A Finite Volume Study on the Effects of Electro-Hydro-Dynamic Fluid Acceleration on Airflow Around a Cylinder at Low Reynolds Numbers

Applying a high-voltage electric field in a flow (electro-hydrodynamics or EHD) is a highly effective approach to locally accelerate a fluid to a desired speed. In this paper, a finite volume implementation is used to study the fluid flow around a fixed cylinder at a low Reynolds number. Two pairs of wire-plate electrodes are used to generate the electric field for fluid acceleration. Two Reynolds numbers are considered: Re=40, 100. We show that by increasing the Reynolds number, the relative effect of EHD is decreased. Further, we study the change in drag force due to EHD actuation. Finally, we showed that under certain voltage, electrode placement and Reynolds number the EHD fluid acceleration does not increase the total drag on the cylinder and yet leads to an increase in the total streamwise momentum transfer by augmenting the velocity at the top of the boundary layer

Effects of Gravity on the Acceleration and Pair Statistics of Inertial Particles in Homogeneous Isotropic Turbulence

Within the context of heavy particles suspended in a turbulent airflow, we study the effects of gravity on acceleration statistics and radial relative velocity (RRV) of inertial particles. The turbulent flow is simulated by direct numerical simulation (DNS) on a 2563 grid and the dynamics of O(106) inertial particles by the point-particle approach. For particles/droplets with radius from 10 to 60 µm, we found that the gravity plays an important role in particle acceleration statistics: (a) a peak value of particle acceleration variance appears in both the horizontal and vertical directions at a particle Stokes number of about 1.2, at which the particle horizontal acceleration clearly exceeds the fluid-element acceleration; (b) gravity constantly disrupts quasi-equilibrium of a droplet\u27s response to local turbulent motion and amplifies extreme acceleration events both in the vertical and horizontal directions and thus effectively reduces the inertial filtering mechanism. By decomposing the RRV of the particles into three parts: (1) differential sedimentation, (2) local flow shear, and (3) particle differential acceleration, we evaluate and compare their separate contributions. For monodisperse particles, we show that the presence of gravity does not have a significant effect on the shear term. On the other hand, gravity suppresses the probability distribution function (pdf) tails of the differential acceleration term due to a lower particle-eddy interaction time in presence of gravity. For bidisperse cases, we find that gravity can decrease the shear term slightly by dispersing particles into vortices where fluid shear is relatively low. The differential acceleration term is found to be positively correlated with the gravity term, and this correlation is stronger when the difference in colliding particle radii becomes smaller. Finally, a theory is developed to explain the effects of gravity and turbulence on the horizontal and vertical acceleration variances of inertial particles at small Stokes numbers, showing analytically that gravity affects particle acceleration variance both in horizontal and vertical directions, resulting in an increase in particle acceleration variance in both directions. Furthermore, the effect of gravity on the horizontal acceleration variance is predicted to be stronger than that in the vertical direction, in agreement with our DNS results. © 2015 AIP Publishing LLC

Effects of Forcing Time Scale on the Simulated Turbulent Flows and Turbulent Collision Statistics of Inertial Particles

In this paper, we study systematically the effects of forcing time scale in the large-scale stochastic forcing scheme of Eswaran and Pope [ An examination of forcing in direct numerical simulations of turbulence, Comput. Fluids 16, 257 (1988)] on the simulated flow structures and statistics of forced turbulence. Using direct numerical simulations, we find that the forcing time scale affects the flow dissipation rate and flow Reynolds number. Other flow statistics can be predicted using the altered flow dissipation rate and flow Reynolds number, except when the forcing time scale is made unrealistically large to yield a Taylor microscale flow Reynolds number of 30 and less. We then study the effects of forcing time scale on the kinematic collision statistics of inertial particles. We show that the radial distribution function and the radial relative velocity may depend on the forcing time scale when it becomes comparable to the eddy turnover time. This dependence, however, can be largely explained in terms of altered flow Reynolds number and the changing range of flow length scales present in the turbulent flow. We argue that removing this dependence is important when studying the Reynolds number dependence of the turbulent collision statistics. The results are also compared to those based on a deterministic forcing scheme to better understand the role of large-scale forcing, relative to that of the small-scale turbulence, on turbulent collision of inertial particles. To further elucidate the correlation between the altered flow structures and dynamics of inertial particles, a conditional analysis has been performed, showing that the regions of higher collision rate of inertial particles are well correlated with the regions of lower vorticity. Regions of higher concentration of pairs at contact are found to be highly correlated with the region of high energy dissipation rate. © 2015 AIP Publishing LLC

Simultaneous characterization of mesoscale and convective-scale tropical rainfall extremes and their dynamical and thermodynamic modes of change

The Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud-Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative-convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius-Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (-1.1%/K) offsets this increase. Convective-scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large-scale mass flux, while the intensity of convective-scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective-scale rainfall intensities. These results motivate the need for better understanding the role of the large-scale forcing on the formation and intensification of heavy convective rainfall

Simultaneous characterization of mesoscale and convective-scale tropical rainfall extremes and their dynamical and thermodynamic modes of change

The Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud-Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative-convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius-Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (-1.1%/K) offsets this increase. Convective-scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large-scale mass flux, while the intensity of convective-scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective-scale rainfall intensities. These results motivate the need for better understanding the role of the large-scale forcing on the formation and intensification of heavy convective rainfall

Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales

Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes

Global Microphysical Sensitivity of Superparameterized Precipitation Extremes

A recent study found statistically significant differences in extreme precipitation distributions over the contiguous United States (CONUS) when changing the microphysics scheme in a superparameterized global climate model. Here, we repeat the analysis globally and similarly find that differences are widespread when varying the number of predicted moments in the microphysics parameterization, but not when comparing variants of the double-moment scheme. However, contrary to the previous study in which differences largely disappeared over CONUS when 5-day simulations were conducted, we found that the signal in these shorter integrations remains within the tropics, implying a direct local effect of microphysics on precipitation extremes in these regions. The effect on precipitation is traced back to changes in vertical velocity profiles changes that are then amplified in the climatological simulations compared to the 5-day ones. Finally, the superparameterized extremes, regardless of the microphysics scheme, are shown to be larger than those from the Global Precipitation Climatology Project One-Degree Daily data set and generally smaller than those from the Tropical Rainfall Measuring Mission 3B42 data set

Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence

Systematic biases in the representation of boundary layer (BL) clouds are a leading source of uncertainty in climate projections. A variation on superparameterization (SP) called “ultraparameterization” (UP) is developed, in which the grid spacing of the cloud-resolving models (CRMs) is fine enough (250 × 20 m) to explicitly capture the BL turbulence, associated clouds, and entrainment in a global climate model capable of multiyear simulations. UP is implemented within the Community Atmosphere Model using 2° resolution (∼14,000 embedded CRMs) with one-moment microphysics. By using a small domain and mean-state acceleration, UP is computationally feasible today and promising for exascale computers. Short-duration global UP hindcasts are compared with SP and satellite observations of top-of-atmosphere radiation and cloud vertical structure. The most encouraging improvement is a deeper BL and more realistic vertical structure of subtropical stratocumulus (Sc) clouds, due to stronger vertical eddy motions that promote entrainment. Results from 90 day integrations show climatological errors that are competitive with SP, with a significant improvement in the diurnal cycle of offshore Sc liquid water. Ongoing concerns with the current UP implementation include a dim bias for near-coastal Sc that also occurs less prominently in SP and a bright bias over tropical continental deep convection zones. Nevertheless, UP makes global eddy-permitting simulation a feasible and interesting alternative to conventionally parameterized GCMs or SP-GCMs with turbulence parameterizations for studying BL cloud-climate and cloud-aerosol feedback

Microphysical Sensitivity of Superparameterized Precipitation Extremes in the Contiguous United States Due to Feedbacks on Large‐Scale Circulation

Superparameterized (SP) global climate models have been shown to better simulate various features of precipitation relative to conventional models, including its diurnal cycle as well as its extremes. While various studies have focused on the effect of differing microphysics parameterizations on precipitation within limited-area cloud-resolving models, we examine here the effect on contiguous U.S. (CONUS) extremes in a global SP model. We vary the number of predicted moments for hydrometeor distributions, the character of the rimed ice species, and the representation of raindrop self-collection and breakup. Using a likelihood ratio test and accounting for the effects of multiple hypothesis testing, we find that there are some regional differences, particularly during spring and summer in the Southwest and the Midwest, in both the current climate and a warmer climate with uniformly increased sea surface temperatures. These differences are most statistically significant and widespread when the number of moments is changed. To determine whether these results are due to (fast) local effects of the different microphysics or the (slower) ensuing feedback on the large-scale atmospheric circulation, we run a series of short, 5-day simulations initialized from reanalysis data. We find that the differences largely disappear in these runs and therefore infer that the different parameterizations impact precipitation extremes indirectly via the large-scale circulation. Finally, we compare the present-day results with hourly rain gauge data and find that SP underestimates extremes relative to observations regardless of which microphysics scheme is used given a fixed model configuration and resolution

Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence

Systematic biases in the representation of boundary layer (BL) clouds are a leading source of uncertainty in climate projections. A variation on superparameterization (SP) called “ultraparameterization” (UP) is developed, in which the grid spacing of the cloud-resolving models (CRMs) is fine enough (250 × 20 m) to explicitly capture the BL turbulence, associated clouds, and entrainment in a global climate model capable of multiyear simulations. UP is implemented within the Community Atmosphere Model using 2° resolution (∼14,000 embedded CRMs) with one-moment microphysics. By using a small domain and mean-state acceleration, UP is computationally feasible today and promising for exascale computers. Short-duration global UP hindcasts are compared with SP and satellite observations of top-of-atmosphere radiation and cloud vertical structure. The most encouraging improvement is a deeper BL and more realistic vertical structure of subtropical stratocumulus (Sc) clouds, due to stronger vertical eddy motions that promote entrainment. Results from 90 day integrations show climatological errors that are competitive with SP, with a significant improvement in the diurnal cycle of offshore Sc liquid water. Ongoing concerns with the current UP implementation include a dim bias for near-coastal Sc that also occurs less prominently in SP and a bright bias over tropical continental deep convection zones. Nevertheless, UP makes global eddy-permitting simulation a feasible and interesting alternative to conventionally parameterized GCMs or SP-GCMs with turbulence parameterizations for studying BL cloud-climate and cloud-aerosol feedback