A new boundary condition for large-eddy simulation of boundary-layer flow over surface roughness transitions

Abstract

Large-eddy simulations are performed to evaluate the performance of the surface boundary condition downwind of a rough-to-smooth surface transition. Two types of boundary conditions are tested: (1) The standard formulation based on local application of the Monin–Obukhov similarity theory, and (2) a new model based on the modification of the recently proposed model of Chamorro and Porté-Agel (L. Chamorro and F. Porté-Agel, Velocity and surface shear stress distributions behind a rough-to-smooth surface transition: A simple new model, Bound.-Layer Meteorol. 130 (2009), pp. 29–41). The new model assumes that the wind velocity downwind of a rough-to-smooth transition can be estimated as a weighted average of two logarithmic profiles. The first log-law is recovered above the internal boundary-layer height and corresponds to the upwind velocity profile. The second log-law is adjusted to the downwind aerodynamic roughness and is recovered near the surface in the equilibrium sublayer. Within that layer, the local log-law model can predict the surface shear stress well. The proposed nonlinear form of the weighting factor is equal to ln(z/δ_eq)/ln(δ _i /δ_eq), where z, δ _i and δ_eq are the height of the prediction location, the internal boundary-layer depth at that downwind distance, and the height of equilibrium sublayer, respectively. The performance of the new model is tested with available wind-tunnel measurements and shows improved predictions of surface shear stress and velocity distribution at different positions downwind of the transition. In addition, the prediction of the new model shows very small dependence on the height (i.e., grid size) at which it is applied.

Keywords:

• atmospheric boundary layer
• large-eddy simulation
• heterogeneous terrain
• roughness transition
• surface boundary condition model

Acknowledgements

This research was supported by the Swiss National Science Foundation (grant 200021_132122). Computing resources were provided by the Minnesota Supercomputing Institute (MSI) and the Swiss National Supercomputing Center (CSCS) under project ID s306.