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Collision efficiency of cloud droplets: Results from point-droplet to droplet-resolving simulations Lian-Ping Wang Department of Mechanical Engineering, University of Delaware, USA & Department of Mechanics and Aerospace Engineering,


  1. Collision efficiency of cloud droplets: Results from point-droplet to droplet-resolving simulations Lian-Ping Wang Department of Mechanical Engineering, University of Delaware, USA & Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, China lwang@udel.edu August 17, 2018, 9:30-10:30 International Workshop on Cloud Dynamics, Micro-Physics and Small-scale Simulation Indian Institute of Tropical Meteorology, Ministry of Earth Science, Pune, India

  2. Outline v Background and Motivation v Overview of point-droplet based hybrid direct numerical simulations ª A status report ª Open research v Droplet-resolving direct numerical simulations ª Why do we need it? ª Is it possible? ª Results for the case of gravitational coalescence v Summary 2

  3. Collision-coalescence: effects of small-scale turbulence on the 3 rd microphysical step to warm rain initiation Growth of cloud droplets War arm m Rain ain Proces ocess nuclei Small droplet Rain 3 3 drop 2 2 How does air turbulence Turbulent affect the collision rates environment and collision efficiency of 1 1 cloud droplets? 1. 1. Act ctiv ivation ion 2. Condens 2. ondensation ion What is the impact on 3. Collis 3. ollision- ion- warm rain initiation? coales coalescence cence 3

  4. Aerosol-cloud-weather/climate interactions Greenhouse Radiation Radiative gases balance Cloudiness Human Aerosol Microscale activity Macroscale Hydrological cycle Meteorology Stevens and Feingold, 2009, Nature, 461, doi:10.1038.

  5. Cloud physics: The multiscale problem down to droplet size! Cloud microphysics Cloud dynamics Droplet- Turbulence- Cloud- Mesoscale Global resolving resolving resolving 10 − 6 10 − 4 10 − 2 1 10 2 10 4 10 6 10 7 m Droplet-resolving DNS Hybrid DNS LES NWP GCM 5 C.-H. Moeng, NCAR

  6. Cloud physics: Also a wide range of time scales Cloud microphysics Cloud dynamics Droplet- Turbulence- Cloud- Mesoscale Global resolving resolving resolving 10 − 6 10 − 4 10 − 2 1 10 2 10 4 10 6 10 7 m Droplet-resolving Point-droplet LES NWP GCM DNS hybrid DNS 2 ρ p a 2 τ K ≈ T e ~ 100 s days hours τ p = 9 µ 10 ms ~ 100 ms ≈ 0 . 1 ms → 10 ms ß Overlap of droplet inertial response time ( ) 10 a 1 + a 2 and droplet-pair interaction time Δ W ≈ 1 ms ~ 100 ms

  7. General comments on the warm rain process Fluid dynamics: a process driven by at least three levels of nonlinearity Nonlinear advection, momentum-buoyancy coupling, local heating due to condensation - Large flow Reynolds number - Vertical Buoyancy velocity scale ~ horizontal wind fluctuations - Latent heat release ~ kinetic energy of turbulent air flow Multiscale processes: Coupling of microphysics and turbulent flows - Dispersed multiphase turbulent flow with phase change Clouds are the major source of uncertainty in weather prediction We understand warm rain process well qualitatively The devil is in the quantitative details and complex couplings among scales The spectral width of cloud droplets? The time scale for warm rain initiation? ….. Effect of air turbulence on collision-coalescence?

  8. Hybrid Direct Numerical Simulation Real clouds Grabowski and Wang, Growth of cloud droplets in a turbulent environment. Annu. Rev. Fluid Mech. , 45 (2013) 293-324.

  9. Direct simulation of small-scale air turbulence: A bottom-up approach � Flow field � � � U � P 1 � ∂ ⎛ ⎞ 2 2 U U U f ( x , t ) = × ω − ∇ ⎜ + ⎟ + ν ∇ + ⎜ ⎟ t 2 ∂ ρ ⎝ ⎠ � solved with in a periodic box ∇ U 0 ⋅ = � U � ( x , t ) 0 isotropic and homogeneous: = Kolmogorov scales: 1/ 4 1/ 2 % ( η = ν 3 ' ( % ; τ k = ν 1/ 4 ( ) Primary effect ; v k = νε * ' * & ) ε ε & ) Effect of large scales:   2 U ⋅ U $ ' Secondary 15 u ' u ' = or R λ = & ) effect 3 v k % ( Small-scale flow of adiabatic cumulus cloud core is assumed to be nearly homogeneous and isotropic. (Vaillancourt and Yau 2000) ( ) ( ) = O 10 − 3 O 10 − 6 One-way coupling: Loading by mass or by volume. 9

  10. Equation of motion for droplets     ) +   [ ] ( ( α ) ( t ) − ( α ) ( t ), t ( α ) , t ) V U Y u ( Y ( α ) ( t ) −  d V g = − ( α ) dt τ p   ( α ) ( t ) d Y ( α ) ( t ) V = dt 2 /(9 µ ), W ( α ) = τ p ( α ) = 2 ρ p a ( α ) Where ( ) ( α ) g τ p   If hydrodynamic interaction is considered: ( α ) , t ) ≠ 0 u ( Y Self-consistent: no ambiguity in defining undisturbed fluid velocity Typically tracking 10 5 ~10 7 droplets with hydrodynamic interactions. A lot of quantitative information can be extracted! 10

  11. The hybrid DNS approach: disturbance flows due to droplets N p ! ! ! ! ! ! U ( ! ! ⎟− ! Three-way ⎛ ⎞ ⎛ ⎞ ( ) ; a m ( ) , m ( ) − m ( ) , t m ( ) m x , t ) + u s Y − Y V U Y u ∑ ⎜ ⎜ ⎟ interactions? ⎝ ⎠ ⎝ ⎠ m = 1 Background turbulent flow Disturbance flows due to droplets + Features: Background turbulent flow can affect the disturbance flows; No-slip condition on the surface of each droplet is satisfied on average; Both near-field and far-field interactions are considered; Approximate method but efficient. Wang, Ayala, and Grabowski, J. Atmos. Sci. 62(4): 1255-1266 (2005). Ayala, Wang, and Grabowski, J. Comp. Phys, 225, 51-73 (2007). 11 Onishi, Takahashi, Vassilicos, J. Comp. Phys, 242, 809-827 (2013).

  12. Dynamic collision kernel Volume concentrations are low, binary collision events dominate ! N 12 D = Γ 12 n 1 ⋅ n 2 Much more complex: Turbulence Droplet inertia # of collision events # ! Hydrodynamic interaction N 12 = = m 3 ⋅ s ( ) ⋅ per unit time ( ) per unit volume n 1 = # of particles of radius a 1 = # m 3 ( ) per unit volume n 2 = # of particles of radius a 2 = # m 3 ( ) per unit volume W 1 − W 2 D = m 3 s = relative swept volume ⇒ Γ 12 time Kinematic description à No hydrodynamic interaction No turbulence 2 W ( ) 2 a 1 + a 2 K = π a 1 + a 2 ( ) 1 − W 2 Γ 12

  13. Geometric collision kernel: Finite-inertia droplets in a turbulent flow ! N = 2 π R 2 | w r ( r = R ) | g 12 ( r = R ) Γ 12 = n 1 n 2 Geometric radial distribution function Geometric Radial relative velocity   N pair ( r − δ ≤ d ≤ r + δ )/4 π ( r + δ ) 3 − ( r − δ ) 3 ( ) [ ] V V  1 − 1 2 ∑ | w r | = r ⋅ g 12 ( R ) = lim N pair r N 1 N 2 / V B δ << r all pairs • Based on the spherical formulation • Confirmed by DNS for all different situations • Straightforward to calculate in DNS, but could be very difficult to measure!!! Sundaram & Collins, J. Fluid Mech. 335: 75-109 (1997). 13 Wang, et al. J. Atmos. Sci. 62: 2433-2450 (2005).

  14. Representative results on the geometric collision rate of cloud droplets ( ) ε R λ Resolution a 1 , a 2 ( ) ) cm 2 s − 3 ( µ m Franklin et al. (2005, 2007) 2.5 ~ 30 95 ~ 1535 up to 55 240 3 Wang et al. (2005) Ayala et al. (2008a,b) 10 ~ 60 10 ~ 400 up to 72 128 3 Rosa et al. (2013) 10 ~ 60 400 up to 500 1024 3 Chen et al. (2016) 5 ~ 25 50 ~ 1500 up to 589 1024 3 Onishi & Seifert (2016) 20 ~ 50 100 ~ 1000 up to 1140 6000 3 Analytical parameterizations are made available.

  15. Reynolds number dependence Onishi and Seifert (2016) Atmos. Chem. Phys. ß Rosa et al. (2013), New J. Phys.

  16. The collision efficiency

  17. Local hydrodynamic interactions: gravitational collision efficiency Rigorous collision efficiency in still air τ p 2 = inertial response time of the smaller droplet R / v p 1 − v p 2 ( ) hydrodynamic interaction time Rosa, Wang, Maxey, and Grabowski, 2011, An accurate model for aerodynamic interactions of cloud droplets, J. Comp. Phys., 230, 8109-8133.

  18. Back of the envelope analysis a 1 a 2 τ p 2 10 R τ p 2 W 1 , Stokes W 2 , Stokes E a 1 Δ W Stokes 10 R / Δ W Stokes ( ) cm / s ( ) cm / s ( ) Stokes, gravity ( ) s 30 µ m 0.20 4.48 × 10 − 4 10.963 0.4385 3.42 × 10 − 3 0.131 0.04566 30 µ m 0.50 2.80 × 10 − 3 10.963 2.7407 5.47 × 10 − 3 0.512 0.5273 30 µ m 0.90 9.06 × 10 − 3 10.963 8.8800 2.74 × 10 − 3 0.330 0 . 1875 1 . 85 ⎛ ⎞ τ p 2 E ~ ⎜ ⎟ ⎜ ⎟ 10 R / Δ W Stokes " % a 2 τ p = 2 ρ p ⎝ ⎠ − 1 $ ' $ ' 9 ρ f υ # & W Stokes = τ p g

  19. The collision efficiency based on point-droplet hybrid DNS using ISM

  20. How to obtain turbulent collision efficiency in point-droplet based DNS? 2 ⎡ ⎤ Turb Turb , HI x grazing T ≡ E 12 Γ g = Turb = 12 , E 12 η 12 , E 12 ⎢ ⎥ Turb , No − HI g ( ) a 1 + a 2 E 12 ⎢ ⎥ Γ ⎣ ⎦ 12 N Turb , HI N Turb , No − HI ! ! HI = Turb , No − HI = , Γ where Γ 12 12 n 1 n 2 n 1 n 2 Three grazing trajectories of 20-μm droplets relative to 25-μm droplets 400 cm 2 s − 3 ε = 0 100 cm 2 s − 3 dt = 0 . 42 τ p 20 µ m ( ) Wang, et al., J. Atmos. Sci. 62: 2433-2450 (2006). 20 Ayala et al. , J. Comp. Phys. 225: 51-73. 62 (2007).

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