2.1. LES Model and Numerical Experiments
For the numerical simulations in our study, we used the LES model PALM [
17], revision 2705. PALM has been already successfully used to simulate the flow in urban environments in high detail, e.g., [
14,
18,
19,
20,
21,
22]. Also, PALM provides the possibility to represent three-dimensional building topologies and includes an embedded Lagrangian particle model, making it well-suited to study pollutant dispersion and ventilation in urban environments. PALM solves the non-hydrostatic incompressible Boussinesq equations. For the subgrid model, the kinetic energy scheme of Deardorff [
23] was used. The advection terms were discretized by a fifth-order scheme [
24], while near solid walls the order of the scheme was successively degraded. For the time discretization a third-order Runge-Kutta scheme by Williamson [
25] was used.
The model domain consists of several building patches that are aligned and shifted in rows as depicted in
Figure 1. A single patch consists of a building containing a courtyard and an adjacent street at its southern and western wall. To study effects of different building/courtyard geometries on courtyard ventilation, we performed three simulations with different courtyard aspect ratio (AR, ratio of building height, or courtyard depth,
H, to courtyard width
W), which are listed in
Table 1. The case with
, where
, as well as the cases with high and low AR are hereafter referred to as “AR1”, “AR3” and “AR03”, respectively. The case AR1 was chosen to link to other studies as this is the most famous case throughout other research, e.g., [
2,
7,
9,
10]. The other two cases, AR03 and AR3, were chosen as they showed the most pronounced differences in scalar concentrations compared to the AR1 case within the study by Hall et al. [
2]. The building within a patch has a single lateral opening either on its western, eastern, northern or southern wall, or has no opening at all, i.e., it is closed. These patches are labelled as “W”, “E”, “N”, “S”, and “C”, respectively. The lateral opening has a size of 4
by 4
and is always located at the bottom center of the respective building wall to represent an entrance to the courtyard. The size of the opening is chosen according to our personal experience, assumed to be typical for mid-European city quarters, even though we note that openings vary in size in real cities, depending on the prevailing architecture. Also, different sized openings at other locations of a building wall might also appear within a real city. However, in this idealized study, we tried to keep the setup simple, to limit the number of effects and hence the complexity of the results.
A row of buildings is then formed by aligning five building patches, with each patch along the
y-direction having a different courtyard/opening configuration (see
Figure 1). The street width was set to 20
for all streets and the building-wall thickness was set to 20
for all buildings in all simulated cases.
For cases AR1 and AR3, the domain consists of six rows, while three neighboring rows are shifted along the
y-direction, forming a front, center and back row with respect to the
x-parallel flow from the West (see
Figure 1b). For case AR03, the center rows are missing resulting in only four rows within the domain (see
Figure 1c). This was done to keep the length of the
x-parallel (wind-parallel) streets constant throughout the different cases (compare
Figure 1b,c). This ensures that in all simulated setups the flow can accelerate the same distance and has the same input of scalar (see details below) along the
x-parallel streets.
We have chosen this staggered building setup to break-up the street canyons along the
x-direction, to prevent artificial jets that would develop along the infinite
x-parallel street canyons, as we did observe in preparatory test simulations. Hence, we optimized our setup to prevent such unrealistic long streets. Furthermore, these setups allow study of different courtyard realizations within a single simulation, which requires significantly less computational effort compared to study each courtyard realization in a single simulation individually, which would not be possible without increasing the grid spacing. To study different courtyard realizations in one simulation, however, requires that the flow and scalar distribution within the different courtyard cavities are statistically independent from each other. Indeed, an analysis of velocity and scalar variances within identical courtyards but different upstream courtyards revealed no significant differences (not shown). Hence, we are confident that the impact of a lateral opening is limited to the courtyard cavity itself and the part of the street directly adjacent to the opening. This is only true as long as lateral openings do not directly face each other, hence, we avoided this configuration in our building layout (see
Figure 1).
At this point, we would like to note that the studied building configuration is highly idealized. In reality, buildings would vary in height and orientation, and courtyard configurations and opening sizes would be more heterogeneous. However, as we try to focus purely on effects of openings on courtyard ventilation and pollution, we idealized the building setup to isolate those effects, while still trying to mimic an urban environment with neighboring buildings.
In total, the domain size adds up to 480 in the x-direction and 400 (case AR03: 600 ) in the y-direction with a domain height of 531 . A rectilinear grid with an isotropic grid spacing of within the lower 200 of the domain was used. To save computational costs, the vertical grid spacing (along z-direction) above 200 was stretched by a factor of until it reached 4 at a height of 247 , from where on it was kept constant up to the domain top. Overall, the domain consisted of 1200 by 1000 by 602 grid cells (case AR03: 1200 by 1500 by 602) in x- y- and z-direction, respectively.
The simulations were initialized with a logarithmic wind profile reported by Hall et al. [
2] and driven by a horizontal pressure gradient of
along the
x-direction, resulting in a mean wind speed of
at
during the analysis period with flow parallel to the
x-direction, while almost constant wind speed during the analysis period.
At the lateral boundaries, we used cyclic conditions at the spanwise boundaries and shifted cyclic conditions according to Munters et al. [
26] at the streamwise boundaries. The shifted cyclic condition was used to prevent the generation of streamwise-elongated coherent structures that can appear if pure cyclic conditions are applied [
26]. The shifting distance along the
y-direction was set to the size of a single building patch, hence, to 80
in case AR1 and AR3 and 120
in case AR03. Free-slip boundary conditions were applied at the domain top. As surface boundary condition for the momentum equations (at Earth and building surfaces), Monin–Obukhov similarity theory (MOST) was applied locally between the surface and the first grid point normal to the respective surface orientation. This applies for all surfaces, i.e., at horizontal upward- and downward-facing surfaces (at the top of the lateral opening), as well as at vertical surfaces, following Park et al. [
20] and Park et al. [
27]. The boundary layer in our simulations is purely shear-driven, i.e., we solved no equation for the temperature or humidity.
To investigate dispersion of pollutants, e.g., from car exhausts, into the courtyards, we considered line sources of passive scalar within the street canyons, as indicated by the
red lines in
Figure 1. These sources emulate a time-constant, Gaussian-shaped surface scalar flux along the center line of the streets. This way, we simulated the pollutant release from traffic within the street canyons which allows us to investigate how such pollutants are transported into the courtyards. Surface fluxes were identical on all streets, i.e., we did not distinguish between main and side streets with different traffic density. Besides directly simulating pollutant sources, also other concepts exist which evaluate the ventilation of the urban environment such as the concept of air delay [
28]. The air delay gives an estimate of how long a specific air parcel resides within the urban environment and this way concludes the ventilation. However, the main focus of this study is to evaluate how pollutants are advected from outer sources into the courtyard cavity while lateral openings are considered. Therefore, a direct simulation of scalar sources is superior to indirect measurements.
The total simulation time for all cases was 4 . This includes 2 spin-up time and 2 analysis time.
Presented data were averaged over the analysis period (2 ) as well as over identical courtyards (in a simulation, there are two identical courtyard realizations). Before time-averaging, scalar concentration s was normalized by the time-dependent background concentration , which is defined as the domain-averaged concentration at . The normalization was done to account for any time dependencies in scalar concentration due to the time-constant scalar flux.
To investigate the relative occurrence of high concentrations within the courtyards and how these depend on the lateral openings, we calculated probability density functions (PDF) for the scalar concentration. Concentrations were sampled at the courtyard center at a height of at each time step during the analysis period. The sampled concentrations were then normalized by .
Finally, in the following, we refer to courtyards with westward lateral opening in the front, center, and back row as “W front”, “W center”, and “W back”, respectively (equivalent for the other opening orientations, see
Figure 1).
The used model parameter lists for PALM for all described cases, as well as the additional code parts used for data analysis in this study are included in the
Supplementary Materials.
2.2. Balance Term Analysis
To study courtyard pollution in more detail and distinguish between mixing of scalar into the courtyard through the top opening and the lateral opening, we examined the terms of the time-averaged scalar transport equation
where the left-hand side describes the local temporal change of passive scalar
s. The first and the second terms on the right-hand side describe the resolved-scale transport of
s in horizontal as well as in vertical direction, respectively, with
being the horizontal velocity components and
being the vertical component. The third and fourth terms on the right-hand side are the parametrized turbulent transport on the subgrid scale in horizontal (
) and vertical direction (
), respectively. The overbar indicates the time-averaging. To be consistent with the numerical discretization, we directly used the flux divergence provided by the advection scheme and the subgrid-scale parametrization. As no sources or sinks of passive scalar exist within the courtyard volume, the entire passive scalar is entrained into the courtyard via the openings, so that we can make use of Gauss’s theorem to calculate the net transport. Thus, integrating Equation (
1) over the entire courtyard volume leads to the mean net transport of scalar along the respective spatial direction,
with
V indicating the entire courtyard volume up to
. The left-hand side describes the temporal mean accumulation of scalar within the courtyard volume, which is, however, relatively small compared to the terms on the right-hand side. The first term on the right-hand side of Equation (
2) gives the net transport of scalar via the lateral opening (
), while the second term gives the vertical net transport of scalar via the top opening (
). A positive value indicates an increase of passive scalar, while a negative value indicates a decrease of passive scalar. For closed courtyards, the
vanishes so that scalar accumulation is only due to the vertical transport.
2.3. Evaluation of Pollutant Residence Times
The impact of pollutants on human health depends, among other factors, on the time humans are exposed to these pollutants [
4,
5], or, in other words, how long pollutants reside within the courtyard cavity. To estimate the residence times of pollutants within courtyards, we followed Lo and Ngan [
6] and used a Lagrangian particle model embedded into the LES model, where the residence time is defined as the time elapsed between the entry of a particle into the region of interest and its exit [
29]. Although the Lagrangian particle model requires higher computational resources, it allows us to directly measure the residence time and therefore gives more reliable results than indirect measurements retrieved from scalar concentration values as, e.g., when analyzing the air delay.
The embedded Lagrangian particle model is based on Weil et al. [
30], to separate the particle speed into a deterministic and a stochastic contribution, which corresponds to dividing the turbulent flow field into a resolved-scale and a subgrid-scale (SGS) portion, respectively, following the LES philosophy. The resolved-scale velocity is provided by the LES at each time step, while the SGS velocity is predicted by integrating a stochastic differential equation according to Weil et al. [
30], who strictly adopted the Thomson [
31] model to the subgrid scale by assuming isotropic and Gaussian-distributed turbulence. To parametrize the stochastic particle dispersion on the subgrid scale, the LES provides local values of the SGS turbulent kinetic energy and the dissipation rate at each time step.
According to Steinfeld et al. [
32], the LES data are interpolated bi-linearly on the actual particle position in the horizontal. In the vertical, a linear interpolation is used, except for the particles located between the surface and the first grid level, where a logarithmic interpolation according to local MOST for the resolved-scale horizontal velocity components is applied. At the solid boundaries, i.e., upward-and downward-facing as well as vertical building surfaces, we used a reflection boundary condition for the particles, and cyclic conditions at the lateral boundaries. A more detailed description of the particle model embedded into the LES model is given by Steinfeld et al. [
32] and Maronga et al. [
17].
Following Lo and Ngan [
6], we calculated the residence time of a particle by summing-up the total time the particle spent within the courtyard volume. Once a particle exits the courtyard volume by the lateral or the top opening, the particle age is stored and the particle itself is immediately removed from the simulation. Particles were released within the courtyard each LES time step at a height of
. This particle-source height should be representative for human exposure. Setting the particle sources within the courtyard only, has the advantage that fewer particles need to be modelled to obtain sufficient statistics, compared to the case where particle sources are along the street canyons and only a small portion of the particles would be mixed into the courtyards.
Physically, larger residence times indicate less turbulent mixing and ventilation of the courtyard. This in turn indicates larger impact of pollutants on human health in case of high concentrations, compared to smaller residence times [
6]. Please note that we focus on residence times only, which is a pure ventilation measure. Following Lo and Ngan [
6], however, the exposure time is a more direct measure to relate the impact of pollutants on human health as it also considers re-entrainment of particles into the region of interest, which the residence time does not. However, Lo and Ngan [
6] showed that the number of re-entrainment events is relatively small, so that we decided to focus on the residence times, for the sake of simplicity and computational effort.
At this point, we want to note that Lo and Ngan [
6] did observe particle accumulation near solid walls in their study (also using PALM) and excluded these regions from their analysis. In preparatory studies, we could observe similar particle accumulation near solid walls. This accumulation could be traced back to an erroneous treatment of SGS particle velocities near solid walls, which was fixed in PALM revision 2418. In the following, we could not observe particle accumulation near solid walls any more.
2.4. Validation and Grid Sensitivity
To prove PALM’s capability to correctly represent the flow within a courtyard cavity, we first compare simulation results against data from wind-tunnel experiments by Hall et al. [
2] and Reynolds-averaged simulations by Ryu and Baik [
9]. The validation setup consists of a single building with a closed courtyard (no lateral opening) and an undisturbed incoming flow according to the wind-tunnel experiments shown by Hall et al. [
2]. The building setup (height, width) is similar to a single building of case AR1 with its center placed at
. The domain size for this case is 620
by 288
by 240
in the
x-,
y-, and
z-direction, respectively, with an isotropic grid spacing of
. The simulation time was 4
. The complete parameter list of this simulation is included in the
Supplementary Materials.
Figure 2 shows profiles of the normalized and time-averaged
u-component of the wind speed and its standard deviation. Within the courtyard cavity,
u agrees well with the data from Ryu and Baik [
9] and Hall et al. [
2], except for the lower half of the courtyard where Hall et al. [
2] reported a higher negative
u-component, indicating a stronger re-circulation within the courtyard cavity. Above the cavity, the simulated
u-profile agrees fairly well with the observed wind-tunnel data. The variation
can only be compared with the wind-tunnel measurement of Hall et al. [
2] as it was not reported by Ryu and Baik [
9]. The simulated
is about
within the courtyard cavity, while Hall et al. [
2] reported larger values of around
within the cavity. However, other courtyard setups with larger and smaller ARs reported by Hall et al. [
2] showed significantly lower values which are in better agreement with our validation case. Since the mean wind profile is in good agreement with the data reported by Hall et al. [
2] and Ryu and Baik [
9] and
shows qualitatively good agreement with profiles of Hall et al. [
2], we are confident that PALM is capable of correctly simulating the flow inside the courtyard cavity.
The scalar dispersion simulated by PALM was previously validated by Park et al. [
20] via wind-tunnel data for a street-canyon case and is therefore not validated again in the current study.
By definition, the results of an LES with implicit filtering, as used in PALM, depend on the grid spacing [
33,
34]. In practice, the question is whether the analyzed statistical moments converge towards finer grid spacing. Hence, we performed a grid sensitivity study where we conducted a simulation of a single building patch as described above (domain size:
), including a courtyard with an opening in wind direction. Three different grid sizes (1
, 0.4
and 0.2
) were considered and the simulation time was 10
. Other simulation parameters were identical to the main simulations (a detailed parameter list is included in the
Supplementary Materials).
Figure 3a,c show the mean wind speed within the courtyard cavity and along the center line of the opening, respectively. The mean wind speed differs most between the simulation with 1
and
grid spacing, while the differences between
and
grid spacing are only small. A similar behavior can be observed for the standard deviation
(
Figure 3b,d), even though there are still small differences between
and
grid spacing. Hence, as a grid spacing of
does not yield to significantly different results compared to a grid spacing of
but would significantly increase the computational demands, we chose a grid spacing of
for all following simulations.