scholarly journals Turbulent Condensation of Droplets: Direct Simulation and a Stochastic Model

2009 ◽  
Vol 66 (3) ◽  
pp. 723-740 ◽  
Author(s):  
Roberto Paoli ◽  
Karim Shariff
Keyword(s):  
Turbulent Mixing ◽  
Isotropic Turbulence ◽  
Droplet Size Distribution ◽  
Temperature Fluctuations ◽  
Stochastic Equations ◽  
Direct Simulation ◽  
Homogeneous Isotropic Turbulence ◽  
Cloud Droplets ◽  
Droplet Condensation

Abstract The effect of turbulent mixing on droplet condensation is studied via direct numerical simulations of a population of droplets in a periodic box of homogeneous isotropic turbulence. Each droplet is tracked as a fluid particle whose radius grows by condensation of water vapor. Forcing of the small wavenumbers is used to sustain velocity, vapor, and temperature fluctuations. Temperature and vapor fluctuations lead to supersaturation fluctuations, which are responsible for broadening the droplet size distribution in qualitative agreement with in situ measurements. A model for the condensation of a population of cloud droplets in a homogeneous turbulent flow is presented. The model consists of a set of Langevin (stochastic) equations for the droplet area, supersaturation, and temperature surrounding the droplets. These equations yield corresponding ordinary differential equations for various moments and correlations. The statistics predicted by the model, for instance, the droplet area–supersaturation correlation, reproduce the simulations well.

scholarly journals Reynolds-number dependence of turbulence enhancement on collision growth

2016 ◽  
Vol 16 (19) ◽  
pp. 12441-12455 ◽  
Author(s):  
Ryo Onishi ◽  
Axel Seifert
Keyword(s):  
Reynolds Number ◽  
Isotropic Turbulence ◽  
Reynolds Numbers ◽  
Stokes Number ◽  
Homogeneous Isotropic Turbulence ◽  
Collision Efficiency ◽  
Cloud Droplets ◽  
Clustering Effect ◽  
Reynolds Number Dependence ◽  
Coagulation Kernel

Abstract. This study investigates the Reynolds-number dependence of turbulence enhancement on the collision growth of cloud droplets. The Onishi turbulent coagulation kernel proposed in Onishi et al. (2015) is updated by using the direct numerical simulation (DNS) results for the Taylor-microscale-based Reynolds number (Reλ) up to 1140. The DNS results for particles with a small Stokes number (St) show a consistent Reynolds-number dependence of the so-called clustering effect with the locality theory proposed by Onishi et al. (2015). It is confirmed that the present Onishi kernel is more robust for a wider St range and has better agreement with the Reynolds-number dependence shown by the DNS results. The present Onishi kernel is then compared with the Ayala–Wang kernel (Ayala et al., 2008a; Wang et al., 2008). At low and moderate Reynolds numbers, both kernels show similar values except for r2 ∼ r1, for which the Ayala–Wang kernel shows much larger values due to its large turbulence enhancement on collision efficiency. A large difference is observed for the Reynolds-number dependences between the two kernels. The Ayala–Wang kernel increases for the autoconversion region (r1, r2 < 40 µm) and for the accretion region (r1 < 40 and r2 > 40 µm; r1 > 40 and r2 < 40 µm) as Reλ increases. In contrast, the Onishi kernel decreases for the autoconversion region and increases for the rain–rain self-collection region (r1, r2 > 40 µm). Stochastic collision–coalescence equation (SCE) simulations are also conducted to investigate the turbulence enhancement on particle size evolutions. The SCE with the Ayala–Wang kernel (SCE-Ayala) and that with the present Onishi kernel (SCE-Onishi) are compared with results from the Lagrangian Cloud Simulator (LCS; Onishi et al., 2015), which tracks individual particle motions and size evolutions in homogeneous isotropic turbulence. The SCE-Ayala and SCE-Onishi kernels show consistent results with the LCS results for small Reλ. The two SCE simulations, however, show different Reynolds-number dependences, indicating possible large differences in atmospheric turbulent clouds with large Reλ.

scholarly journals Modulation of fluid temperature fluctuations by particles in turbulence

2021 ◽  
Vol 931 ◽  
Author(s):  
Izumi Saito ◽  
Takeshi Watanabe ◽  
Toshiyuki Gotoh
Keyword(s):  
Isotropic Turbulence ◽  
Thermal Relaxation ◽  
Temperature Fluctuations ◽  
Turnover Time ◽  
Fluid Temperature ◽  
Thermal Coupling ◽  
Homogeneous Isotropic Turbulence ◽  
Large Eddy ◽  
Theoretical Predictions ◽  
Momentum Coupling

Modulation of fluid temperature fluctuations by particles due to thermal interaction in homogeneous isotropic turbulence is studied. For simplicity, only thermal coupling between the fluid and particles is considered, and momentum coupling is neglected. Application of the statistical theory used in cloud turbulence research leads to the prediction that modulation of the intensity of fluid temperature fluctuations by particles is expressed as a function of the Damköhler number, which is defined as the ratio of the turbulence large-eddy turnover time to the fluid thermal relaxation time. Direct numerical simulations are conducted for two-way thermal coupling between the fluid temperature field and point particles in homogeneous isotropic turbulence. The simulation results are shown to agree well with the theoretical predictions.

scholarly journals Droplet size distribution in homogeneous isotropic turbulence

2012 ◽  
Vol 24 (6) ◽  
pp. 065101 ◽  
Author(s):  
Prasad Perlekar ◽  
Luca Biferale ◽  
Mauro Sbragaglia ◽  
Sudhir Srivastava ◽  
Federico Toschi
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Isotropic Turbulence ◽  
Droplet Size Distribution ◽  
Homogeneous Isotropic Turbulence

Momentum Diffusion From a Slot Jet Into a Moving Secondary

1956 ◽  
Vol 23 (3) ◽  
pp. 437-443
Author(s):  
A. S. Weinstein ◽  
J. F. Osterle ◽  
W. Forstall
Keyword(s):  
Turbulent Mixing ◽  
Isotropic Turbulence ◽  
Single Parameter ◽  
Spreading Coefficient ◽  
Homogeneous Isotropic Turbulence ◽  
University Of Illinois ◽  
Secondary Stream ◽  
The University ◽  
Initial Turbulence ◽  
Tube Study

Abstract Results are presented for an experimental, impact-tube study of the diffusion of momentum for the isothermal, incompressible, turbulent mixing of a slot jet issuing into a slower moving secondary region. The symmetric spread of the jet into a uniformly flowing secondary stream of low initial turbulence is well correlated by phenomenological expressions based on assumptions suggested by the Engineering Experiment Station of the University of Illinois and extended in this paper to include the case of the slot jet issuing into a moving secondary. The single parameter upon which this correlation is based, the spreading coefficient, is shown to have an interesting interpretation in terms of the diffusion of a stream of fluid “particles” into uniformly flowing fields of homogeneous isotropic turbulence.

scholarly journals Turbulence–flame interactions in lean premixed hydrogen: transition to the distributed burning regime

2011 ◽  
Vol 680 ◽  
pp. 287-320 ◽  
Author(s):  
A. J. ASPDEN ◽  
M. S. DAY ◽  
J. B. BELL
Keyword(s):  
Numerical Simulation ◽  
Turbulent Mixing ◽  
Isotropic Turbulence ◽  
Three Dimensional ◽  
Homogeneous Isotropic Turbulence ◽  
Mixing Processes ◽  
Lean Premixed ◽  
Three Dimensional Configuration

The response of lean (ϕ ≤ 0.4) premixed hydrogen flames to maintained homogeneous isotropic turbulence is investigated using detailed numerical simulation in an idealised three-dimensional configuration over a range of Karlovitz numbers from 10 to 1562. In particular, a focus is placed on turbulence sufficiently intense that the flames can no longer be considered to be in the thin reaction burning regime. This transition to the so-called distributed burning regime is characterised through a number of diagnostics, and the relative roles of molecular and turbulent mixing processes are examined. The phenomenology and statistics of these flames are contrasted with a distributed thermonuclear flame from a related astrophysical study.

scholarly journals Reynolds-number dependence of turbulence enhancement on collision growth

2016 ◽  
Author(s):  
Ryo Onishi ◽  
Axel Seifert
Keyword(s):  
Reynolds Number ◽  
Isotropic Turbulence ◽  
Reynolds Numbers ◽  
Stokes Number ◽  
Homogeneous Isotropic Turbulence ◽  
Collision Efficiency ◽  
Cloud Droplets ◽  
Clustering Effect ◽  
Reynolds Number Dependence ◽  
Coagulation Kernel

Abstract. This study investigates the Reynolds-number dependence of turbulence enhancement on the collision growth of cloud droplets. The Onishi turbulent coagulation kernel proposed in Onishi et al. (2015) is updated by using the direct numerical simulation (DNS) results for the Taylor-microscale-based Reynolds number (Reλ) up to 1,140. The DNS results for particles with a small Stokes number (St) show a consistent Reynolds-number dependence of the so-called clustering effect with the locality theory proposed by Onishi et al. (2015). It is confirmed that the present Onishi kernel is more robust for a wider St range and has better agreement with the Reynolds-number dependence shown by the DNS results. The present Onishi kernel is then compared with the Ayala-Wang kernel (Ayala et al. (2008a); Wang et al. (2008)). At low and moderate Reynolds numbers both kernels show similar values except for r2 ∼ r1, for which the Ayala-Wang kernel shows much larger values due to its large turbulence enhancement on collision efficiency. A large difference is observed for the Reynolds-number dependences between the two kernels. The Ayala-Wang kernel increases for the autoconversion region (r1, r2 < 40 μm) and for the accretion region (r1 < 40 μm and r2 > 40 μm; r1 > 40 μm and r2 < 40 μm) as Reλ increases. In contrast, the Onishi kernel decreases for the autoconversion region and increases for the rain-rain self-collection region (r1, r2 > 40 μm). Stochastic collision-coalescence equation (SCE) simulations are also conducted to investigate the turbulence enhancement on particle size evolutions. The SCE with the Ayala-Wang kernel (SCE-Ayala) and that with the present Onishi kernel (SCE-Onishi) are compared with results from the Lagrangian Cloud Simulator (LCS, Onishi et al. (2015)), which tracks individual particle motions and size evolutions in homogeneous isotropic turbulence. The SCE-Ayala and SCE-Onishi kernels show consistent results with the LCS results for small Reλ. The two SCE simulations, however, show different Reynolds-number dependences, indicating possible large differences in atmospheric turbulent clouds with large Reλ.

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