Flow around a blu ff body

TheRANSandLESturbulence models are validated with a practical wind engineering appli- cation of flow around a bluffbody, namely a cube immersed in a boundary layer. The external pressure distribution around the cube is sought usingRANSandLESturbulence models. The setup used for this test is similar to the one used byKose & Dick(2010); cube height H=4 cm, bulk velocity 10 m/s and molecular viscosity at 10−5 kg/ms, and Re= 40000. The mesh con- sists of about 200000 cells. The cells are expanded away from the cube as shown in Fig. 3.11. Appropriate boundary conditions are applied as specified inKose & Dick(2010); Richard and hoxey inlet profiles fork− model and a turbulent inlet with random fluctuations for the LES model, symmetry boundry conditon on the left,right and top walls, a pressure-outlet condi- ton, and simulations are carried out using standard k-epsilon and SmagorniskyLESturbulence models. A SmagorinskyLESmodel with a time step of 2x10−4sec is used for the simulation.

3.5. Development of high performanceCFDcode 75

(a)LES (b)RANS

Figure 3.12: Plots of instantaneous and mean velocity contours showing vortex shading behind the cube

Figure 3.13: Pressure coefficients along vertical section of cube. Adapted from Bitsuamlak et al.(2010)

Formation of Karman vortex street behind the cube is captured by the simulation as shown in the instantaneous profile of Fig. 3.12. Pressure coefficients are calculated for a vertical section passing through center lines of the front, top and back faces of the cube. FromLESsimulations pressure values can be obtained at any instant of time, whileRANSgives only time averaged (mean) pressure values. The pressure values are normalized by the dynamic head according to Eq. 3.26. The reference pressure P0is usually taken as atmospheric pressure.

Cp=

P−P0

ρU2 (3.26)

The pressure coefficient (Cpe) distributions from the current study are shown in Fig.3.13along with other experimental and CFD investigations by many researchers including Bitsuamlak

et al. (2010). The results from the current CFD study on the upstream side of the onset of

flow separation lie with in the shaded area that signifies limits of acceptable range. The current standard k-epsilon model over shoots at the leading edge, where flow separation occurs, similar to the results ofWright & Easom(2003) who used the same turbulence model. This confirms the suspicion that RANSmodels can indeed have problems at flow separation zones. On the other hand, the currentLES model gives reasonable values even at the leading edge. On the top wall, the current LES model seem to underestimate the suction pressure compared tok−. This could be due to the use of a simple LES model with a constantCsthat needs to be adjusted based on the flow behaviour.

Chapter 4

Numerical evaluation of roughness effects

Atmospheric Boundary Layer (ABL)flow is affected by aerodynamic roughness that consists

of the effect of surface cover (roughness) as well as the shape of the terrain (topography). This chapter examines the effect of roughness alone by conductingComputational Fluid Dynamics

(CFD) simulations over various roughness setups. Given velocity and turbulence intensity

measurements at a certain location, it is possible determine roughness parameters z0 and d by fitting suitable profiles of either the log-law or power-law type. Some methods of fitting, with different degree of accuracy, have been discussed in section2.7.2. Therefore the task of determining roughness parameters can be considered to be equivalent to determining velocity and turbulence intensity profiles either from field observations or numerical simulations, which is the case in the current work. The investigation of roughness effects is conducted beginning from the lowest level of complexity, namely a flat terrain, and progresses to the case of a real built environment. Each model will be validated against existing literature and wind tunnel tests when available.

1. Complexity 0: Preliminary investigations on an empty domain

2. Complexity 1: Regularly arranged array of blocks similar to that used in wind tunnels. Empirical formulas for estimating roughness parameters based on density of obstacles are compared with currentCFDresults.

3. Complexity 2: The effect of inhomogeneous roughness, i.e. multiple roughness patches upstream of a site, is evaluated using three dimensional CFD simulations and results are compared against existing wind speed models. The simulations are carried out in a

Virtual Boundary Layer Wind Tunnel (V-BLWT)by duplicating all roughness features

used namely spires, blocks and barrier. Sixty nine cases tested byWang & Stathopoulos

(2007a) in wind tunnel are simulated and results are compared with wind speed models. 4. Complexity 3: The flow characteristics in a semi-idealized urban environment is studied by conducting model scale simulations and results are compared with existingBoundary

Layer Wind Tunnel (BLWT)test data

5. Complexity 4: Simulations over a real urban environment are conducted in an area in Downtown Miami. There is usually a lack of validation data for such kind of simulations thus the only qualitative discussion of results is made.

FinallyArtificial Neural Network (ANN) are considered as an alternative to setup roughness features in an actualBLWTfor a required wind profiles at the turn table. A neural network is trained with half of the dataset obtained fromRowan Williams Davies and Irwin Incorporation

(RWDI), and then the model is tested for prediction ability on the rest of the dataset.

4.1

Complexity 0: Empty domain

CFD enjoys a wide spread use in the wind engineering community however many parame- ters that influence the simulation results are not well understood (Franke & Hirsch 2004). A rather trivial case that is commonly used to demonstrate disparity between simulation results of differentCFDsoftware is the case of an empty domain. Since there are no obstacles, the char- acteristics of the wind should be maintained along the whole length of the domain. It may seem at first that simulation on an empty terrain is trivial but is quite challenging. The problem stems from difficulty of achieving horizontally homogeneous flow unless proper boundary conditions are used (Blocken et al. 2007,Hargreeves & Wright 2007, Richards & Hoxey 1993). This in- vestigation also helps to outline the steps involved in a typicalComputational wind engineering

(CWE)simulation.