How to Use Computational Fluid Dynamics Models for Urban Canopy Parameterizations - Meteorological and Air Quality Models for Urban Areas - page 27

4.3 The Role of CFD Models
With the term Computational Fluid Dynamics (CFD) models we usually indicate
numerical models that solves the Navier-Stokes equations over small domains (few
hundreds of meters at maximum), at high resolution (meters or less), and explicitly
resolve the buildings. There are two main types of such models:
1. Large Eddy Simulations (LES) models explicitly resolve the largest eddies, and
parameterize the effect of the sub grid features. Such models resolve time depen-
dent, spatially filtered Navier-Stokes equations. They may be quite heavy com-
putationally, in particular when a time average is needed to derive statistical
information.
2. Reynolds Averaged Navier Stokes (RANS) models that parameterize all the tur-
bulence, and resolve only the mean motions. Very often these models are solved
in a stationary state. Although in general such models are less computational
time demanding than LES, but they are less precise.
The ability of CFD models to reproduce microscale (e. g. building scale) airflow
behaviour in urban areas has been tested extensively in the last years, in particu-
lar over simplified geometries (wind tunnel cases, for example), with encouraging
results.
The procedure we propose to use CFD models is the following:
1. Validate CFD model results, by comparison with point measurements. Since the
spatial resolution of the models is of the order of meters, a comparison with
point measurements is meaningful. Measurements can come both from wind tun-
nel experiment (where the conditions are controlled) and field campaigns. Such
model intercomparison is useful to define the degree of confidence in the CFD
model results that we can expect.
2. Perform spatial averages of the CFD model results in order to derive averaged
variables comparable with those of the mesoscale models.
The spatially averaged variables can be used to test urban parameterization, or to
improve them (for example, deriving values for some constant needed in the param-
eterization, as it is the case of the drag coefficient), or to investigate the importance
of different physical mechanisms (e.g. dispersive stress). Moreover, CFD results can
be used to derive parameterizations of the subgrid spatial distributions of mean pol-
lutant concentration or of the variability due to turbulence. Information, that can be
both useful for exposure studies, for example.
The choice of the CFD approach to use (RANS vs. LES) can be influenced by
several factors: (1) the type of 'averaging' operator chosen in the mesoscale model.
If the definition (1) is used, for example, LES may give more useful information,
while if (2) is chosen, RANS results are probably enough. (2) The CPU time needed.
In order to do a parameter study with CFD, for example, RANS may be prefer-
able, since it allows performing more simulations over different configurations in a