Analytical Review of Wind Assessment Tools for Urban Wind Turbine Applications

Abstract

Due to the complex nature of the built environment, urban wind flow is unpredictable and characterised by high levels of turbulence and low mean wind speed. Yet, there is a potential for harnessing urban wind power by carefully integrating wind turbines within the built environment at the optimum locations. This requires a thorough investigation of wind resources to use the suitable wind turbine technology at the correct location—thus, the need for an accurate assessment of wind resources at the proposed site. This paper reviews the commonly used wind assessment tools for the urban wind flow to identify the optimum tool to be used prior to integrating wind turbines in urban areas. In situ measurements, wind tunnel tests, and CFD simulations are analysed and reviewed through their advantages and disadvantages in assessing urban wind flows. The literature shows that CFD simulations are favoured over other most commonly used tools because the tool is relatively easier to use, more efficient in comparing alternative design solutions, and can effectively communicate data visually. The paper concludes with recommendations on best practice guidelines for using CFD simulation in assessing the wind flow within the built environment and emphasises the importance of validating CFD simulation results by other available tools to avoid any associated uncertainties.

Full text

Citation: Abohela, I.; Sundararajan, R.
Analytical Review of Wind
Assessment Tools for Urban Wind
Turbine Applications. Atmosphere
2024,15, 1049. https://doi.org/
10.3390/atmos15091049
Academic Editors: Yi-Shuai Ren and
Yong Jiang
Received: 2 July 2024
Revised: 14 August 2024
Accepted: 21 August 2024
Published: 30 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
atmosphere
Review
Analytical Review of Wind Assessment Tools for Urban Wind
Turbine Applications
Islam Abohela 1, * and Raveendran Sundararajan 2
1Department of Creative Industries, School of Digital, Technology, Innovation and Business,
Staffordshire University, Stoke-on-Trent ST4 2DE, UK
2School of Engineering, London South Bank University, London SE1 0AA, UK; r.sundar@lsbu.ac.uk
*Correspondence: islam.abohela@staffs.ac.uk
Abstract: Due to the complex nature of the built environment, urban wind flow is unpredictable and
characterised by high levels of turbulence and low mean wind speed. Yet, there is a potential for
harnessing urban wind power by carefully integrating wind turbines within the built environment
at the optimum locations. This requires a thorough investigation of wind resources to use the
suitable wind turbine technology at the correct location—thus, the need for an accurate assessment
of wind resources at the proposed site. This paper reviews the commonly used wind assessment
tools for the urban wind flow to identify the optimum tool to be used prior to integrating wind
turbines in urban areas. In situ measurements, wind tunnel tests, and CFD simulations are analysed
and reviewed through their advantages and disadvantages in assessing urban wind flows. The
literature shows that CFD simulations are favoured over other most commonly used tools because
the tool is relatively easier to use, more efficient in comparing alternative design solutions, and can
effectively communicate data visually. The paper concludes with recommendations on best practice
guidelines for using CFD simulation in assessing the wind flow within the built environment and
emphasises the importance of validating CFD simulation results by other available tools to avoid any
associated uncertainties.
Keywords: urban wind turbines; buildings; wind tunnel; in situ measurements; CFD; building
performance simulation
1. Introduction
Urban wind turbines have a high potential for in situ power generation since the
power transmission losses are significantly reduced due to using the power where it is
generated [
1
]. They can contribute to a more sustainable built environment by making
buildings closer to being self-sufficient in terms of energy; however, the main challenge
lies in the uncertainty associated with integrating wind turbines within the built environ-
ment [
2
]. This uncertainty stems from the poor performance of urban wind turbines due to
the unpredictable nature of wind within the built environment, which is characterised by
high turbulence and a low mean wind speed [3,4].
However, it is evident that assessing the wind resources at a particular urban site
could inform the design team about the optimum mounting/integration locations of wind
turbines. Moreover, it could take advantage of the accelerating effect, which happens when
wind collides with an obstacle [
5
,
6
]. Assessing the wind condition at a particular site will
help identify specific locations where the wind flow is less turbulent and the mean wind
velocity is higher. Mounting the wind turbines in these locations will result in a significant
increase in the energy yield [7,8].
Accordingly, understanding the wind flow at the mounting location plays an impor-
tant role in estimating the performance of the proposed wind turbine. The energy yield
will depend on factors such as local wind flow conditions, including urban settings, surface
Atmosphere 2024,15, 1049. https://doi.org/10.3390/atmos15091049 https://www.mdpi.com/journal/atmosphere

Atmosphere 2024,15, 1049 2 of 27
cover, and vegetation [
9
,
10
]. In addition to local wind flow conditions, there are other
factors affecting the successful integration of wind turbines, and these factors include
the geographical distribution of wind speeds, characteristic parameters of the wind, to-
pography, and measurement of the mean wind speed [
11
]. With the aid of Geographic
Information Systems (GIS), informed decisions about optimum mounting locations could
be reached [
12
]. Moreover, the emergence of multiple artificial intelligence (AI) appli-
cations and machine learning (ML) techniques increases confidence in predicting urban
wind flows [
13
]. In a recent investigation, comparing computational fluid dynamics (CFD)
simulation of the pedestrian level wind speed to the results of an ML-based tool using
an image similarity assessment method, there was significant agreement between both
tools [
14
]. GIS, AI, and ML could be implemented in conjunction with the most commonly
used urban wind assessment tools for more accurate results.
A variety of tools are available for assessing the wind flow within the built environ-
ment, with the most common tools being in situ measurements, wind tunnel tests, and
CFD simulations [
15
]. This paper analyses and reviews these tools to identify the optimum
tool to be used in assessing urban wind flow. This was carried out by reviewing the pros
and cons of each wind assessment tool for the purpose of integrating wind turbines in
urban areas.
Accordingly, this article consists of three main sections: (a) understanding wind
resources at the installation site considering the macro- and microscale wind conditions;
(b) investigation of the most commonly used urban wind assessment tools for studying the
wind flow within the built environment, discussing pros and cons of each wind assessment
tool to identify areas of application; And (c) in-depth investigation of the proposed urban
wind assessment tool with a focus on how to use the tool for obtaining reliable results. The
paper concludes with a set of guidelines for using the recommended tool to yield reliable
results when studying wind flow in urban areas.
2. Estimation of Wind Resources
Wind resources in any specific location are determined by macroscale and mesoscale
wind conditions. As for urban areas, the microscale wind conditions are the main factor
affecting wind resources. This is strongly dependant on the geometry of the buildings as
well as the urban setting. Local weather stations can provide reliable information about
macroscale and mesoscale conditions. However, they cannot provide more detailed data on
microscale wind conditions as this has to be collected using specific wind assessment tools.
Early investigation of the microscale conditions can affect the design geometry to utilise
the local wind for wind power generation, passive cooling, and wind load analysis [
16
,
17
].
The main factor affecting the feasibility of integrating wind turbines in urban areas is
wind velocity. However, turbulence, which occurs due to the interaction between wind
and obstacles in the form of buildings, also affects the energy yield from the integrated
wind turbines. Areas with high levels of turbulence should be avoided when considering
urban wind turbine installations. High levels of turbulence will result in a reduction in the
power output from the wind turbine. To identify areas of high mean wind velocity and low
levels of turbulence, wind assessment tools are used to inform the decisions of the optimum
mounting/integration locations of wind turbines within the built environment [6].
2.1. Macroscale Wind Conditions
Different geographic locations have different climates and wind conditions. The
movement of air (wind) is governed by the natural phenomenon of differential temperature
between the poles and the equator. The temperature difference results in areas of different
pressure values, which causes the air to flow from the areas of high pressure to areas of
low pressure, creating air currents, commonly known as the prevailing wind. As seen in
Figure 1, isobars represent areas of different air pressure values [
18
–
20
]. At higher altitudes
ranging between one thousand and two thousand meters, the earth’s rotation as well as the
curvature of the earth’s surface affects air movement. These forces are ineffective in the

Atmosphere 2024,15, 1049 3 of 27
atmospheric boundary layer where the frictional forces and drag forces are more prominent
at the surface [21].
Atmosphere 2024, 15, x FOR PEER REVIEW 3 of 28
seen in Figure 1, isobars represent areas of different air pressure values [18–20]. At higher
altitudes ranging between one thousand and two thousand meters, the earth’s rotation as
well as the curvature of the earth’s surface affects air movement. These forces are ineffec-
tive in the atmospheric boundary layer where the frictional forces and drag forces are
more prominent at the surface [21].
Figure 1. Isobars shown as contour lines on a weather map. (Source: hps://www.metof-
fice.gov.uk/weather/maps-and-charts/surface-pressure, accessed: 10 August 2024).
Global and regional jet streams are natural phenomena resulting from air movement
because of temperature differences. The regional phenomenon is determined by oro-
graphic conditions, e.g., the surface structure of the area, and by global phenomena. The
wind conditions in this area, known as the boundary layer, are influenced by the energy
transferred from the undisturbed high-energy stream of the geostrophic wind to the layers
below as well as by regional conditions. Turbulence is found near the ground due to the
increased roughness [22,23].
These phenomena affect the three main factors in assessing wind energy resources:
The first is the mean wind speed per annum, which is dependent on the terrain roughness
the wind passes by to reach the studied location and the prevailing wind at a specific
location. The second is the wind speed distribution profile, which is the frequency of dif-
ferent wind speeds throughout the year. This factor is necessary for determining the
power available in the wind since the energy available in the wind is directly proportional
to the cube of the wind speed. The third is the wind direction, which is of paramount
importance when thinking of wind power generation in urban areas.
If the type of integration is building-augmented wind turbines, the whole building
will be oriented towards the prevailing wind direction to collect the maximum amount of
wind since the yawing system of the turbine will not be possible in this scenario. In the
case of roof-mounted wind turbines or building-integrated wind turbines, the yawing sys-
tem could be operational, and the turbine could yaw to face the prevailing wind [24].
2.2. Microscale Wind Conditions
Terrain roughness, different elements forming the built environment, and geographic
features are the factors playing an important role in formulating the microscale wind con-
ditions. The wind flow around buildings is affected greatly by terrain roughness [21,25].
The main types of terrains corresponding to three aerodynamic roughness parameters are
(z
0
) as follows: city centre terrain (z
0
> 0.7), suburban terrain (z
0
= 0.25–0.3), and open field
terrain (z
0
= 0.01–0.03). Based on terrain roughness, the atmospheric boundary layer (ABL)
profile, which has the shape of a power law curve, is displaced at a distance d from the
Figure 1. Isobars shown as contour lines on a weather map. (Source: https://www.metoffice.gov.uk/
weather/maps-and-charts/surface-pressure, accessed: 10 August 2024).
Global and regional jet streams are natural phenomena resulting from air movement
because of temperature differences. The regional phenomenon is determined by orographic
conditions, e.g., the surface structure of the area, and by global phenomena. The wind con-
ditions in this area, known as the boundary layer, are influenced by the energy transferred
from the undisturbed high-energy stream of the geostrophic wind to the layers below as
well as by regional conditions. Turbulence is found near the ground due to the increased
roughness [22,23].
These phenomena affect the three main factors in assessing wind energy resources:
The first is the mean wind speed per annum, which is dependent on the terrain roughness
the wind passes by to reach the studied location and the prevailing wind at a specific
location. The second is the wind speed distribution profile, which is the frequency of
different wind speeds throughout the year. This factor is necessary for determining the
power available in the wind since the energy available in the wind is directly proportional
to the cube of the wind speed. The third is the wind direction, which is of paramount
importance when thinking of wind power generation in urban areas.
If the type of integration is building-augmented wind turbines, the whole building
will be oriented towards the prevailing wind direction to collect the maximum amount of
wind since the yawing system of the turbine will not be possible in this scenario. In the case
of roof-mounted wind turbines or building-integrated wind turbines, the yawing system
could be operational, and the turbine could yaw to face the prevailing wind [24].
2.2. Microscale Wind Conditions
Terrain roughness, different elements forming the built environment, and geographic
features are the factors playing an important role in formulating the microscale wind
conditions. The wind flow around buildings is affected greatly by terrain roughness [
21
,
25
].
The main types of terrains corresponding to three aerodynamic roughness parameters are
(z
0
) as follows: city centre terrain (z
0
> 0.7), suburban terrain (z
0
= 0.25–0.3), and open field
terrain (z
0
= 0.01–0.03). Based on terrain roughness, the atmospheric boundary layer (ABL)
profile, which has the shape of a power law curve, is displaced at a distance d from the
ground. In open fields, accessible wind can be reached easily while in cities and areas of
urban characteristics, the accessible wind is located at higher altitudes and, in many cases,
does not reach the same wind speed as in open fields (Figure 2) [6].

Atmosphere 2024,15, 1049 4 of 27
Atmosphere 2024, 15, x FOR PEER REVIEW 4 of 28
ground. In open fields, accessible wind can be reached easily while in cities and areas of
urban characteristics, the accessible wind is located at higher altitudes and, in many cases,
does not reach the same wind speed as in open fields (Figure 2) [6].
Figure 2. ABL profile in different terrains. Different terrains have different aerodynamic roughness
parameters. d is the displacement of the ABL profile in urban areas and z0 is the roughness length
[6].
As a first step, macroscale wind conditions need to be assessed, and if the conditions
are suitable for urban wind power generation, a more detailed full assessment of the wind
resources at the proposed location should be carried out. This assessment will help in de-
ciding the feasibility of installing a wind turbine at the proposed urban location. For large-
scale wind power generation, wind farm locations are chosen very carefully to reach the
maximum wind resources, while in the case of small-scale wind power generation within
the built environment, where many variables affect local urban wind flow, a detailed as-
sessment is needed. It should be noted that relying solely on wind turbines for power
supply in urban areas, is not a feasible approach. Urban wind turbines should be consid-
ered as a complementary source of renewable energy [15,26].
3. Wind Assessment Tools for the Built Environment
It is evident that the most common research tools used to assess urban wind flow are
as follows [27–30]:
• In situ measurements;
• Wind tunnel tests;
• Computational fluid dynamics simulations (CFD).
Choosing between any of the above-mentioned tools for assessing a specific wind
flow scenario depends on their pros and cons. Good design decisions about the type of
integration of wind turbines in the vicinity of buildings would be reached by proper im-
plementation of these tools to study the local urban wind flow [31–33]. The annual mean
wind speed is the main factor affecting the energy yield of the wind turbine; the higher
the wind, the more power generated by the wind turbine. Accordingly, it is very important
to accurately understand how the wind flows within the proposed location as well as pre-
dict or identify the wind speed at the proposed installation location. This plays an im-
portant role in determining the feasibility of using a wind turbine in an urban seing
[34,35].
Although the previously mentioned tools are the most common in studying urban
wind flows, it should be noted that Geographic Information System (GIS) applications
play an important role as a start point for providing essential data on wind regimes and
urban fabric at a specific location [36]. Thus, coupling the capabilities of wind assessment
tools with GIS applications provides reliable data on wind flow regimes in specific loca-
tions. This approach reflects positively on a beer understanding of urban wind flow
Figure 2. ABL profile in different terrains. Different terrains have different aerodynamic roughness
parameters. d is the displacement of the ABL profile in urban areas and z
0
is the roughness length [
6
].
As a first step, macroscale wind conditions need to be assessed, and if the conditions
are suitable for urban wind power generation, a more detailed full assessment of the wind
resources at the proposed location should be carried out. This assessment will help in
deciding the feasibility of installing a wind turbine at the proposed urban location. For
large-scale wind power generation, wind farm locations are chosen very carefully to reach
the maximum wind resources, while in the case of small-scale wind power generation
within the built environment, where many variables affect local urban wind flow, a detailed
assessment is needed. It should be noted that relying solely on wind turbines for power
supply in urban areas, is not a feasible approach. Urban wind turbines should be considered
as a complementary source of renewable energy [15,26].
3. Wind Assessment Tools for the Built Environment
It is evident that the most common research tools used to assess urban wind flow are
as follows [27–30]:
•In situ measurements;
•Wind tunnel tests;
•Computational fluid dynamics simulations (CFD).
Choosing between any of the above-mentioned tools for assessing a specific wind
flow scenario depends on their pros and cons. Good design decisions about the type
of integration of wind turbines in the vicinity of buildings would be reached by proper
implementation of these tools to study the local urban wind flow [
31
–
33
]. The annual mean
wind speed is the main factor affecting the energy yield of the wind turbine; the higher the
wind, the more power generated by the wind turbine. Accordingly, it is very important to
accurately understand how the wind flows within the proposed location as well as predict
or identify the wind speed at the proposed installation location. This plays an important
role in determining the feasibility of using a wind turbine in an urban setting [34,35].
Although the previously mentioned tools are the most common in studying urban
wind flows, it should be noted that Geographic Information System (GIS) applications play
an important role as a start point for providing essential data on wind regimes and urban
fabric at a specific location [
36
]. Thus, coupling the capabilities of wind assessment tools
with GIS applications provides reliable data on wind flow regimes in specific locations.
This approach reflects positively on a better understanding of urban wind flow resulting in
making informed decisions on wind turbine integration within the built environment [
37
].
One of the main factors affecting the accuracy of wind tunnel tests and CFD sim-
ulations is the accuracy of modelling the atmospheric boundary layer (ABL) profile as
it is evident that GIS mapping provides accurate data in this regard, which gives more
confidence in results from CFD simulations and wind tunnel tests [
9
]. The application of
coupling GIS and urban wind assessment tools is not only useful for urban wind energy

Atmosphere 2024,15, 1049 5 of 27
applications, but it also covers other forms of renewable energy systems and urban thermal
comfort investigation [38].
For urban wind turbine installations, GIS applications provide maps resulting from
meteorological and morphological data of urban areas with a focus on wind speed assess-
ment since it is the main factor affecting the energy yield of wind turbines. GIS applications
coupled with CFD simulations provide estimates of wind speeds and potential zones for
wind turbine installations. However, the data should be carefully processed since these are
broad-scale estimates, and finer-scale data are still not readily available. This uncertainty is
also attributed to the lack of urban data related to vegetation, building heights, temporary
structures, and changes in urban morphology. This could be overcome by newly emerging
artificial intelligence (AI) technologies capable of making predictions covering missing
data [39].
Accordingly, it is envisioned that AI applications will be filling the gap between
coupling GIS and urban wind assessment tools. This approach should be supported by
verification and validation studies comparing the results of one tool with verified data
produced by another tool, leading to more confidence in the produced data [40].
3.1. In Situ Measurements
The device mostly used for full-scale measurements of wind resources is the anemome-
ter. This is the most accurate tool among all tools, especially in the case of roof-mounted
wind turbines or integrating wind turbines within completely developed urban areas. The
main flow variables affecting the feasibility of the integrated wind turbines are wind veloc-
ity, wind direction, and turbulent kinetic energy/turbulence intensity. The anemometer
is capable of collecting data about these variables. The most used type of anemometer in
urban applications is the cup anemometer. Wind direction is determined by fitting the cup
anemometer with a vane, which can correlate the wind velocity with the wind direction
(Figure 3) [6,41–44].
Atmosphere 2024, 15, x FOR PEER REVIEW 5 of 28
resulting in making informed decisions on wind turbine integration within the built envi-
ronment [37].
One of the main factors affecting the accuracy of wind tunnel tests and CFD simula-
tions is the accuracy of modelling the atmospheric boundary layer (ABL) profile as it is
evident that GIS mapping provides accurate data in this regard, which gives more confi-
dence in results from CFD simulations and wind tunnel tests [9]. The application of cou-
pling GIS and urban wind assessment tools is not only useful for urban wind energy ap-
plications, but it also covers other forms of renewable energy systems and urban thermal
comfort investigation [38].
For urban wind turbine installations, GIS applications provide maps resulting from
meteorological and morphological data of urban areas with a focus on wind speed assess-
ment since it is the main factor affecting the energy yield of wind turbines. GIS applica-
tions coupled with CFD simulations provide estimates of wind speeds and potential zones
for wind turbine installations. However, the data should be carefully processed since these
are broad-scale estimates, and finer-scale data are still not readily available. This uncer-
tainty is also aributed to the lack of urban data related to vegetation, building heights,
temporary structures, and changes in urban morphology. This could be overcome by
newly emerging artificial intelligence (AI) technologies capable of making predictions
covering missing data [39].
Accordingly, it is envisioned that AI applications will be filling the gap between cou-
pling GIS and urban wind assessment tools. This approach should be supported by veri-
fication and validation studies comparing the results of one tool with verified data pro-
duced by another tool, leading to more confidence in the produced data [40].
3.1. In Situ Measurements
The device mostly used for full-scale measurements of wind resources is the ane-
mometer. This is the most accurate tool among all tools, especially in the case of roof-
mounted wind turbines or integrating wind turbines within completely developed urban
areas. The main flow variables affecting the feasibility of the integrated wind turbines are
wind velocity, wind direction, and turbulent kinetic energy/turbulence intensity. The an-
emometer is capable of collecting data about these variables. The most used type of ane-
mometer in urban applications is the cup anemometer. Wind direction is determined by
fiing the cup anemometer with a vane, which can correlate the wind velocity with the
wind direction (Figure 3) [6,41,42,43,44].
Figure 3. Cup anemometer with a weathervane on the left and a sonic anemometer on the right
(Source: hps://skyview-systems.co.uk/products, accessed: 10 August 2024).
It should be noted that the power available in the wind is determined by air density,
which is affected by other variables, such as humidity, temperature, and barometric pres-
sure [45]. Other important data include wind power density at various heights and wind
Figure 3. Cup anemometer with a weathervane on the left and a sonic anemometer on the right
(Source: https://skyview-systems.co.uk/products, accessed: 10 August 2024).
It should be noted that the power available in the wind is determined by air den-
sity, which is affected by other variables, such as humidity, temperature, and barometric
pressure [
45
]. Other important data include wind power density at various heights and
wind shear coefficient, which could be collected by adding a microwave radiometer and a
Doppler lidar [
46
]. Turbulent kinetic energy, which is a main feature of wind flow in urban
areas, is another very important factor, which the cub anemometer cannot measure. A good
device that is able to capture wind speed as well as turbulence is the sonic anemometer.
Moreover, they are capable of capturing wind direction and recording the vertical wind
component. Since they do not have any moving parts, they require less maintenance. The
downsides though are the requirements for large data storage capacity and relatively high
cost compared with other in situ measurement devices [47].

Atmosphere 2024,15, 1049 6 of 27
When installing urban wind power generation devices, the data collected should be
very accurate and specific to the location where the wind turbine will be installed. Thus,
the measurement tool should be mounted in the exact location where the wind turbine is
expected to be installed. While doing this, the prevailing wind direction should be studied
carefully and areas of expected high levels of turbulence should be avoided as well as
nearby obstacles [
34
,
35
]. The collected data should cover a period of at least one year with
10 min intervals between the readings. After that, the collected data are normalised against
the data collected over a period of 30 years. It could be argued that the long data collection
period is one of the downsides of this tool. In addition to the lengthy process, it is also
costly due to the involvement of manpower to carry out maintenance and monitoring of
the devices [6,48].
Taking in situ measurements is not an error-free process [
49
]; errors in this tool could
reach 20%, especially in urban areas at the pedestrian level [
50
]. These errors stem from
the over-acceleration of the cup anemometer, which is more responsive to an increase in
wind speed than to a decrease in the wind speed of the same value. Thus, the recorded
data are expected to be higher than the actual wind speed. The opposite occurs with
cup anemometers fitted with a vane since the device does not align immediately with
the changing wind direction. These devices miss recording some of the dynamic features
of urban wind due to the slow response time of the device to any change in the wind
direction [51–53].
Erecting the mast to mount the anemometer requires planning permission for a very
long period, and the mast itself is an extra cost. These are expensive factors added to the
already deemed expensive tool for in situ measurements. The cost should be investigated
in the context of the size and energy yield of the proposed wind turbine as this could dis-
courage developers from using in situ measurements as the tool becomes no longer feasible
for assessing wind flow at the proposed location. Accordingly, other wind assessment tools
could be considered, such as wind tunnel tests and computational fluid dynamics (CFD)
simulations. Both have proven to be relatively inexpensive and produce reliable data about
urban wind flow if used according to the validated best practice guidelines [45,54].
Of all the advantages of in situ measurements, it is the fact that they produce accurate
data at higher levels above the pedestrian street level and they overcome scale mismatch
due to Reynolds number (Reynolds number (R
e
) is a dimensionless number that gives a
measure of the ratio of inertial forces to viscous forces and, consequently, quantifies the
relative importance of these two types of forces for given flow conditions. R
e
=
ρ
vl/
µ
Where
ρ
is the density of the fluid, v is the velocity of the fluid, l is a characteristic linear dimension
and
µ
is the dynamic viscosity), wind shear, and turbulence intensities, or from blockage
effects. As for the disadvantages, the cost and the time it takes are the obvious ones. The
data are also embedded with errors due to the difficulty of controlling the approach flow
conditions. Although in situ measurements are used for the calibration of wind tunnels, it
is worth mentioning that the full-scale measurement errors might be, in some cases, larger
than the errors in wind tunnel test results. It has been recorded that errors could reach up
to 15% in full-scale measurements [55–57].
3.2. Wind Tunnel Tests
A wind tunnel test is a good substitute for full-scale on-site measurements since there
is more control over the test environment contrary to the unpredictable conditions on
in situ measurements. In a wind tunnel test, wind pressure coefficients along buildings’
facades could be calculated with a high level of accuracy, even in highly dense modelled
urban areas [
57
]. Wind tunnel tests have many applications, some of which are calculating
wind loads on buildings and investigating pollutant dispersion in urban street canyons,
wind comfort at the pedestrian street level, and wind turbine integration in urban areas.
Moreover, wind turbine test results could be used for validating data produced by other
wind assessment tools [
21
,
58
–
63
]. It could be argued that using wind tunnel tests at the

Atmosphere 2024,15, 1049 7 of 27
early stages of the design gives more flexibility for investigating design alternatives to
reach the optimum design solution [21].
Wind tunnel tests were initially developed for industrial engineering and aeronautic
applications, but they are currently widely used for investigating wind flow around and
inside buildings with applications ranging from natural ventilation in buildings to wind
turbine integration. They are also referred to as scale modelling or physical experiments.
Using wind tunnels for testing the wind flow is well established in aerodynamics due to the
fact that for a long time, they have been tested, validated, and produced reliable results [
60
].
When wind tunnels were used for urban wind flow applications, they had to be developed
according to the complexity of the urban wind flow, which is different from the flow in
aeronautic engineering applications where the flow is considered still. This is due to the
turbulent nature of the wind flow in urban areas, which means that wind can flow from
a variety of directions. Also, the flow is around bluff bodies causing the flow to separate,
which is significantly different from the flow around the aerodynamically designed shapes
in aeronautical engineering [64].
The early wind tunnels that were used to test urban settings were inaccurate because
the applied wind profile at the inlet was uniform across the cross section of the wind tunnel.
This is different from the atmospheric boundary layer (ABL) profile, which sees the wind
velocity increase with height. This led to the development of wind tunnels able to mimic
the actual ABL profile by taking into consideration the variation in wind velocity with
height [
65
,
66
]. These wind tunnels are called atmospheric boundary layer wind tunnels
(BLWTs) and test results obtained from these BLWTs are relatively accurate. To improve
the accuracy of the results of the wind flow around a specific building, all surroundings
should also be accurately modelled and included inside the wind tunnel (Figure 4) [67].
Atmosphere 2024, 15, x FOR PEER REVIEW 7 of 28
3.2. Wind Tunnel Tests
A wind tunnel test is a good substitute for full-scale on-site measurements since there
is more control over the test environment contrary to the unpredictable conditions on in
situ measurements. In a wind tunnel test, wind pressure coefficients along buildings’ fa-
cades could be calculated with a high level of accuracy, even in highly dense modelled
urban areas [57]. Wind tunnel tests have many applications, some of which are calculating
wind loads on buildings and investigating pollutant dispersion in urban street canyons,
wind comfort at the pedestrian street level, and wind turbine integration in urban areas.
Moreover, wind turbine test results could be used for validating data produced by other
wind assessment tools [21,58–63]. It could be argued that using wind tunnel tests at the
early stages of the design gives more flexibility for investigating design alternatives to
reach the optimum design solution [21].
Wind tunnel tests were initially developed for industrial engineering and aeronautic
applications, but they are currently widely used for investigating wind flow around and
inside buildings with applications ranging from natural ventilation in buildings to wind
turbine integration. They are also referred to as scale modelling or physical experiments.
Using wind tunnels for testing the wind flow is well established in aerodynamics due to
the fact that for a long time, they have been tested, validated, and produced reliable results
[60]. When wind tunnels were used for urban wind flow applications, they had to be de-
veloped according to the complexity of the urban wind flow, which is different from the
flow in aeronautic engineering applications where the flow is considered still. This is due
to the turbulent nature of the wind flow in urban areas, which means that wind can flow
from a variety of directions. Also, the flow is around bluff bodies causing the flow to sep-
arate, which is significantly different from the flow around the aerodynamically designed
shapes in aeronautical engineering [64].
The early wind tunnels that were used to test urban seings were inaccurate because
the applied wind profile at the inlet was uniform across the cross section of the wind tun-
nel. This is different from the atmospheric boundary layer (ABL) profile, which sees the
wind velocity increase with height. This led to the development of wind tunnels able to
mimic the actual ABL profile by taking into consideration the variation in wind velocity
with height [65,66]. These wind tunnels are called atmospheric boundary layer wind tun-
nels (BLWTs) and test results obtained from these BLWTs are relatively accurate. To im-
prove the accuracy of the results of the wind flow around a specific building, all surround-
ings should also be accurately modelled and included inside the wind tunnel (Figure 4)
[67].
Figure 4. Atmospheric boundary layer wind tunnel (BLWT) [67].
The dimensions of the atmospheric BLWT are 15–30 m long (the working section)
and 2–5 m wide. Wind speed from 10–50 m/s could be tested in these wind tunnels using
air under atmospheric pressure. However, it should be noted that reaching the actual
wind speed is unnecessary as long as Reynolds numbers are maintained. Usually, the flow
Figure 4. Atmospheric boundary layer wind tunnel (BLWT) [67].
The dimensions of the atmospheric BLWT are 15–30 m long (the working section) and
2–5 m wide. Wind speed from 10–50 m/s could be tested in these wind tunnels using
air under atmospheric pressure. However, it should be noted that reaching the actual
wind speed is unnecessary as long as Reynolds numbers are maintained. Usually, the flow
variable values are normalised [
67
–
69
]. Wind speed is not the only flow variable that needs
to be simulated since the investigated flow is in urban areas where the flow is characterised
as being mostly turbulent. To simulate conditions similar to those in urban areas, a variety
of items, such as vortex generators, fences, and spires, are placed in the test section. This
results in simulating the turbulent nature of the urban wind flow [
21
]. Another important
factor for increased accuracy of the results from atmospheric BLWT is the scaling similarity,
which includes the following [29]:
•Geometric similarity where the ratios of linear dimensions are equal;
•Dynamic similarity where the ratios of forces are equal;
•Kinematic similarity where particle paths are geometrically similar.
Other requirements needed to ensure the accuracy of the wind tunnel test results
include the following:
•
Accuracy in modelling the mean wind speed, turbulent kinetic energy, and turbulent
dissipation rate vertically across the wind tunnel;

Atmosphere 2024,15, 1049 8 of 27
•
Modelling the important properties of atmospheric turbulence, in particular the rel-
evant length scale of the longitudinal turbulence component, with the same scale
approximately the same scale as that used to model buildings or structures;
•
Keeping the effect of the longitudinal pressure gradient across the wind tunnel minimal.
Adhering to the above-mentioned requirements would give confidence in wind tunnel
test results comparable with the previously discussed in situ measurements. However, due
to the difficulty in achieving these requirements accurately, they are considered among
the disadvantages of wind tunnel tests. Another disadvantage is the high expense of
using techniques such as laser-Doppler anemometry (LDA), particle image velocimetry
(PIV), and laser-induced fluorescence (LIF) due to the nature of the wind tunnel test as a
point measurement device [
65
]. Alternative less expensive techniques include hot-wire or
hot-film anemometers, Irwin probes, or sand erosion [66].
Wind tunnel tests fall short when it comes to comparing design alternatives. The
process becomes costly and time-consuming since the scaled models will need to be rebuilt
and the instruments will need to be fine-tuned requiring professional knowledge of how to
operate and fine-tune all the used devices. Also, due to the limitation in the size of the wind
tunnel, it is very difficult to simulate some flow variables, such as turbulence. Accordingly,
corrections need to be made to overcome the inevitable errors resulting from the lack of
accuracy in modelling all flow variables [70].
Since the professionals concerned with the built environment are mostly architects,
urban planners, and designers, it should be noted that they lack the needed training for
using wind tunnels. Thus, they cannot use the tool with confidence, and subsequently, they
lose the advantages of using the wind tunnel at the early stages of the design [
61
]. Due
to the familiarity of those professionals with using computer software, CFD simulation
packages might be a viable alternative to wind tunnel tests. But accurate wind tunnel tests
still remain a good source for producing accurate and reliable data, which could be used
for validating CFD simulation results [71].
The challenge that faces wind tunnel tests remains to be the possibility of reproducing
the details of the studied urban elements in the scaled physical model, especially when
studying low-rise buildings. To overcome this problem, a relatively larger physical model
is needed—a model with a scale of at least 1:50 [
72
]. Obviously, this might not be achievable
due to the limitations in the wind tunnel sizes. This also means that the largest turbulence
length scales in the wind tunnel are much smaller than the scaled full-scale equivalents. In
such situations, the modeller must decide whether to match the turbulence intensity, the
integral length scale, or neither, which would affect the accuracy of the results [
73
]. CFD
simulations overcome this problem since the model is virtually a full-scale model.
3.3. Computational Fluid Dynamics (CFD) Simulations
In computational fluid dynamics, the governing equations of fluid dynamics describ-
ing the flow of a Newtonian fluid (A Newtonian fluid is a fluid whose stress at each
point is linearly proportional to its strain rate at that point. For a Newtonian fluid, the
viscosity, by definition, depends only on temperature and pressure, not on the forces acting
upon it. If the viscosity does depend on the forces acting upon it then the fluid is said
to be non-Newtonian) are solved, which includes the turbulence effect. Similar to wind
tunnel tests, CFD simulation was initially developed for aerospace and aeronautical appli-
cations [
74
–
76
]. Recently, CFD has been widely used in studying wind flow around and
inside buildings, and the results have been verified and validated and proven to be of the
high level of accuracy [
77
]. Verification and validation studies are very important for in-
creased confidence in CFD simulation results. Thus, it is a viable alternative to wind tunnel
tests [
78
,
79
]. One of the main advantages of CFD simulations that makes them suitable
for urban wind assessment applications is their ability to produce detailed visualisations
and point measurements of different flow variables around and inside the studied urban
configurations [58,71].

Atmosphere 2024,15, 1049 9 of 27
Initially, CFD urban-related applications were focused on studying wind flow for
the purpose of natural ventilation in buildings [
80
], but recently, the applications have
covered a wide range of wind flow scenarios around buildings. Other applications include,
but not limited to, microscale atmospheric environment around the human body and air
flow around and inside buildings with data covering air temperature, velocity, and flow
rate [81–83].
When comparing CFD to other tools for urban wind assessment, it is relatively more
economical than wind tunnel tests and in situ measurements. They are widely used in
studying design alternatives for providing high-rise buildings with natural ventilation
and enhancing the building form for wind power generation purposes. A good example
is the 30 St Mary Axe building by Fosters and Partners, which was previously known
as Swiss Re and informally called the Gherkin, where CFD simulations were used for
understanding the wind flow at the pedestrian level of the building for the purpose of
reducing the turbulence and providing a more comfortable pedestrian level wind flow.
This resulted in the tapering form of the lower part of the building we currently see. The
tool was also used to improve the atrium’s natural ventilation across the full height of the
building [84,85].
Since the 1970s when CFD emerged, researchers [
83
,
86
–
94
] have used, studied, and
developed the tools, and agreed on the following points as the advantages of CFD simulations:
•
CFD simulations are cost-effective when compared with other wind assessment tools,
such as in situ measurements and wind tunnel tests. They are even becoming more
economical with the increase in computational power.
•
When comparing CFD simulations with other tools, CFD simulations can produce a
wealth of quantitative and qualitative data across the study domain and not just point
measurements.
•
CFD simulations are not restricted by the similarity constraints previously discussed.
•
CFD simulations are very effective in investigating design alternatives and studying
alterations in designs to reach the optimum design solution. This advantage means
that CFD speeds up the design process and the design decisions.
•
CFD simulations are considered full-scale simulations, which is a big advantage when
studying large urban areas or high-rise buildings. The alternative would be wind
tunnel tests, which will be limited by the size of the wind tunnel.
•
CFD simulations communicate the results easily in the form of meaningful visualisa-
tions, which could be understood by the majority of people.
However, CFD simulations also have their disadvantages, which are well documented
in the literature [
95
–
102
] and need to be carefully considered to mitigate them and make
the most out of the tool. The disadvantages are as follows:
•
With low computational power, CFD simulations are not very accurate in simulating
urban wind flow in high-density built environments. The turbulent nature of the urban
wind flow in high-density built environment requires significant computational power.
•
There is a high risk of inexperienced users using the tool without professional knowl-
edge of fluid dynamics, which might result in the wrong interpretation of the produced
data. The set of skills needed to confidently use CFD simulations is not common among
planners, architects, and designers.
•
Commercial CFD codes require a wide range of variables to be set up prior to the
simulation, such as boundary conditions, turbulence model, grid type, discretisation
schemes, adjustments in the ABL profile, etc. If any of these variables is wrong,
it will significantly affect the simulation output. Thus, these variables should be
specified carefully.
•
Due to the complexity of the variables that need to be specified before the simulation,
best practice guidelines should be followed as the first step to ensure reliable results,
and then, validation against other wind assessment tools should be carried out. In
some cases, the validation data are not available; in this case, the results should be
treated carefully and interpreted with caution.

Atmosphere 2024,15, 1049 10 of 27
Considering the pros and cons of CFD simulations in comparison to the pros and cons
of in situ measurements and wind tunnel tests, it can be argued that if the users of CFD
simulations are well trained in fluid dynamics and have proper experience in using the
tool, CFD simulations would be the most suitable tool for studying the wind flow within
the built environment. The ongoing increase in computational power is another factor
supporting this argument as CFD simulations have become more accessible for a wide
range of people without the need for sophisticated equipment. Thus, CFD is being adopted
by many stakeholders concerned with the design and construction of the built environment
with wind in mind [103].
3.4. Relevance of Different Tools for Assessing Urban Wind Flow
In terms of flexibility in comparing alternative design solutions, wind tunnel tests and
CFD simulations are more flexible than in situ measurements. However, when comparing
CFD simulations with wind tunnel tests, CFD simulations provide a good alternative to
wind tunnel tests since they are faster in giving reliable results, cheaper than wind tunnel
tests, and very powerful when it comes to quantitative and qualitative visualisation of
the produced data. There is a good agreement between the two tools in areas around the
buildings, but the discrepancies appear at the ground level. The discrepancies stem from
near-wall region treatment by the mesh and roughness specifications. The main reason
behind this is that in the near-wall area, the flow variables change significantly; also, this
area is characterised by vigorous momentum and other scalar transports. Accordingly, the
flow in these areas need to be represented accurately, using the relevant turbulence model
to yield accurate results [104–106].
Generally, there is an agreement in flow trends between wind tunnel test results and
CFD simulation results, which suggests that the wind environment needs to be simulated
more accurately using both tools. Any difference in results would have implications
in terms of making different design decisions. Accordingly, more work is needed to
identify the problems in both CFD turbulence simulation and wind tunnel test point
measurements [
69
]. In a study comparing CFD simulation results and wind tunnel test
results, discrepancies ranged from 2 to 30% according to the wind direction. However, the
results would still be useful due to the agreement in the flow pattern distribution between
the two tools [107].
In terms of the expenses associated with using the three tools, they are all deemed
relatively expensive to investigate the urban wind flow. As discussed earlier, in situ
measurements will yield the most accurate results at the highest cost over the longest
period, which leads to the need for more developments in CFD simulation and wind
tunnel tests to rely more on them and avoid the expensive and time-consuming in situ
measurements. Considering the reviewed literature, the following Table 1summarises
the comparison between the three tools in terms of accuracy, visualisation capabilities,
preference of usage for existing and future planned developments, cost, the required time
for assessment, and availability to users.
Table 1. Comparison between in situ measurements, wind tunnel tests, and CFD simulations.
Tools Arranged in Descending Order
High accuracy In Situ Measurements—Wind Tunnel—CFD
High visualisation CFD—Wind Tunnel—In Situ Measurements
Preference of usage for existing developments In Situ Measurements—CFD—Wind Tunnel
Preference of usage for future developments CFD—Wind Tunnel—In Situ Measurements
Lowest cost CFD—Wind Tunnel—In Situ Measurements
Less time-consuming CFD—Wind Tunnel—In Situ Measurements
Availability to users CFD—Wind Tunnel—In Situ Measurements

Atmosphere 2024,15, 1049 11 of 27
As seen in the table, of all the investigated tools for studying the wind flow in urban
areas, CFD simulation would be the preferred choice for this type of application. The main
reasons are that CFD is a powerful tool when it comes to comparing design alternatives, it
can also represent the produced data visually more easily, and it is relatively easy to use.
However, the users of CFD commercial codes should be well trained in using the codes and
have relevant background knowledge to be able to interpret the data confidently. Moreover,
the first step in CFD simulation should be following validated best practice guidelines in
specifying the simulation parameters; then, validation studies should be carried out before
the actual simulation for the flow problem takes place [108].
4. Potential of CFD Simulations as an Urban Wind Assessment Tool
As confirmed by the developers of commercial CFD codes, CFD codes are user-friendly,
and with basic knowledge of IT, they are accessible to a wide range of people. Indeed, this is
one of the main advantages of CFD simulation software; however, this also means that the
software is accessible to users who might not necessarily have the needed understanding of
fluid dynamics or the training to use the codes. It is evident that with the ease of using the
CFD applications, more architects, planners, and designers are using them to investigate
the thermal performance of buildings and air flow inside and outside buildings although
they might not have the necessary training or background to use the tool.
Thus, adequate training in using the codes and understanding of at least the basics
of fluid dynamics is needed to obtain meaningful results. To prove the importance of the
experience needed to use CFD codes, a study was conducted among a group of mechanical
engineering graduates who were presented with a flow problem and were asked to use
a CFD code to solve the problem. In their first attempt, none of the participants reached
the correct answer [109]. This study demonstrates that the experience of the user with the
CFD code plays an important role in the accuracy and reliability of the CFD simulation
results [
70
]. Accordingly, not only a background in fluid mechanics engineering is needed
for running accurate CFD simulations, but also experience in CFD simulations is needed.
In the following section, the requirements for successful CFD simulations will be re-
viewed, leading to the most common recommendations for best practice guidelines for using
CFD simulations in studying the wind flow in urban areas for wind turbine integration.
4.1. CFD Simulation of the Urban Wind Flow
When studying urban wind flow, the area of interest extends from the ground level
up to 200 m high [
110
]. The fluid/air properties in this boundary layer are characterised
by very low variations. The atmospheric boundary layer, the urban boundary layer, and
the urban canopy layer need to be modelled accurately to yield reliable results [
111
]. The
Navier–Stokes equations are the governing equations for continuity, momentum, and
energy, which represent the conservation laws of physics, within a turbulent flow, and
solving these equations provides a description of the flow. They are named after the
18th-century scientists Claude Navier and George Stokes who independently obtained the
equations in the first half of the 19th century [
35
,
68
,
76
]. It should be noted that this review
focuses on air flow problems and not heat transfer—thus, the concentration on momentum
and continuity equations.
The law of conservation of mass governs the continuity equation ensuring that the
change of mass in a control volume is equal to the mass that enters through its faces
minus the total mass leaving its faces. Newton’s Second Law of Motion (conservation
of momentum) governs the momentum equation. It states that the rate of change of
momentum of the fluid particles is equal to the total force due to surface stresses and
body forces acting in an aligned direction of a chosen coordinate axis. Navier and Stokes
combined these principles and expressed them in a set of partial differential equations.
Assuming that the flow is incompressible, and the flow nature is three-dimensional, the 3D
form of the equations would be:

Atmosphere 2024,15, 1049 12 of 27
Continuity equation:
∂u
∂x+∂v
∂y+∂w
∂z=0 (1)
X-component of the momentum equation:
ρDu
Dt =−∂p
∂x+∂τxx
∂x+∂τyx
∂y+∂τzx
∂z+ρfx(2)
Y-component of the momentum equation:
ρDv
Dt =−∂p
∂y+∂τxy
∂x+∂τyy
∂y+∂τzy
∂z+ρfy(3)
Z-component of the momentum equation:
ρDw
Dt =−∂p
∂z+∂τxz
∂x+∂τyz
∂y+∂τzz
∂z+ρfz(4)
where u,v, and ware the x,y, and zvelocity components, respectively; pis the pressure; ρ
is the fluid density;
τ
is the shear stress; and f
x
,f
y
, and f
z
are the components of the body
force per unit mass acting on the fluid [112–114].
Computationally, a mesh, which could be regular or irregular, is needed with node
points where the flow variables are calculated. The mesh is the division of the domain
into a finite number of points or nodes where the flow variables are calculated at specific
intervals representing the passage of time. The flow problem is represented in the form of
discrete numerical data, which is known as discretisation. Discretisation is divided into
three main parts to solve the flow of the fluid—these are equation discretisation, spatial
discretisation, and temporal discretisation [76].
In equation discretisation, the governing equations are translated into numerical ana-
logue solvable by the computer. The equations could then be solved by the finite-difference
method (FDM), the finite-element method (FEM), or the finite-volume method (FVM). Of
the three, it is FDM that is known for its ease in obtaining higher-order accuracy discretisa-
tion and, also, for its simplicity. FDM uses a structured cartesian mesh, so it is more used
with simple geometries. For complex geometries, FEM could be used since it implements
unstructured mesh. This unstructured mesh comes with an expensive computational cost
compared with FDM. FVM has the advantage of implementing unstructured and structured
meshes; it is known for its efficiency and can be easily programmed [
76
]. FVM is the most
used due to its simplicity in depicting the conservation laws of mass, momentum, and
energy in a finite volume of a fluid [76,102].
The equations could be solved by several methods using CFD. However, the numerical
method used is recommended to be second-order accurate [
115
]. In most cases, second-
order discretisation schemes obtain a more accurate solution and are preferred for complex
flows such as the urban wind flow [
116
–
119
]. A first-order scheme can be used for initial
iterations but is not recommended for the final solution. Higher-order schemes require
high computational power but are more accurate and efficient. It has been demonstrated
that first-order discretisation schemes give inaccurate results and some journals, such as
the Journal of Fluid Engineering, do not publish research using first-order discretisation
schemes [
120
]. However, it should be noted the that the First International Workshop
on High-Order CFD Methods concluded that higher accuracy is obtained when using
high-order methods with smooth geometries, while it was not the case for non-smooth
geometries [121].
To solve the equations, the computer goes through an iterative process of trying to
solve the equations until the solution converges. Another alternative is that the iterative
process repeats until a predefined residual value is reached. The residual value is 0.001
in most commercial CFD codes. For more accurate results, the residual value is set to

Atmosphere 2024,15, 1049 13 of 27
the lowest possible value. The 0.001 value is considered a relatively high value for a
solution to converge, and a residual value that is at least 4 or 5 orders of the magnitude is
recommended [
115
]. Accordingly, a residual value in the range of 10
−4
to 10
−6
residual
value is targeted for the solution to converge.
Spatial discretisation is the second category of discretisation. In spatial discretisation,
the computational domain is divided by the mesh into smaller subdomains where the flow
could be described by its flow variables at the nodes of the mesh at certain time intervals.
The main mesh types are the unstructured and structured meshes. The latter is usually
used with simple rectilinear geometries while the unstructured mesh is usually used with
complex forms. The reason behind using the unstructured mesh for complex forms is that
it is made of tetrahedral units that are capable of forming any shape/volume. Obviously,
the unstructured grid needs higher computational power. A subcategory of the structured
mesh is the multiblock mesh where the domain is divided into zones having different sizes
of structured meshes. This is a good way of reducing the required computational power to
solve large flow problems. It also gives relatively good control over the mesh when the
geometry is complicated [75].
Time/temporal discretisation is the third discretisation category related to unsteady
simulations. Here, the time is divided into discrete time steps in the continuous flow.
Thus, when compared with the steady-state analysis, there is an additional variable in
the governing equations, which is the time (t). In this category, the partial differential
equations are in time. So, at a given time, the unknowns are a function of the variables
in the previous time step. In this unsteady state, there is an additional step between the
equation and spatial discretisation requiring a longer time to solve the equations compared
with the steady-state case [
112
]. There are also many other simulation parameters that
need to be specified by the user to accurately set up the flow problem. Due to a lack of
experience, the user might mistakenly specify the conditions for the flow problem, which
will yield inaccurate results. Accordingly, these parameters need to be identified and
accurately specified at the early stages to eradicate any uncertainties and minimize errors
in the simulation results.
4.2. CFD Modelling Parameters
CFD codes are embedded with uncertainties, even for experienced users. For con-
sistent simulation results, there are many computational and physical parameters that
need to be accurately specified. Accordingly, the literature extensively covers best practice
guidelines and quality control in CFD simulation [
116
,
122
–
127
]. The five main categories,
which are addressed across the literature on CFD simulation best practice guidelines, are
defining the physical model, the geometry of the flow problem, the computational domain
dimensions, the computational domain boundary conditions, and the computational mesh.
4.2.1. Defining the Physical Model
Defining the physical model is concerned with the physics of the flow by specifying
the most suitable turbulence model for solving the basic equations defining the physics of
the flow problem. In theory, approximately solving the Navier–Stokes equations means
that the flow problem is solved. This is the definition of the direct numerical simulation
(DNS) method where a very fine mesh is constructed to capture all flow features, including
large and small eddies with any variations happening at each time step. This method
is impractical in solving turbulent flow problems due to the enormous computational
power needed to handle the number of cells and time steps. However, if the computational
power is available to solve the equations in an acceptable period of time, DNS could be
used [128,129].
For the equations to be numerically solved, they need to be simplified by filtering out a
variety of scales of the turbulent flow. This filtering process means that the basic equations
are averaged, which results in more unknowns in the form of sub-grid stresses or turbulence
stresses. A turbulence model is used to solve these additional unknowns. The turbulence

Atmosphere 2024,15, 1049 14 of 27
models have some simplified assumptions, which help in solving the equations [
129
].
These turbulence models are either time-averaged or space-averaged models, and they are
effective in solving turbulent flows. A lot of turbulence models are available for solving
turbulent flow problems. The time-averaged method is the most common method, and its
models are referred to as Reynolds-averaged Navier–Stokes (RANS) models. As for the
space-averaged models, they directly solve the equations governing the large eddies, while
the small eddies are modelled using some assumptions. The large eddy simulation (LES)
model is a space-filtered model [
123
]. LES requires very high computational power since it
also needs to resolve the flow fields at each time step. The computational power needed for
LES remains less than that for DNS, which ignores solving the small eddies not affecting
the flow.
A filter function is used in LES to differentiate between small-scale and large-scale
eddies. The length scale used as the filter function is the characteristic filter width of
the simulation. Eddies larger in length than that length scale are simulated (directly
resolved), while eddies smaller than the length scale are modelled (approximated). The
main turbulence properties that are not accurately solved by RANS models, such as the von
Karman vortex shedding in the wake of an obstacle, could be accurately simulated using
this method. This also applies to the recirculation downstream of the windward edges of
the studied building and the transient behaviour of separation. But of course, this accuracy
comes at a very high computational cost [
93
,
128
]. Due to the high computational cost, it is
still impractical to use LES although its main advantage is its accuracy in reproducing the
average and fluctuating data. But, if sufficient computational power is accessible, then LES
could be used to produce data that could be used as a benchmark for comparing the results
of other less complex turbulence models, such as the RANS turbulence models [123,130].
Turbulence models should be chosen with care as they have a significant effect on how
the flow problem is solved. Each turbulence model has its own advantages, disadvantages,
and suitability for solving certain types of flow problems. As a first step, it should be
decided whether the flow is steady or unsteady. Since the focus here is on urban wind
applications, the flow within the atmospheric boundary layer is expected to be mostly
turbulent, thus, unsteady flow assumptions are expected. This will require averaging over
small time steps, which is called unsteady RANS (URANS) [128].
However, it should be noted that URANS will not be able to capture the internal
fluctuations induced in the flow. For capturing these fluctuations, LES and detached eddy
simulation (DES) are recommended. DES implements both RANS and LES; the RANS
model is used to solve the flow in the region near the wall while the LES is used for
solving the unsteadiness in the wake region of the flow. When comparing this method
with using LES across the whole domain, this method significantly consumes less time
than LES. But when comparing DES, RANS, and LES, DES will come in between RANS
and LES as RANS will still need less time than DES to solve the flow problem. DES also
requires experimental data for accurately specifying the inflow boundary conditions; these
data are not readily available in practice, and this is why DES is not widely used in wind
engineering problems [124,131,132].
Accordingly, it is expected that for the foreseeable future, it is the RANS models that
will continue to be used to solve the turbulent wind flow within the built environment in
the near-wall region. As seen in Figure 5, each time-dependent variable can be simplified
into a fluctuating complement and average value [114].
As an example, the velocity of a turbulent flow at a specific point in time equals the
mean velocity of the flow plus the fluctuating velocity component:
U=U+u′(5)
where
U
is the mean velocity taken over a sufficiently long period of time and u
′
is its
fluctuating component.

Atmosphere 2024,15, 1049 15 of 27
Atmosphere 2024, 15, x FOR PEER REVIEW 15 of 28
Turbulence models should be chosen with care as they have a significant effect on
how the flow problem is solved. Each turbulence model has its own advantages, disad-
vantages, and suitability for solving certain types of flow problems. As a first step, it
should be decided whether the flow is steady or unsteady. Since the focus here is on urban
wind applications, the flow within the atmospheric boundary layer is expected to be
mostly turbulent, thus, unsteady flow assumptions are expected. This will require aver-
aging over small time steps, which is called unsteady RANS (URANS) [128].
However, it should be noted that URANS will not be able to capture the internal
fluctuations induced in the flow. For capturing these fluctuations, LES and detached eddy
simulation (DES) are recommended. DES implements both RANS and LES; the RANS
model is used to solve the flow in the region near the wall while the LES is used for solving
the unsteadiness in the wake region of the flow. When comparing this method with using
LES across the whole domain, this method significantly consumes less time than LES. But
when comparing DES, RANS, and LES, DES will come in between RANS and LES as
RANS will still need less time than DES to solve the flow problem. DES also requires ex-
perimental data for accurately specifying the inflow boundary conditions; these data are
not readily available in practice, and this is why DES is not widely used in wind engineer-
ing problems [124,131,132].
Accordingly, it is expected that for the foreseeable future, it is the RANS models that
will continue to be used to solve the turbulent wind flow within the built environment in
the near-wall region. As seen in Figure 5, each time-dependent variable can be simplified
into a fluctuating complement and average value [114].
Figure 5. Time averaging of turbulence using RANS models [114].
As an example, the velocity of a turbulent flow at a specific point in time equals the
mean velocity of the flow plus the fluctuating velocity component:
𝑈= 𝑈
+𝑢′ (5)
where Ū is the mean velocity taken over a sufficiently long period of time and u′ is its
fluctuating component.
Substituting equation (U = Ū + u′) into Navier–Stokes equations, the RANS equations
can be obtained: 𝜕𝑈
𝜕𝑡 + 𝑈𝜕𝑈
𝜕𝑥= −1
𝜌𝜕𝑃
𝜕𝑥+𝜇
𝜌𝜕
𝜕𝑥 󰇧𝜕𝑈
𝜕𝑥− 𝜌𝑢′𝑢′







󰇨 (6)
where Ui is the mean velocity, 𝑃 is the mean static pressure and 𝜌𝑢′𝑢′







are turbulent, or
Reynolds, stresses, which are the stresses contributed by turbulent fluctuations. The exist-
ence of the stress terms means there is no longer a closed set of equations, and turbulence
model assumptions are needed to estimate the unknowns to solve this closure problem.
This time averaging raises questions about the efficiency of RANS in solving turbulent
Figure 5. Time averaging of turbulence using RANS models [114].
Substituting equation (U=
U
+u
′
) into Navier–Stokes equations, the RANS equations
can be obtained:
∂Ui
∂t+Ui
∂Ui
∂xj
=−1
ρ
∂P
∂xi
+µ
ρ
∂
∂xj ∂Ui
∂xj
−ρu′iu′j!(6)
where U
i
is the mean velocity,
P
is the mean static pressure and
ρu′iu′j
are turbulent, or
Reynolds, stresses, which are the stresses contributed by turbulent fluctuations. The exis-
tence of the stress terms means there is no longer a closed set of equations, and turbulence
model assumptions are needed to estimate the unknowns to solve this closure problem.
This time averaging raises questions about the efficiency of RANS in solving turbulent
flow problems since its turbulence models basically describe a turbulent flow as a steady
flow [93,133].
However, it has been shown in many practical applications that RANS results can be
sufficiently accurate [
123
]. In addition, other superior methods, such as LES, still require
higher simulation complexity, which comes with a much larger computational power.
Accordingly, RANS models can still be used for representing wind flow inside and around
buildings. Of the RANS models, it is the k-
ε
turbulence model that is mostly used; it is a
two-equation model and is the most popular RANS turbulence model.
The most recent literature also shows that the k-
ω
turbulence models can yield reliable
and accurate results. The results show agreement with experimental data for reproducing
the atmospheric boundary layer (ABL) profile. The drawback of the k-
ω
turbulence model
is in the estimation of the turbulent kinetic energy (TKE) near the building’s walls. The k-
ω
with shear stress transport (k-
ω
SST) overcome the overestimation of the pressure in the
stagnation zone in the windward façade of the building [134–137].
Thus, the k-
ω
turbulence models also provide a good alternative to the k-
ε
turbulence
models for certain flow problems. For example, in simulating drone flight using CFD simu-
lation, the k-
ω
turbulence model was more accurate in predicting aerodynamic coefficients
and pressure distribution than the k-
ε
turbulence model [
138
]. However, it should be noted
that for urban wind turbine applications where the main concern is wind velocity, the k-
ε
turbulence models predict the velocity more accurately than the k-
ω
turbulence models. In
a study comparing different turbulence models to predict the flow around a bluff body, the
k-
ε
turbulence model compared more favourably than the k-
ω
turbulence model in com-
puting pressure distribution along the cube surface and the mean wind velocity, especially
in the leeward direction [139].
The same results were obtained in a study where the realizable k-
ε
turbulence model
gave the closest value quantitatively, while the k-
ω
turbulence model and the standard k-
ε
turbulence model over- and underpredicted the re-attachment lengths, respectively [
140
].
Since there should be a fine balance between the accuracy and the time needed for running

Atmosphere 2024,15, 1049 16 of 27
the simulations, the investigation recommended the realizable k-
ε
RANS model for reaching
this balance for urban wind flow problems. In that study, a variety of turbulence models
were tested and the realizable k-
ε
yielded consistent results without taking a significant
time to solve the flow [78].
4.2.2. Flow Problem Geometry
The built environment is geometrically very complicated, which hinders the ability
to include all the details of its geometry in the model since the CFD code will take a very
long time to solve the flow problem between its parts [
141
]. However, the area of interest
in the model should be reproduced with as much details as possible to the nearest 1 m of
detail. Buildings far away from the area of interest are not necessarily important in terms of
their geometry and can be included as blocks [
124
]. The computational domain dimensions
will be determined based on the details and dimensions of the buildings to be investigated.
The relationship between the building dimensions and the domain dimensions will be
discussed in the next section.
4.2.3. Dimensions of the Computational Domain
The computational domain is a truncation from the real case scenario representing the
area of interest. Accordingly, accurately specifying the boundary conditions of the domain
plays an important role in the conversion of the solution. Not only specifying the boundary
conditions plays an important role in the accuracy of the solution, but also specifying the
location or the distance between these boundaries is very important. The distance between
the boundaries and the studied area/building should also be specified accurately.
Assuming that the height of the building of interest is H, then the height of the domain
should extend five times the height of the building, which makes the total vertical height
of the domain six times the height of the highest building (5H) [
126
,
142
]. Having this
amount of clearance above the highest building prevents any unrealistic flow acceleration
above the building [
124
]. The distance between the two sides of the domain is calculated
so that the blockage ratio is a maximum of 3%, where the blockage is defined as the ratio
of the projected area of the building in the flow direction to the free cross section of the
computational domain. The recommended blockage ratio is 1.5%, which means that a
distance of 5H should be allowed between the inlet boundary and the windward facade as
well as between the side boundaries and the side facades of the building [
126
,
142
]. In the
leeward direction of the building, a distance of 15H between the back facade and the outlet
of the domain is recommended [
70
,
124
,
126
,
142
]. Domain dimensions in all directions for a
cubic building of specific length H are shown in Figure 6.
Atmosphere 2024, 15, x FOR PEER REVIEW 17 of 28
Assuming that the height of the building of interest is H, then the height of the do-
main should extend five times the height of the building, which makes the total vertical
height of the domain six times the height of the highest building (5H) [126,142]. Having
this amount of clearance above the highest building prevents any unrealistic flow acceler-
ation above the building [124]. The distance between the two sides of the domain is calcu-
lated so that the blockage ratio is a maximum of 3%, where the blockage is defined as the
ratio of the projected area of the building in the flow direction to the free cross section of
the computational domain. The recommended blockage ratio is 1.5%, which means that a
distance of 5H should be allowed between the inlet boundary and the windward facade
as well as between the side boundaries and the side facades of the building [126,142]. In
the leeward direction of the building, a distance of 15H between the back facade and the
outlet of the domain is recommended [70,124,126,142]. Domain dimensions in all direc-
tions for a cubic building of specific length H are shown in Figure 6.
Figure 6. Dimensions of a computational domain for studying a cubic building of height H.
4.2.4. Computational Domain Boundary Conditions
Since the domain is considered a cut through the surrounding of the flow problem,
this process means that the artificial boundaries need to have some physical properties
minimising the effect of the truncation from the surroundings [112]. Correctly specifying
these physical properties, or what is referred to as boundary conditions, is very important
for the convergence speed of the solution as well as the accuracy of the simulation. Ac-
cordingly, boundary conditions are assigned to the boundaries of the computational do-
main to simulate the physical quantities in the real flow problem [125]. The main five
boundary conditions that need to be assigned in a typical external urban flow problem are
the inflow boundary condition, outflow boundary condition, boom boundary condition,
top boundary condition and the side boundary conditions. The top and the side boundary
conditions are mostly treated in the same way.
An atmospheric boundary layer (ABL) is assigned to the inflow boundary condition
as an inlet. This ABL profile should be consistent for all flow variables across an empty
domain until it leaves the domain through the outlet plan. This is what is referred to as
the horizontal homogeneity of the ABL profile. The ABL takes the shape of a power expo-
nent or a logarithmic function that describes the changes in the mean wind speed as a
function of height due to the variation in the wind speed with height [95,143] (Figure 2).
A logarithmic velocity inlet profile is usually used at the inlet. The profile can be obtained
Figure 6. Dimensions of a computational domain for studying a cubic building of height H.

Atmosphere 2024,15, 1049 17 of 27
4.2.4. Computational Domain Boundary Conditions
Since the domain is considered a cut through the surrounding of the flow problem,
this process means that the artificial boundaries need to have some physical properties
minimising the effect of the truncation from the surroundings [
112
]. Correctly specifying
these physical properties, or what is referred to as boundary conditions, is very impor-
tant for the convergence speed of the solution as well as the accuracy of the simulation.
Accordingly, boundary conditions are assigned to the boundaries of the computational
domain to simulate the physical quantities in the real flow problem [
125
]. The main five
boundary conditions that need to be assigned in a typical external urban flow problem are
the inflow boundary condition, outflow boundary condition, bottom boundary condition,
top boundary condition and the side boundary conditions. The top and the side boundary
conditions are mostly treated in the same way.
An atmospheric boundary layer (ABL) is assigned to the inflow boundary condition
as an inlet. This ABL profile should be consistent for all flow variables across an empty
domain until it leaves the domain through the outlet plan. This is what is referred to as the
horizontal homogeneity of the ABL profile. The ABL takes the shape of a power exponent
or a logarithmic function that describes the changes in the mean wind speed as a function of
height due to the variation in the wind speed with height [
95
,
143
] (Figure 2). A logarithmic
velocity inlet profile is usually used at the inlet. The profile can be obtained from the
logarithmic profile corresponding to the upwind terrain through the roughness length (z
0
)
or from the profiles of the wind tunnel test results; the turbulence quantities at the inlet can
be obtained from the assumption of an equilibrium boundary layer, which means that the
production and dissipation rates of the turbulent kinetic energy are equal [124].
Using the logarithmic function to compute the velocity profile can be described using
the following equation [29]:
V(z)
V(10)=lnz
z0
ln10
z0(7)
where
V(z)
is the wind speed at operating height
z
(m/s),
z0
is the roughness length, and
V(10)
is the wind speed (m/s) at a reference height 10 m from the ground. It should be
noted that either the power exponent or a logarithmic function can be used to calculate
the mean wind velocity or speed at a given height if the mean wind velocity is known
at the reference height (z). Parameter
z0
represents the roughness length for the type of
terrain involved (Figure 2). Another way of obtaining an atmospheric boundary layer
(ABL) velocity profile is the empirical formulation known as the power law:
V(z)=VGz
zGα
(8)
where
V(z)
is the wind velocity (m/s) at a height z(m),
α
is the exponent dependent on
terrain conditions,
VG
is the wind velocity at the gradient height and
zG
is the gradient
height. The values in the equations depend on the conditions of the terrain being rural,
suburban, or urban (Table 2) [
70
]. The main advantage of the power law model is its
simplicity, and its accuracy is sufficient for most wind engineering applications [
21
]. On
the other hand, the power law models are purely empirical, lacking the support of proven
theory, and they are not good for representing the velocity profile close to the ground [
70
].
As mentioned earlier, the horizontal homogeneity of the ABL profile is an important
characteristic of the inlet profile. In other words, the flow variables (velocity, turbulent
kinetic energy, and turbulent dissipation rate) across the domain until it reaches the studied
building should not change [
95
,
110
,
144
] (Figure 7). The roughness of the bottom wall
boundary with the boundary condition of the top boundary helps achieve the horizontal
homogeneity of the ABL profile. Thus, tests of the horizontal homogeneity of the ABL
profile should be performed in an empty domain before starting to simulate the flow
problem. Once the horizontal homogeneity of the ABL profile is achieved, the boundary

Atmosphere 2024,15, 1049 18 of 27
conditions used in achieving the horizontal homogeneity of the ABL profile could be used
in the actual flow problem.
Table 2. Values of αcorresponding to zG.
Type of Terrain
z
G
, Gradient Height (m)
α
Open terrain with very few obstacles such as open grass
or farmland with few trees, hedgerows, and other barriers;
prairie, tundra shores, low islands of inland, lakes,
and deserts
300 0.16
Terrain uniformly covered with obstacles 10 to 15 m in
height; e.g., residential suburbs, small towns; woodland
and shrub, small fields with bushes, trees, and hedges
430 0.28
Terrain with large and irregular objects; e.g., centres of
large cities, very broken country with many windbreaks
of tall trees, etc.
560 0.40
Atmosphere 2024, 15, x FOR PEER REVIEW 19 of 28
A no-slip wall boundary condition is assigned to the boom boundary. The flow is
greatly affected by this boundary condition since the velocity at the wall surface reaches
zero with the maximum value of shear stress. The wall roughness also affects the accuracy
of the solution to a great extent—sand-roughened surfaces are implemented by most com-
mercial CFD codes with a corresponding roughness height (ks) for the roughness of the
wall [124]. If that is the case, the following requirements should be satisfied simultane-
ously [110]:
• High mesh resolution in the vertical direction close to the boom of the computa-
tional domain;
• Maintaining the horizontal homogeneity of the ABL profile upstream and down-
stream of the computational domain;
• The distance between the centre of the first cell away from the boom boundary (zp)
and the boom wall boundary to be greater than the roughness height (zp > ks);
• Roughness height equal to thirty times the roughness length (z0) (ks = 30 z0).
Different terrains have different roughness lengths. Table 3 shows the values of z0 for
six recognizable terrain types in the UK for the design of wind load on buildings and
structures. Although it is common sense to have zp > ks because it is not physically mean-
ingful to have mesh cells with centre points within the physical roughness height, this
would lead to a coarse mesh [110]. Thus, it is recommended to alleviate the requirement
zp > ks.
Figure 7. Inlet, approach, and incident flows in a computational domain with the indication of dif-
ferent parts of the domain [110].
Table 3. Roughness parameters for differ terrain categories [145].
Category z0 (m) Remark
0 0.003 Corresponding to large expanses of water, mudflats, snow-
covered farmland, and large flat areas of tarmac
1 0.01 Corresponding to flat grassland, parkland, or bare soil, without
hedges, and with very few isolated obstructions
2 0.03
Meteorological standard, basic terrain roughness corresponding
to typical UK farmland, nearly flat or gently undulating
countryside, fields with crops, fences or low boundary hedges
and few trees
3 0.1 Corresponding to farmland with frequent boundary hedges,
occasional small farm structures, houses, or trees
Figure 7. Inlet, approach, and incident flows in a computational domain with the indication of
different parts of the domain [110].
A no-slip wall boundary condition is assigned to the bottom boundary. The flow is
greatly affected by this boundary condition since the velocity at the wall surface reaches zero
with the maximum value of shear stress. The wall roughness also affects the accuracy of the
solution to a great extent—sand-roughened surfaces are implemented by most commercial
CFD codes with a corresponding roughness height (k
s
) for the roughness of the wall [
124
].
If that is the case, the following requirements should be satisfied simultaneously [110]:
•
High mesh resolution in the vertical direction close to the bottom of the computational
domain;
•
Maintaining the horizontal homogeneity of the ABL profile upstream and downstream
of the computational domain;
•
The distance between the centre of the first cell away from the bottom boundary (z
p
)
and the bottom wall boundary to be greater than the roughness height (zp>ks);
•Roughness height equal to thirty times the roughness length (z0) (ks= 30 z0).
Different terrains have different roughness lengths. Table 3shows the values of z
0
for six recognizable terrain types in the UK for the design of wind load on buildings
and structures. Although it is common sense to have z
p
>k
s
because it is not physically
meaningful to have mesh cells with centre points within the physical roughness height, this
would lead to a coarse mesh [
110
]. Thus, it is recommended to alleviate the requirement
zp>ks.

Atmosphere 2024,15, 1049 19 of 27
Table 3. Roughness parameters for differ terrain categories [145].
Category z0(m) Remark
0 0.003 Corresponding to large expanses of water, mudflats, snow-covered
farmland, and large flat areas of tarmac
1 0.01
Corresponding to flat grassland, parkland, or bare soil, without hedges,
and with very few isolated obstructions
2 0.03
Meteorological standard, basic terrain roughness corresponding to
typical UK farmland, nearly flat or gently undulating countryside,
fields with crops, fences or low boundary hedges and few trees
3 0.1
Corresponding to farmland with frequent boundary hedges, occasional
small farm structures, houses, or trees
4 0.3 Corresponding to dense woodland, with domestic housing typically
between 10% and 20% of plan-area density
5 0.8
Corresponding to city centres, comprising mostly four-storey buildings,
or higher, typically between 30% and 50% plan-area density
To ensure the homogeneity of the inflow profile through the side and top boundary
conditions, constant shear stress is applied to the sides corresponding to the inflow profile.
A free slip condition at a rigid lid is sometimes used. To achieve the best results, the
top and side boundary conditions are specified as symmetry boundary conditions. This
means that the flow at the sides and the top of the domain will be parallel and the normal
velocity component to the boundary and other flow variables, will disappear, which
could be different from the inflow boundary profile [
115
]. The fluid will mostly leave
the computational domain from the boundary behind the studied building/area. This
boundary is specified as a constant static pressure or an outflow. Specifying an outflow
boundary condition means that all flow variable derivatives are forced to disappear as
in a fully developed flow. However, this could result in the flow coming back inside the
domain and probably resulting in the solution not reaching convergence. To avoid this and
minimise its effect, the boundary is placed far away from the studied building [146].
4.2.5. The Computational Mesh
One of the main factors affecting the quality of the CFD simulation is the computational
mesh. Thus, care should be taken to construct the mesh and specify its type to be suitable
for the flow problem [
125
,
147
]. It is agreed that in industrial applications using CFD
simulations, most of the time is spent on creating the mesh and adjusting it if needed.
This is because a good mesh provides a good balance between the needed computational
power and the accuracy of simulation [
148
]. Due to its important effect on the solution, it
is agreed that different mesh sizes and configurations should be used until it is noticed
that the solution does not change significantly, and solution is independent of the mesh
size [149–153].
The mesh could be refined until the solution does not change. However, this is very
time-consuming, which makes the recommendation by Franke et al. (2011) [
124
] to limit
the tested meshes to three levels of refined meshes a viable practice, widely adopted in
CFD simulations [
154
]. The rule of thumb is that the ratio of cells for two consecutive grids
should be at least 3.4; sometimes, this is not possible due to computational limitations. If
that is the case, it is recommended to refine the mesh in the areas of interest. When studying
the wind flow around bluff bodies, the areas where the flow is expected to be more complex
are upwind and in the leeward direction of the body and these should be the areas of grid
refinements to capture the complicated nature of the flow [70].
Mesh refinement is very problem-dependant, and it is not easy to be specific about the
recommendations on mesh resolution [
155
]. But the general recommendation of the mesh
is that the mesh should not deform the geometry of the studied object; ideally, mesh cells
should be aligned in an equidistant order, especially in areas of high gradients. It should

Atmosphere 2024,15, 1049 20 of 27
be noted that sometimes, compression and stretching are permissible as long as the ratio
between two consecutive cells does not exceed 1.3 [124].
A good-quality mesh would have the angle between the normal vector of a cell surface
and the line connecting the midpoints of the parallel neighbouring cells [
156
]. Figure 8
shows the shapes of the two types of cells that are either hexahedral or tetrahedral cells.
In the structured mesh, hexahedral cells are used, while in the unstructured meshes,
tetrahedral cells are mostly used. Hexahedral cells are preferred to tetrahedral cells [157].
Atmosphere 2024, 15, x FOR PEER REVIEW 21 of 28
Figure 8. Left: hexahedral structured mesh; Right: tetrahedral unstructured mesh [157].
In terms of needed computational power, structured meshes are more advantageous
over unstructured meshes as the equations are solved faster in structured meshes due to
the simplicity of the connectivity between the cells [70]. The main disadvantage of struc-
tured meshes is that although they are limited in representing complex shapes and forms,
they are good in representing rectilinear simple shapes. However, this problem could be
overcome by refining the mesh in the vicinity of complex shapes and geometries. Unstruc-
tured meshes, on the other side, are capable of fiing any geometrical form or shape since
they are made of tetrahedral units. But this comes with high computational cost due to
the complexity of solving the algebraic equations, which is no longer regular as in the
structured mesh. Accordingly, more time will be needed for the calculations to take place
when using an unstructured mesh.
5. Conclusions
This review discussed the most commonly used urban wind assessment tools, which
are in situ measurements, wind tunnel tests, and CFD simulations. The tools were criti-
cally investigated to assess their pros and cons. Observations were recorded, and it could
be argued that for studying the urban wind flow for the purpose of integrating wind tur-
bines within the built environment, CFD simulation is the most relevant tool. This is
mainly due to the iterative nature of the design process and changes that will be made
along the way of deciding about the type of wind turbine integration within the built en-
vironment as well as the optimum mounting/integration location.
Wind turbine integration within the built environment could use existing buildings
or new developments. In existing buildings, all factors affecting the wind flow in the pro-
posed mounting location should be included in the model, and the CFD simulation has
high potential in identifying the optimum mounting location in terms of a high mean
wind speed and low levels of turbulence. In the case of new developments, alternative
design solutions should be investigated, and the CFD simulation tool should be imple-
mented to compare alternative solutions and types of integration. Some of the factors that
should be investigated include, but are not limited to, the surrounding urban configura-
tion, site vegetation, and building heights as they will affect the local air flow at the pro-
posed installation site.
CFD simulation as a tool for assessing urban wind flow for urban wind turbine inte-
gration was discussed further to identify the requirements for using the tool to yield ac-
curate and consistent results. It can be concluded that available computational power
plays a very important role in deciding about a variety of factors affecting the CFD simu-
lation. In addition, the availability of experimental data obtained in wind tunnel tests and
in situ measurements is important for validating the CFD simulation results. When com-
paring RANS, LES, DNS, URANS, and DES models, RANS models are the most com-
monly used for studying the urban wind flow. However, LES, DNS, URANS, and DES
models could yield more accurate results but require higher computational power. Due
to the wide usage of RANS models in studying the urban wind flow, there is a wide range
of best practice guidelines available in the literature, which are summarised in Table 4.
Figure 8. (Left): hexahedral structured mesh; (Right): tetrahedral unstructured mesh [157].
In terms of needed computational power, structured meshes are more advantageous
over unstructured meshes as the equations are solved faster in structured meshes due
to the simplicity of the connectivity between the cells [
70
]. The main disadvantage of
structured meshes is that although they are limited in representing complex shapes and
forms, they are good in representing rectilinear simple shapes. However, this problem
could be overcome by refining the mesh in the vicinity of complex shapes and geometries.
Unstructured meshes, on the other side, are capable of fitting any geometrical form or
shape since they are made of tetrahedral units. But this comes with high computational
cost due to the complexity of solving the algebraic equations, which is no longer regular as
in the structured mesh. Accordingly, more time will be needed for the calculations to take
place when using an unstructured mesh.
5. Conclusions
This review discussed the most commonly used urban wind assessment tools, which
are in situ measurements, wind tunnel tests, and CFD simulations. The tools were critically
investigated to assess their pros and cons. Observations were recorded, and it could be
argued that for studying the urban wind flow for the purpose of integrating wind turbines
within the built environment, CFD simulation is the most relevant tool. This is mainly due
to the iterative nature of the design process and changes that will be made along the way
of deciding about the type of wind turbine integration within the built environment as well
as the optimum mounting/integration location.
Wind turbine integration within the built environment could use existing buildings or
new developments. In existing buildings, all factors affecting the wind flow in the proposed
mounting location should be included in the model, and the CFD simulation has high
potential in identifying the optimum mounting location in terms of a high mean wind
speed and low levels of turbulence. In the case of new developments, alternative design
solutions should be investigated, and the CFD simulation tool should be implemented to
compare alternative solutions and types of integration. Some of the factors that should
be investigated include, but are not limited to, the surrounding urban configuration, site
vegetation, and building heights as they will affect the local air flow at the proposed
installation site.
CFD simulation as a tool for assessing urban wind flow for urban wind turbine integra-
tion was discussed further to identify the requirements for using the tool to yield accurate
and consistent results. It can be concluded that available computational power plays a
very important role in deciding about a variety of factors affecting the CFD simulation. In
addition, the availability of experimental data obtained in wind tunnel tests and in situ

Atmosphere 2024,15, 1049 21 of 27
measurements is important for validating the CFD simulation results. When comparing
RANS, LES, DNS, URANS, and DES models, RANS models are the most commonly used
for studying the urban wind flow. However, LES, DNS, URANS, and DES models could
yield more accurate results but require higher computational power. Due to the wide usage
of RANS models in studying the urban wind flow, there is a wide range of best practice
guidelines available in the literature, which are summarised in Table 4.
Table 4. Best practice guidelines requirements for a reliable CFD simulation.
Solution Method
Second-order schemes or above are recommended for solving algebraic
equations.
Residuals In the range of 10−4to 10−6.
Mesh
Multiblock structured mesh.
Carrying out a sensitivity analysis with three levels of refinements
where the ratio of cells for two consecutive grids should be at least 3.4.
Mesh cells to be equidistant while refining the mesh in areas of
complex flow phenomena.
If cells are stretched, a ratio not exceeding 1.3 between two consecutive
cells should be maintained.
Turbulence model Realizable k-εturbulence model.
Accuracy of studied
buildings Details of dimension equal to or more than 1 m to be included.
Domain dimensions
If H is the building height, the lateral dimension = 2H + building width.
Flow direction dimension = 20H + building dimension in the flow
direction.
Vertical direction = 6H.
A blockage ratio below 3% must be maintained.
Boundary conditions
Inflow: horizontally homogenous log law atmospheric boundary layer
(ABL) velocity profile.
Bottom: a no-slip wall with standard wall functions.
Top and side: symmetry.
Outflow: pressure outlet.
In addition to implementing the above-mentioned requirements as a good start point in
the CFD simulation, validation studies using other wind assessment tools are mandatory to
eradicate any uncertainties or errors in the CFD simulation. The validation could be carried
out by using the recommended parameters for studying the wind flow around a 3D cube
mounted in a turbulent channel flow. Data sets from wind tunnel test results as well as in situ
instruments for this flow problem are well documented in the literature [73,93,130,158–163].
Author Contributions: Conceptualization, I.A.; methodology, I.A.; software, I.A. and R.S.; valida-
tion, I.A. and R.S.; formal analysis, I.A.; investigation, I.A. and R.S.; resources, I.A. and R.S.; data
curation, I.A.; writing—original draft preparation, I.A.; writing—review and editing, I.A. and R.S.;
visualization, I.A.; supervision, I.A.; project administration, I.A.; funding acquisition, I.A. All authors
have read and agreed to the published version of the manuscript.
Funding: This research was funded by Staffordshire Centre for Renewable and Sustainable Engineer-
ing through the Enhancing Research Culture grant (RE-CL-2022-06). The APC was funded by I.A.
and R.S. through their institutions.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: This manuscript serves as an analytical review, utilizing literature for
the analysis without producing new data.

Atmosphere 2024,15, 1049 22 of 27
Acknowledgments: The authors wish to extend their sincere appreciation to Staffordshire Centre
for Renewable and Sustainable Engineering at Staffordshire University for the support provided in
completing this review paper. Furthermore, the authors are grateful to London South Bank University
(LSBU) for facilitating the role of I.A. as a Visiting Professor to LSBU which made the collaboration
with R.S. possible to produce this review.
Conflicts of Interest: The authors declare no conflict of interest.
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