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2 FiguresA GUI for urban wind ﬂow CFD analysis of small

scale wind applications

Anders Goude, Bahri Uzuno

˘

glu, Gabriele Giovannini

Department of Engineering Sciences, Division of Electricity

Centre for Renewable Electric Energy Conversion

Uppsala University, The

˚

Angstr

¨

om Laboratory

Box 534, 751 21 Uppsala, Sweden,

e-mail: anders.goude@angstrom.uu.se

e-mail: bahri.uzunoglu@angstrom.uu.se

Javier Magdalena and Antonio Fern

´

andez

SOLUTE Ingenieros

Av. Cerro del guila 3 Ediﬁcio 2, Of 3 Izd

28703 San Sebastin de los Reyes, Madrid

e-mail:javier.magdalena@solute.es

e-mail:antonio.fernandez@solute.es

Abstract—In order to have better insight into the physics

of the urban wind turbines, a graphical user interface (GUI)

that employs OpenFOAM ﬂow solver has been developed for

industrial applications by Uppsala University with Spanish en-

gineering company SOLUTE via EU framework as part of the

WINDUR framework 7 project. Urban wind resource assessment

for small scale wind applications present several challenges and

complexities for that are different from large-scale wind power

generation. Urban boundary layer relevant in this regime of

ﬂows have different horizontal proﬁles impacted by the buildings,

low speed wind regimes, separation and different turbulence

characteristics. This software addresses the project setup and

scientiﬁc visualization of the results for right investment decision

needs. Preliminary numerical results will be presented for a

test site in Huesca, Spain where a measurement campaign is

undertaken to validate the Computational Fluid Dynamics (CFD)

results.

Keywords—Urban ﬂow, Small scale wind, Graphical User

Interface (GUI), Computational Fluid Dynamics (CFD), Boundary

Layer, Buildings, OpenFOAM, Reynolds Averaged Navier Stokes

Equation (RANS)

I. INTRODUCTION

The small scale wind mainly corresponds to turbines

installed in rural and isolated areas. As 80 % of European

population lives in cities and the EU Directive 2010/31/EU on

Energy Performance of Buildings requires that Member States

shall ensure that by 31 December 2020 all new buildings are

nearly zero-energy buildings. This is a commercial opportunity

in terms of small scale wind applications while also provides

a motivation to investigate technical challenges related to the

peculiarities of Urban wind regime.

Urbanization brings with itself higher energy demand. Ver-

tically structured generation requires transmission from gener-

ation to urban areas where there is demand. In comparison

to this market structure, one of the advantages of distributed

generation from renewable energy sources such as wind is

local production and consumption. Local consumption of local

wind resources in urban areas can avoid losses, congestion and

infrastructure investment from transmission and distribution

and is preferable on other central power generation approaches

when this approach feasible. If suitable wind regimes can be

found in urban areas, it is possible to have right investment

decisions for small scale wind.

Project design for any wind project should not be too labor

intensive while the cost margins for the project management

employing Computational Fluid Dynamics (CFD) softwares

will require specialized expertise for problem setup and pre-

processing and post-processing tools. This problem which

has been well recognized for large scale wind industry, was

addressed by several commercially developed graphical user

interface (GUI) applications for large scale wind farms. These

GUI applications made use of generalized CFD solvers by

simplifying the labor and expertise required processes. The

usage of tools with similar GUI for small scale wind applica-

tions were limited. A GUI that can process roughness maps,

height contours and that can implement buildings for the urban

boundary layers without intervention of the expert user with

default parameter setup will address some of the aspects of

this gap. The graphical user interface (GUI) approach in this

study provides an user friendly tool based on the open source

CFD solver OpenFOAM, which signiﬁcantly simpliﬁes the

process of creating the simulation geometry and setting up the

simulation parameters, making the process much faster and

available to non-experts in CFD.

Urban wind regimes are relevant to urban boundary layer

research which investigates wind proﬁles and thermally driven

secondary circulations over cities [1] [2] [3] [4]. Some of the

characteristics of urban boundary layer in horizontal direction

involves large roughness elements with stored heat that has

reduced moisture in possible sealed areas that leads to higher

level of turbulence that creates a larger boundary layer [2]

[3] [4]. In this study heat ﬂuxes and heat islands are ignored

however they can be a signiﬁcant part of ﬂow characteristics in

large city formations where as mentioned above a secondary

circulation can be formed with winds toward city center near

the ground and uprising form there on. Independent urban

block structures at microscale are investigated. Microscale

ﬂow regime level will be more appropriate classiﬁcation for

the current study. Larger scale simulations where appropriate

boundary conditions for a section block of a larger urban

site block from a complete urban boundary layer will not be

investigated in this study.

Some of the characteristics of urban boundary layer in

vertical direction creates an internal boundary layer with

modiﬁed horizontal proﬁle. In this context, the urban boundary

layer can be classiﬁed into four main boundary layers in

2015 International Conference on Cyberworlds

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2015 International Conference on Cyberworlds

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193

vertical direction [1] [4] [5]. The highest boundary layer is

the geostrophic wind direction that is named Ekman layer.

Below this layer is the third layer what is named Prandtl

layer or surface layer in homogeneous ﬂows that will be

corresponding to constant ﬂux layer (CFL) or inertial sublayer

(IS) in urban ﬂows. For convectively dominated ﬂows, these

two upper layers become one mixing layer. The second layer

and ﬁrst layer respectively are wake layer that can go up-to ﬁve

times the building heights and the bottom urban canopy layer

(UCL) which can go up to average height of the buildings [1]

[4] [5]. The urban-roughness layer (URL) is the term used to

deﬁne these third and fourth layers jointly.

The urban-roughness layer proﬁle which is the most rele-

vant proﬁle for wind installations can be approximated by pro-

ﬁle laws using logarithmic wind proﬁle for neutral, stable and

unstable stratiﬁcation. However general formulations for ﬂat

terrain have been noted to be not accurate for complex ﬂows

like urban ﬂows as a result modiﬁcations were introduced.

For the Prandtl layer which is the third layer, the following

approximation can be used.

u =

u

∗

κ

ln

z − d

z

o

(1)

where u

∗

is characteristic velocity and κ is von karman

constant which is 0.41 and z

0

is roughness length, d is

displacement height which gives the vertical displacement of

the ﬂow for buildings [5] [6] [7] [8] [9] . Herein characteristic

velocity u

∗

can be derived from turbulence ﬂuctuations of

north east and west south components correlated with verti-

cal components or directly by inversion of geostrophic drag

law. The vertical height is denoted by z. Finally u will be

temporarily denote the wind speed here till it is redeﬁned to

be used as west to east wind component in the next section.

This relation has been modiﬁed for the second layer which

is wake layer with new parameter α to reﬂect the impact of

buildings as below

u = α

u

∗

κ

ln

z − d

z

o

. (2)

This was further modiﬁed for the ﬁrst layer as an exponential

rule for the bottom urban canopy layer (UCL)

u = u exp

a

z

h

− 1

(3)

where a is a constant which is dependent on building mor-

phology, z is the vertical height and h is the height of

the building [6] [7] [9]. All of these approximations have

large margins of error in the context of complex ﬂows of

urban ﬂows [6] [7] [9]. As a result, the limitation of this

algebraic relations can be overcomed by using partial differen-

tial equations in this context Computational Fluid Dynamics

(CFD) that will be discussed in the next section. Since the

CFD is labor intensive work, we have developed a graphical

user interface (GUI) for industrial applications that employs

an industry based CFD solver namely OpenFOAM solver

that has been developed by Uppsala University with Spanish

engineering company SOLUTE via EU framework as part

of the WINDUR framework 7 project. For CFD solutions,

steady state equations of ﬂuid mechanics will be used so all

the solutions are time averaged and as discussed in the next

section Reynolds Averaged Navier Stokes (RANS) equations

are going to be used in comparison to Large Eddy Simulation

(LES) which is computationally demanding [10] [11] [12].

RANS use time-averaged NS equations, and LES utilize space-

averaged (ﬁltered) equation [13]. Both approaches produce

turbulence ﬂuctuation terms that need to be modeled. The

main advantage of LES over the computationally more efﬁcient

RANS approach is the increased level of detail it can deliver.

The ability to predict instantaneous ﬂow characteristics and tur-

bulent ﬂow structures is particularly important in simulations

of transient phenomena however the computational demand

might not necessarily practical for ﬁnancial decision analysis

and business cycle of small scale wind installations while

RANS addresses the problem with much lower computational

resources.

II. T

HEORY AND DISCRETIZATION

For incompressible ﬂows, the general form of the Navier-

Stokes (NS) is given by

∂

∂x

j

[ρu

j

]=0 (4)

∂

∂t

(ρu

i

)+

∂

∂x

j

[ρu

i

u

j

+ pδ

ij

− τ

ij

]=0, (5)

i =1, 2, 3 (6)

where ρ deﬁnes density, p deﬁnes hydrostatic pressure, u

j

deﬁnes velocity, u

1

= u, u

2

= v, u

3

= w and x

j

spatial

coordinates and the stress tensor τ

ji

depends linearly on the

rate-of-strain tensor S

ij

, μ dynamic viscosity.

τ

ij

=2μS

ij

, (7)

and

S

ij

=

1

2

∂u

i

∂x

j

+

∂u

j

∂x

i

. (8)

In an appropriate time T interval if the velocity u is time

averaged:

u(x)=

1

T

t

0

+T

t

0

u(x, t)dt (9)

The averaged term and the ﬂuctuation term of the Reynolds

decomposition of velocity u =

u + u

that satisfy the

properties

u

=0, and u = u can simplify convective

term in the momentum Equation 12 that can be simpliﬁed

as

u

i

u

j

= u

i

u

j

+ u

i

u

j

= u

i

u

j

− τ

R

ij

where the ﬂuctuation

term is Reynolds stress tensor τ

R

ij

= −u

i

u

j

. After Reynolds

194194

decomposition of velocity, the time-averaged Navier-Stokes

(NS) equation is,

∂

∂x

j

[ρu

j

]=0(10)

∂

∂t

(ρ

u

i

)+

∂

∂x

j

ρ

u

i

u

j

+ pδ

ij

− τ

ij

+ τ

R

ij

=0, (11)

i =1, 2, 3. (12)

which is also titled Reynolds Averaged Navier-Stokes equation

(RANS) [14].

Turbulence modeling based on k and

The RANS model needs closure. In the derivation of

the time average on momentum equations, six additional

unknowns are introduced via Reynolds stress. To close this,

several approximations exist, one of them is k and model.

These equations carry variable k which is the turbulent kinetic

energy variable which can be derived from RANS equations

by multiplying x, y, z components with u, v, w components

and same will be trued for the other components which will

give the turbulent kinetic energy equations eventually. If the

same derivations is done for the mean kinetic energy K, one

will observe that the k and K equations will also carry a

turbulence production term which is negative in the mean K

and positive in k equations which shows mean kinetic energy

is destroyed and converted turbulence kinetic energy. Also the

dissipation term of k

j

in turbulence kinetic energy equations

gives negative contribution which is caused by the work done

against small eddies by viscous stresses which destroys the

turbulence kinetic energy. If this term is normalized by mass,

it is denoted as . The term can be expanded to another

set of transport equations based on empirical approaches and

this will be deﬁne the two equations model of k and . These

equations will be coupled to to RANS equations via turbulence

viscosity term in the Reynolds stress tensor via k and terms

that deﬁned the mixing length with an empirical constant [14].

III. S

IMULATION TOOL

In the present work, a simulation tool has been developed

to allow simulations in urban environments based on the

OpenFOAM solver [15] [16]. The tool is designed to allow

the user to input the geometry data including buildings and

surface roughness. This tool uses the built in mesh generation

tool snappyHexMesh from OpenFOAM, and the tool will both

suggest basic parameters to use for the mesh generation, and

also allow the user to manually change these for better control

of the mesh generation. The tool also handles all boundary

conditions, such as terrain roughness and the atmospheric

boundary layer. It is also capable of running the simulations

and plotting the velocity proﬁles at chosen evaluation points.

A. Discretization scheme

To describe theses set of equations non-linear transport

ﬂow equations in a computer, discretization needs to be

implemented. For discretization scheme, ﬁnite volume method

that divides the domain into a ﬁnite set of volumes by

deﬁning a volume integral over the continuous equations can

be implemented. One can create a discrete system of equations

that can be solved by iterative solutions in a computer by linear

algebra [15] [16] [14]. Some of the discretization methods that

are used for divergence, gradient and Laplacian operators are

summarized in a Table I .

For gradient terms, divergence terms and laplacian terms,

the Gauss keyword in Table I denotes the standard ﬁnite

volume discretisation of Gaussian integration that interpolates

values from cell centres to face centres.

In gradient terms, linear interpolation is used while this can

be limited to face or cell. Cell limited version is chosen. Cell

limiting denotes the limited gradient along a line connecting

adjacent cell centers while face limiting denotes the limited

gradient on the face itself. Gradient limiters otherwise known

as slope limiters are implemented to prevent spurious oscilla-

tions in the solution ﬂow ﬁeld by enforcing the monotonicity

principle by prohibiting the linearly reconstructed ﬁeld variable

on the cell faces to exceed the maximum or minimum values

of the neighboring cells. These limiters can be classiﬁed as

non-differentiable such as standard limiter, multidimensional

limiter, differentiable limiter. Gradient limiters can be cate-

gorized into two general groups: differentiable limiters and

non-differentiable limiters that contains standard limiter and

multidimensional limiter while minimum and maximum types

of functions for limiting the solution variables are used in this

form for these non-differentiable limiters [15] [16] [14].

For divergence terms, upwind discretization scheme is used

that makes use of ﬂow directional information in interpolation.

Before convergence is reached, divergence of velocity ﬁeld for

incompressible is not always zero. To include this bound term

within a numerical solution poses boundedness of the solution

variable and promotes better convergence so the bounded term

in Table I implement this [15] [16] [14].

TABLE I: Numerical discretization schemes

Category of mathematical terms Variables Schemes

interpolation schemes all linear

Gradient k cell limited Gauss linear 1

cell limited Gauss linear 1

The rest of variables Gauss linear

Divergence u

j

bounded Gauss linear upwindV

k bounded Gauss linear upwind

bounded Gauss linear upwind

the rest of variables bounded Gauss linear

Laplacian All Gauss linear limited

B. Solution and algorithm control

The discretized equations generated above can be used

linear-solver that solves a set of linear equations. Two of the

solvers are used here. Two solvers are smooth solver and

generalised geometric-algebraic multi-grid (GAMG). Smooth

solver employs well-known linear solvers such as Gauss-

Seidel, Diagonal incomplete-Cholesky (symmetric) and Di-

agonal incomplete-Cholesky with Gauss-Seidel (symmetric)

[15] [16] [14]. The generalised method of geometric-algebraic

multi-grid (GAMG) employs the principle cascade of quick

solution on a mesh starting with a small number of cells and

projecting these solutions onto a ﬁner mesh. The concept uses

an initial guess to obtain an accurate solution on the ﬁne

mesh [15] [16] [14]. This decreases the computational effort by

195195

(a) Graphical user interface for the urban ﬂow software -

building input

(b) Manual editor for mesh reﬁnements

(c) Post processing interface

Fig. 1: Graphical user interface for the urban ﬂow software

solving ﬁrst on coarser meshes while bypassing the additional

costs of mesh reﬁnement and projection of ﬁeld data. This

concept can be used as a preconditioner or as an independent

solver. The method used for each ﬁeld variable is summarized

in Table II. All of these parameters were implemented in

the graphical user interface also giving the user ﬂexibility to

further advances modiﬁcation of the solver settings.

TABLE II: Solver schemes

Variables Schemes

p GAMG

u

j

smoothSolver

k smoothSolver

smoothSolver

IV. GRAPHICAL USER INTERFACE

Simulation tools, such as OpenFOAM, have the capability

to perform the required simulations, but to properly set up a

simulation in from scratch requires a lot of work and good

knowledge about the simulation software. To make it possible

for a wider audience to use perform these simulations, it is

necessary to provide a user interface which have already been

conﬁgured for the chosen set of simulations. This is the main

purpose of the user interface presented in the current work.

This section will therefore describe the GUI and how it can

assist with these difﬁculties to make it easy to perform urban

simulations.

One initial step of setting up urban simulations is to create

the input geometry, which is the step where a GUI is most

useful, as manual generation of all input geometry ﬁles is not

feasible for the normal user. For urban simulations, both the

orography and the building geometries have to be taken into

account and given in the stereolithography (stl) ﬁle format,

which is used by OpenFOAM. These stl ﬁles contain a set of

triangles that describe the surfaces of the geometry. As this

is not the typical ﬁle format used to describe orography, the

GUI has to be able to generate these ﬁles from more common

orography data formats. It was chosen to use the traditional ﬁle

formats .map and .xyz for the import of orography data. In this

way, data available for other software such as industry standard

Wind resource and energy yield assessment software (WAsP)

.map format can also be used in the current model. These

formats do instead contain height contour lines (or possibly

only point data for the xyz format). From the points specifying

these contour lines, Delaunay triangulation

1

was used to create

a set of triangles, which describe the surface of the terrain and

can be saved in the geometry format used by OpenFOAM. In

the simulations presented in the current work, map ﬁles with

countour interval of 5 m were used for the site.

The second part of the geometry is to add the buildings.

For simple building shapes, the GUI can directly generate the

required geometry ﬁles, but to support advanced shapes, it is

suitable to allow import of CAD drawings as well. As it is

common that CAD software can export drawings in the stl

format, and this format is hence used as a possible input format

in the GUI. One key aspect of setting up the urban simulations

is to add the buildings to the correct geometric positions. A

CAD drawing will normally only contain the shape of the

building, but it is up to the GUI to modify this geometry data to

place the buildings at their correct positions, and also rotate the

buildings according to their real positions. The GUI supports

both moving buildings graphically by drag and drop, and by

manually giving building coordinates. One further step that is

required from the GUI is to calculate the ground level at each

building position and place the buildings at the correct height.

A snapshot of the GUI for building input is given in Figure 1a.

As the geometry ﬁles used for OpenFOAM are in the stl

format, which uses single precision, the ﬁnal step of the gener-

ation of geometry ﬁles for the GUI is to shift all geometries to

make the all simulation domains at zero, instead of using map

coordinates. This is to limit the effects of truncation errors,

1

http://www.codeproject.com/Articles/587629/A-Delaunay-triangulation-

function-in-C

196196

which otherwise could remove all details from the building

data.

Mesh generation in the GUI and solver is managed by

OpenFOAM structured and unstructured mesh generators. The

basic mesh generated by structured mesh generator is further

reﬁned by unstructured mesh generator for complex geometries

such as buildings. The GUI will provide a suggested set of

default reﬁnement parameters, to simplify for the user, but

for the advanced user, the mesh reﬁnement parameters can be

manually edited (Figure 1b ). Similar to the building input, the

GUI will also allow the user to select given positions, where

the velocity should be calculated. This information will also

be used to automatically reﬁne the mesh around these points

of interest.

The next step of setting up the simulations is to give

other simulation parameters, such as surface roughness and

velocity proﬁle. Surface roughness can be included, either as

a constant value, or by providing roughness data in either the

map format, or as structured xyz ﬁles. The velocity is given as

a logarithmic proﬁle, where the velocity at a reference height

can be given, which together with the roughness data, will

provide the velocity proﬁle.

The next step of the for the GUI is to take care of

all steps in the OpenFOAM execution, to allow the user to

run simulations by pushing a single button. The GUI will

ﬁrst call the mesh generation parts of OpenFOAM. When

the mesh is generated, the boundary conditions has to be

updated with respect to the new mesh. First, the ground level

for the logarithmic velocity proﬁle has to be updated for

each individual position, to make the logarithmic proﬁle start

from the correct position throughout the domain. If varying

roughness data is provided, this also has to be updated to give

each ground position the correct roughness value. Then the

GUI should call OpenFOAM to run the simulations. Finally,

the GUI should extract the simulation results at the given points

of interest. All these steps are handled automatically without

user inﬂuence. The GUI will provide support for running both

single simulations, and sector sweeps, where the wind direction

is changed between the simulations to generate data for wind-

rose plots.

The ﬁnal part of the GUI is to provide a simple post-

processing interface where the simulation results for the points

of interest can be seen. This is illustrated in Figure 1c,

where the turbulent kinetic energy k is plotted for the upwind

building.

V. N

UMERICAL RESULTS AND SIMULATION PARAMETERS

Huesca site in Spain that is displayed in in Figure 2a shows

the characteristics of the orography and buildings involved

for numerical development. The measurement campaign at

3.5m based on the design considerations of wind turbine

that is being developed is in progress on this site. There are

two measurement stations on this site for validation purposes

and our vertical proﬁles are going to be proﬁled form these

positions for a ﬁxed wind speed.

The orography was imported from map ﬁles for the site

with contour interval of 5 m. The buildings were drawn

using traditional CAD software and imported into the GUI.

(a) EDIF 3 and EDIF 4 in Huesca that are being simu-

lated.

(b) The buildings in Huesca that have met masts namely

EDIF 3 and EDIF 4 in urban ﬂow simulator.

Fig. 2: The simulation set-up and visualization in urban ﬂow

software a) EDIF 3 and EDIF 4 buildings that are being

simulated in Huesca. b) Graphical user interface for the urban

ﬂow software. c) Velocity magnitude ﬁeld view from Paraview

for the EDIF 3 and EDIF 4 buildings that have met masts.

To avoid excessive computational effort, only the important

buildings were drawn in detail, and the remaining buildings

were simpliﬁed to avoid having too many mesh points to

describe these buildings. All buildings were created with 10

m extra height, and were then shifted 10 m into the ground

to account for possible slopes in the terrain, which otherwise

could give empty space below parts of the building.

The simulation domain was chosen to extend up and

downstream 1000 m and crosstream 800 m from each of the

two positions where met masts are installed. This gives a

domain size of 2126 x 1602 m. The simulation height is chosen

to extend 1000 m above the highest point of the terrain. By

using the meshing model of OpenFOAM, the domain is ﬁrst

divided into uniform base mesh, in this case 28 x 21 x 14

elements. Then, for the important regions, the mesh is reﬁned.

In the current method, the mesh was reﬁned close to the terrain

and close to the buildings. Mesh reﬁnements up to 8 times (i.e.

each block is divided into 8 smaller boxes 8 times) were used,

although the majority of the 6.9 million mesh cells were at

reﬁnement level 6.

197197

(a) The mesh used in the simulations.

(b) The wind speed magnitudes.

Fig. 3: Simulation set-up a) the mesh used in the simulations

b) the wind speed magnitudes.

The boundary conditions used was to use the logarithmic

boundary layer for the inlet velocity since the ﬂow simulations

were always started from ﬂat terrain where displacement height

is d =0. Here, the asymptotic ﬂow velocity was set to 10 m/s

at 1000 m height from EDIF 4 to EDIF3 West to East direction.

The surface roughness was estimated to be z

0

=0.05 which

corresponds to a ground with bushes. This was chosen from

studying satellite images of the surrounding terrain. Rough

wall boundary conditions were also used for the terrain to

include the effects of surface roughness here as well. The

preliminary velocity proﬁles on the roof of the buildings are

presented in Figure 4a and 4b which shows the signiﬁcant

impact of one building on the downstream building proﬁles

for meteorological mast locations which are marked with red

in the ﬁgures where vertical proﬁles are analyzed.

VI. C

ONCLUSIONS

To meet the needs of the wind industry, a novel GUI with

several numerical solver, discretization and mesh generation

options via OpenFOAM open source for a user friendly envi-

ronment is implemented. The approach combines advantages

of open source solver approach with a user friendly interface.

The preliminary results are presented for a site in Spain namely

Huesca where a measurement campaign is in progress to

validate the numerical results. The ﬁnal work will include

0 1 2 3 4 5 6

0

2

4

6

8

10

Horizontal wind speed

Height from the roof of the building

Last building EDIF 3

First building EDIF 4

(a) The velocity proﬁle for EDIF 3 and EDIF 4 when both buildings

are conﬁgured in simulations.

0 2 4 6 8

0

5

10

15

20

Horizontal wind speed

Height from the roof of the building

Both buildings EDIF 3 AND EDIF 4

One building EDIF 3

(b) The EDIF 3 velocity proﬁle with EDIF 3 with and without EDIF 4.

Fig. 4: Velocity proﬁle comparisons for meteorological mast

locations which are marked with red in the ﬁgures where

vertical proﬁles are analyzed a) the velocity proﬁle with two

buildings EDIF 3 and EDIF 4 same as above b) the velocity

proﬁle for EDIF 3 simulation only without EDIF 4.

the validation exercise with the measurement campaign. The

software addresses microscale urban ﬂow regimes in urban

roughness layer where complex geometries of buildings create

several physical phenomena where simple proﬁles might not

be sufﬁcient.

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