs moyens (RANS) x p,i,u p,i W i,t L Champs

9 [ RANS / PDF ]b Résolution de lequation pour le melange de la concentration transportée par chaque particule de fluide, d p p m Conc. moyenne dt S dt p m Conc. instantanée particule p Temp de mélange

11 Pure RANS approach : Concentration mean: i x c u x C D x x C U t C = + ) ( ) ( ) ρ( ρ ) ( t t x C Sc c u = µ ρ t µ Sc t turbulent viscosity ε ρ µ µ 2 k C t = Schmidt number Concentration variance: i x c x c D x C c u x c u x c D x x c U t c = ) ( ) ( ) ( ρ ρ ρ ) ( 2 2 t t x c Sc c u = µ ρ k c R x c x c D f c ε ρ ρε 2 2 = =

number Concentration variance: i x c x c D x C c u x c u x c D x x c U t c = + 2 2 )

12 The Mock Urban Setting Test (MUST) Biltoft (2001). Yee and Biltoft (2004). Near full scale experiment in the U.S. Army Dugway Proving Ground (Utah), conducted for the DTRA (Defense Threat Reduction Agency )

13 Simulations with Mercure 0.6 to 1m ~2 m Mesh: ~ hexahedral elements Dimensions: 240 m x 233 m x 32m ~4 m ~4 m ~0.3 m Horizontal grid : lower levels Stretched vertical grid MUST: 20 selected cases Detailed study of dynamics and mean concentration with Mercure (Milliez and Carissimo, 2006).

14 Simulations with Mercure Mean concentration (ppm) Root mean square of concentration fluctuations (ppm)

15 Simulations with Mercure 20 simulated cases:

16 Simulations with Mercure 20 simulated cases: Root mean square of concentration fluctuations MG VG FAC2 Line % Line % Line % Line4 All horizontal Tower T Masts A, B, C, D All vertical All % 66.4 % 50.3 % 53.8 % 52.6 % 60.1 % MG = exp ( ln Co ln Cp ) VG = exp [ (ln Co-ln Cp) 2 ] FAC2 = fraction of data that satisfy 0.5<Cp/Co<2 Co = MUST observations Cp = Mercure predictions x = average over the data set

$1 % MG = exp ( ln Co ln Cp ) VG = exp [ (ln Co-ln Cp) 2 ] FAC2 = fraction of data that satisfy 0.$

17 Root mean square of concentration fluctuations (ppm) Simulations with Mercure Influence of turbulence and stratification 10 simulated cases: FAC2 (neutral) = 46.9 % FAC2 (stable) = 53.3 % + 6 %

18 Simulations with Mercure Influence of initialisation and emission rate Root mean square of concentration fluctuations (ppm) c (source) = 1% C(source)

19 Overview : other activities Cloud modeling : plume + fog Impact of building and complex terrain on dispersion Chemistry of reactive plumes Aerosols formation (local scale) Wind energy Radiative effects operational ABL prediction (SIRTA) Traffic and tunnel }Following presentations

plumes Aerosols formation (local scale) Wind energy Radiative effects

20 Panache Bugey 12 mars 1980 Calcul code Mercure iso-concentration eau liquide

21 b) a) Fig. 2 Comparison of the in-situ spectra measured by the aircraft with the simulated ones. a) Horizontal slice at z=1320m above the ground for case B ( - - : iso-lwc and : iso-n c ). The symbol shows the maximum of LWC, and the circles o are part of the same arc whose center is. b) shows the measured spectra (black) 660m far from the and the log-normal curves (blue) obtained from the LWC and N c values given by the code at the position of the shaded circles (located 660m far from the ).

22 Stage C. Samba Évolution de la visibilité Nouveau paramètre d étude : la visibilité VIS ln(0.02) = β avec β = ( ρ ) q l Variations spatio-temporelles de la visibilité 220 vis ibilite Mesures Altitude (m) MERCURE CAILLOU Temps (heure ) 10 3 phases dans l évolution du brouillard : La formation, le développement vertical et la dissipation de la couche de brouillard

23 IV. Résultats Spectre des gouttelettes à 2m Diamètre moyen de l ordre de 10 µm et 20 µm Nc de l ordre de quelques centaines de gouttelettes par cm³ Distribution des gouttelettes de nuages (cm-3/µm) à 2m en fonction du diamètre des gouttelettes (µm)

24 Overview : other activities Cloud modeling : plume + fog Impact of building and complex terrain on dispersion Chemistry of reactive plumes Aerosols formation (local scale) Wind energy Local ABL prediction (SIRTA) Traffic and tunnel Radiative effects

26 Résultats pour la situation de vent de Sud (Mercure_Saturne 1.1.3) Attaque du 1er aéro

27 Résultats pour la situation de vent de Sud (Mercure_Saturne 1.1.3)

28 Overview : other activities Cloud modeling : plume + fog Impact of building and complex terrain on dispersion Chemistry of reactive plumes Aerosols formation (local scale) Wind energy Local ABL prediction (SIRTA) Traffic and tunnel Radiative effects

31 Overview : other activities Cloud modeling : plume + fog Impact of building and complex terrain on dispersion Chemistry of reactive plumes Aerosols formation (local scale) Wind energy Local ABL prediction (SIRTA) Traffic and tunnel Radiative effects

32 Near source aerosol modeling (Thesis B. Albriet)

33 Overview : other activities Cloud modeling : plume + fog Impact of building and complex terrain on dispersion Chemistry of reactive plumes Aerosols formation (local scale) Wind energy Local ABL prediction (SIRTA) Traffic and tunnel Radiative effects

34 Wind energy : turbulence in complex terrain and mask effect for large parks (Thesis L. Laporte) Askervein Hill :

Including thermal effects in CFD simulations

Including thermal effects in CFD simulations Catherine Meissner, Arne Reidar Gravdahl, Birthe Steensen [email protected], [email protected] Fjordgaten 15, N-125 Tonsberg hone: +47 8 1800 Norway Fax: