Modeling of Cube Array Roughness: RANS, LES, and DNS

Flow over arrays of cubes is an extensively studied model problem for rough wall turbulent boundary layers. While considerable research has been performed in computationally investigating these topologies using DNS and LES, the ability of sublayer-resolved RANS to predict the bulk flow phenomena of these systems is relatively unexplored, especially at low and high packing densities. Here, RANS simulations are conducted on six different packing densities of cubes in aligned and staggered configurations. The packing densities investigated span from what would classically be defined as isolated, up to those in the d-type roughness regime, filling in the gap in the present literature. Three different sublayer-resolved turbulence closure models were tested for each case; a low Reynolds number k-ε model, the Menter k-ω SST model, and a full Reynolds stress model. Comparisons of the velocity fields, secondary flow features, and drag coefficients are made between the RANS results and existing LES and DNS results. There is a significant degree of variability in the performance of the various RANS models across all comparison metrics. However, the Reynolds stress model demonstrated the best accuracy in terms of the mean velocity profile as well as drag partition across the range of packing densities.

Impact of surface roughness on dust occurrence frequency over arid regions

<p>Soil dust aerosol created by wind erosion of arid and semi-arid surfaces dominates climate effects over large areas of the Earth. To represent the dust cycle, Global Earth System Models (ESMs) typically prescribe preferential dust sources phenomenologically using empirical source scaling functions. While this approach has helped to compensate for a lack or inaccuracy of soil and surface input data to models, it potentially limits progress in the representation of the global dust cycle, because such strong empirical constraints make models less sensitive to parameters known to affect dust emission, and thus potentially insensitive to changes in climate. Here we investigate the link between surface roughness due to non-erodible elements such as vegetation, pebbles and rocks, and the spatial patterns of dust activity. Using two different satellite-based methods to represent roughness within an atmospheric dust transport model, we evaluate the impact of surface roughness on the spatial distribution of dust optical depth occurrence frequency observed from satellite by both reducing the atmospheric momentum available for particle entrainment and protecting the surface from dust emission. We test the variability of our results across conceptually different parameterizations of dust emission and drag partition. Our results suggest that the spatial patterns of dust activity are largely determined by surface roughness, not only in semi-arid, but also in arid regions, where green vegetation is sparse or absent. </p>

Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module

Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.

A new parameterization of particle dry deposition over rough surfaces

In existing particle dry deposition schemes, the effects of gravity and surface roughness elements on particle motion are often poorly represented. In this study, we propose a new scheme to overcome such deficiencies. Particle deposition velocity is a function of aerodynamic, surface-collection and gravitational resistances. In this study, the effect of gravitation settling is treated analytically. More importantly, the new scheme takes into consideration the impacts of roughness elements on turbulent particle diffusion and surface particle collection. A relationship between aerodynamic and surface-collection processes is established by using an analogy between drag partition and deposition-flux partition. The scheme is then tested against a wind-tunnel data set for four different surfaces and a good agreement between the scheme predictions and the observations is found. The sensitivity of the scheme to the input parameters is tested. Important factors which affect particle deposition in different particle size ranges are identified. The scheme shows good capacity for modeling particle deposition over rough surfaces.

A new parameterization of dust dry deposition over rough surfaces

The performances of existing dust dry deposition schemes are rather unsatisfactory for rough surfaces. In this study, we propose a new scheme to overcome some of the deficiencies. The scheme takes into consideration of the impacts of roughness elements on turbulent dust diffusion and surface dust collection. A relationship between the aerodynamics and surface collection process is established by using an analogy between deposition-flux partition and drag partition. The scheme is then tested against a wind-tunnel dataset for four different surfaces and a good agreement between the scheme predictions and the observations is found. The sensitivity of the scheme to the input parameters is tested. Important factors which affect dust deposition in different particle size ranges are identified. The scheme shows good capacity for modeling dust deposition over rough surfaces.