Ng. Wright et al., Development and validation of a non-linear k-epsilon: model for flow over a full-scale building, WIND STRUCT, 4(3), 2001, pp. 177-196
At present the most popular turbulence models used for engineering solution
s to flow problems are the k-epsilon and Reynolds stress models. The shortc
oming of these models based on the isotropic eddy viscosity concept and Rey
nolds averaging in flow fields of the type found in the field of Wind Engin
eering are well documented. In view of these shortcomings this paper presen
ts the implementation of a non-linear model and its evaluation for flow aro
und a building. Tests were undertaken using the classical bluff body shape,
a surface mounted cube, with orientations both normal and skewed at 45 deg
rees to the incident wind. Full-scale investigations have been undertaken a
t the Silsoe Research Institute with a 6 m surface mounted cube and a fetch
of roughness height equal to 0.01 m. All tests were originally undertaken
for a number of turbulence models including the standard, RNG and MMK k-eps
ilon models and the differential stress model. The sensitivity of the CFD r
esults to a number of solver parameters was tested. The accuracy of the tur
bulence model used was deduced by comparison to the full-scale predicted ro
of and wake recirculation zone lengths. Mean values of the predicted pressu
re coefficients were used to further validate the turbulence models. Prelim
inary comparisons have also been made with available published experimental
and large eddy simulation data. Initial investigations suggested that a su
itable turbulence model should be able to model the anisotropy of turbulent
flow such as the Reynolds stress model whilst maintaining the ease of use
and computational stability of the two equations models. Therefore developm
ent work concentrated on non-linear quadratic and cubic expansions of the B
oussinesq eddy viscosity assumption. Comparisons of these with models based
on an isotropic assumption are presented along with comparisons with measu
red data.