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Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
A review on the CFD analysis of urban microclimate
Y. Toparlar
a,b,⁎
, B. Blocken
a,c
, B. Maiheu
b
, G.J.F. van Heijst
d
a
Building Physics and Services, Department of the Built Environment, Eindhoven University of Technology, P.O. box 513, 5600 MB Eindhoven, The
Netherlands
b
Environmental Modeling, Flemish Institute for Technological Research, Boeretang, 2400 Mol, Belgium
c
Building Physics Section, Department of Civil Engineering, KU Leuven, Bus 2447, 3001 Leuven, Belgium
d
Fluid Dynamics Laboratory, Department of Applied Physics, Eindhoven University of Technology, P.O. box 513, 5600 MB Eindhoven, The Netherlands
ARTICLE INFO
Keywords:
Computational Fluid Dynamics (CFD)
Urban physics
Adaptation measures
Building energy consumption
Sustainability
ABSTRACT
Urban microclimate studies are gaining popularity due to rapid urbanization. Many studies documented that
urban microclimate can affect building energy performance, human morbidity and mortality and thermal
comfort. Historically, urban microclimate studies were conducted with observational methods such as field
measurements. In the last decades, with the advances in computational resources, numerical simulation
approaches have become increasingly popular. Nowadays, especially simulations with Computational Fluid
Dynamics (CFD) is frequently used to assess urban microclimate. CFD can resolve the transfer of heat and mass
and their interaction with individual obstacles such as buildings. Considering the rapid increase in CFD studies
of urban microclimate, this paper provides a review of research reported in journal publications on this topic till
the end of 2015. The studies are categorized based on the following characteristics: morphology of the urban
area (generic versus real) and methodology (with or without validation study). In addition, the studies are
categorized by specifying the considered urban settings/locations, simulation equations and models, target
parameters and keywords. This review documents the increasing popularity of the research area over the years.
Based on the data obtained concerning the urban location, target parameters and keywords, the historical
development of the studies is discussed and future perspectives are provided. According to the results, early
CFD microclimate studies were conducted for model development and later studies considered CFD approach as
a predictive methodology. Later, with the established simulation setups, research efforts shifted to case studies.
Recently, an increasing amount of studies focus on urban scale adaptation measures. The review hints a possible
change in this trend as the results from CFD simulations can be linked up with different aspects (e.g. economy)
and with different scales (e.g. buildings), and thus, CFD can play an important role in transferring urban climate
knowledge into engineering and design practice.
1. Introduction
TheUnitedNations(UN)andtheWorldBankanticipatearapid
increase of the percentage of the world population living in urban areas
within the course of the 21st century [1,2] (Fig. 1). This change is expected
to occur due to the increase in the number of cities, migration from rural to
urban areas and transformation of some rural settlements into urban areas
[3]. Recently, making “cities and human settlements climate resilient and
sustainable”is marked as one of the sustainable development goals by the
UN [4]. As a result, research on sustainable habitats and related topics is
gaining importance and will continue to do so in the coming years [5].
Urban settlements are formed by replacing natural surroundings by
urban environments and the latter create their own, unique micro-
climates.
1
In his pioneering publication “the Climate of London”, Luke
Howard [6] documented that urban microclimates can be substantially
different from their rural counterparts as the former tend to produce
and retain more heat and are therefore characterized by higher
temperatures. This phenomenon is commonly known as the Urban
Heat Island (UHI) effect, a term first used by Manley in 1958 [7],
although Erell et al. [8] mention it might have been coined earlier.
Interpreting the impact of the UHI effect merely as an “increase of
temperature inside urban areas”would be an oversimplification. The
http://dx.doi.org/10.1016/j.rser.2017.05.248
Received 9 September 2016; Received in revised form 30 January 2017; Accepted 26 May 2017
⁎
Corresponding author at: Building Physics and Services, Department of the Built Environment, Eindhoven University of Technology, P.O. box 513, 5600 MB Eindhoven, The
Netherlands.
E-mail address: y.toparlar@tue.nl (Y. Toparlar).
1
According to the Meteorology Glossary of American Meteorological Society (AMS) (http://glossary.ametsoc.org/), the term microclimate is defined as “the fine climatic structure of
the air space that extends from the very surface of the earth to a height where the effects of the immediate character of the underlying surface no longer can be distinguished from the
general local climate.”From a spatial perspective, the glossary defines the meteorological microscale as the “horizontal spatial scales of 2km or less.”
Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
1364-0321/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Please cite this article as: Toparlar, Y., Renewable and Sustainable Energy Reviews (2017), http://dx.doi.org/10.1016/j.rser.2017.05.248
UHI effect in particular and urban microclimate in general can yield a
wide range of impacts on health and energy use and these impacts are
not necessarily negative. For instance, UHIs can reduce building energy
demand depending on the city [9], location within the same city [10],
type of building [11,12] or meteorological conditions [13]. Knowledge
of urban microclimate and its dependency on key physical parameters
is important as input for urban designers, architects and engineers to
design and plan built environments [14–21]. The inherent complexity
and multiscale character evidently requires a multiscale approach for
its analysis.
Urban microclimate as a research topic has a long history. The
paper by Mills [22] identifies the work by Luke Howard [6] as the
starting point. Mills [17] specifies six distinct periods of urban
microclimate studies based on the research methodology followed:
1) Since the 1900s: Observation of urban-rural temperature differ-
ences with conventional meteorological measurement devices;
2) Since the 1960s: Measurement of urban microclimate process
variables such as turbulent heat exchanges and the use of statistical
methods to document UHI intensity;
3) Since the 1970s: Use of early energy budget models for the physical
explanation of the UHI effect, early use of computer modeling
techniques;
4) Since the 1980s: Adoption of experimental approaches, scaled-
physical models and flux measurements (e.g. latent heat flux,
storage heat flux);
5) Since the 1990s: Understanding the relationships between real
urban forms and their effect on urban microclimate, organized field
projects;
6) Since the early 2000s: Development of realistic urban microclimate
models and employment of new techniques for the analysis of urban
microclimate.
As suggested by these six periods, nowadays, a wide range of
approaches can be employed for urban microclimate studies. Mirzaei
Nomenclature
ACH Air change rate (1/h)
AIAA American Institute of Aeronautics and Astronautics
AKNKE Abe-Kondoh-Nagano k-εturbulence model
AMS American Meteorological Society
AQI Air quality index (dimensionless)
AT Air temperature (°C)
BEC Building energy consumption (W)
BES Building Energy Simulations
CFD Computational Fluid Dynamics
CHTC Convective Heat Transfer Coefficient (W/m
2
.K)
CKEKE Chen-Kim Extended k-εturbulence model
CP Pressure (coefficient) (dimensionless)
DBT Dry-bulb temperature (°C)
DKE Durbin k-εturbulence model
DNS Direct Numerical Simulation
DSGS DeardorffSubgrid-scale
ε(TDR) Turbulence Dissipation Rate (m
2
/s
3
)
E (TKE) Turbulent Kinetic Energy (m
2
/s
2
)
EBM Energy Balance Models
ECN Economy (currency)
ED Eddy Diffusivity turbulence model
EPMV Extended PMV (dimensionless)
Fr Froude number (dimensionless)
FYI First Year Index (year)
HF Heat flux (w/m
2
)
HVAC Heating Ventilation and Cooling
IAT Indoor air temperature (°C)
k (TKE) Turbulent Kinetic Energy (m
2
/s
2
)
LRNKE Low Reynolds Number k-εturbulence model
LES Large-Eddy Simulations
MDKE Modified k-εturbulence model
MEE Miao E-εturbulence model
MMM Mesoscale Meteorological Models
MRT Mean radiant temperature (°C)
NOAA National Oceanic and Atmospheric Administration
NWP Numerical Weather Prediction
ω(TDR) Turbulence Dissipation Rate (m
2
/s
3
)
PC Pollutant concentration (dimensionless) (PC)
PD Pressure distribution
PET Physiological equivalent temperature (°C)
PMV Predicted mean vote (dimensionless)
RANS Reynolds-averaged Navier-Stokes
RH Relative humidity (%)
Ri Richardson number (dimensionless)
RKE Realizable k-εturbulence model
RNGKE Re-Normalization Group k-εturbulence model
SAI Solar access index (dimensionless)
SET Standard effective temperature (°C)
SLSGS Smagorinsky-Lilly Subgrid-scale
SPI Statistical performance indicators
SR Solar radiation (W/m
2
)
SSTKW Shear Stress Transport k-ωturbulence model
ST Surface temperature (°C) (ST)
STKE Standard k-εturbulence model
SVF Sky View Factor (dimensionless)
TEP Temperature of equivalent perception (°C)
THI Temperature-humidity index (dimensionless)
TSP Thermal Sensation Perception (dimensionless)
UHI Urban Heat Island
UN United Nations
UTCI Universal thermal climate index (°C)
VR Ventilation rate (l/minute)
WBGT Wet black globe temperature (°C)
WCI Wind comfort index (dimensionless)
WV Wind velocity (m/s)
WVF Water vapor fraction (%)
WVV Wind velocity vectors
WYI Weighted Year Index (year)
YMEE Yamada and Mellor E-εturbulence model
Fig. 1. World population in urban and rural areas. The dotted line denotes the year
2011.
Figure modified from reference [3].
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
2
and Haghighat [15] and Mirzaei [23] distinguish two main categories:
(a) observational approaches and (b) simulation
2
approaches.
Observational approaches refer to measurement techniques such as
field measurements, thermal remote sensing (e.g. satellite imagery) or
small-scale physical modeling (e.g. atmospheric boundary layer wind-
tunnel tests). Traditionally, observational approaches dominated urban
microclimate analysis [24,25]. More recently, the increasing availabil-
ity of computational resources has strongly advocated the application
of numerical simulation approaches [26,27], where a distinction can be
made between Energy Balance Models (EBM) and Computational Fluid
Dynamics (CFD). The main advantage of the numerical simulation
approaches compared with their observational counterparts is the
opportunity to perform comparative analyses based on different
scenarios [19,28]. In addition, while measurements are generally only
performed at a limited number of points in space, numerical simula-
tions can provide information on any investigated variable in the entire
computational domain [16,24,29].
EBMs, which are based on the law of energy conservation for a
control volume, have been used extensively in the past [30] and have
increased in popularity with the pioneering article by Oke [31] entitled
“The Energetic Basis of the Urban Heat Island”. Later, several studies
utilized EBM for validation and model development purposes [32–38].
In the early 2000s, new validated models were proposed by Masson
[39], Martilli et al. [40] and Kanda et al. [41]. Throughout this period
of new EBM developments, the use of observational approaches, such
as heat flux measurements, has continued [42–44], mostly to support
the validation of newly developed models.
From an urban climate research point of view, CFD offers two
advantages compared to EBM: (1) CFD is capable of performing
simulations with the explicit coupling of velocity and temperature
fields and if necessary, with the addition of humidity and pollution
fields; (2) With CFD, it is possible to resolve the flow field at finer scales
(e.g. building or even human scale) than EBM [45]. On the other hand,
CFD simulations require a high-resolution representation of the urban
geometry, the knowledge of boundary conditions for all relevant flow
variables and adequate computational resources [15,16,19].
With the increased necessity for simulations incorporating higher
spatial and modeling details and driven by the advances in computing
power [27], CFD has continued to gain popularity as a tool for urban
microclimate research, in particular from the 1990s. In the concluding
chapters of two urban climate review papers by Souch and Grimmond
[26] and Kanda [28], the increasing popularity of the CFD approach is
pointed out with the following quotes:
“The development and use of CFD is a very active area of inquiry.
The models are becoming more sophisticated in terms of numerical
methods, mesh structures and turbulence modeling approaches.”
(Souch and Grimmond [26]);
“CFD technologies that explicitly resolve urban buildings are the
most complex representation of urban surfaces. Such technologies will
play an important role not only in pure application studies but also in
guiding the improvement of simpler models”(Kanda [28]).
Computational simulations can be employed to study urban micro-
climate at different spatial scales, ranging from the meteorological
mesoscale over the meteorological microscale to the building scale and
the indoor environment [16,19,29] (Fig. 2).
Numerical research at the meteorological mesoscale refers to
climatic studies investigating atmospheric events, which occur within
horizontal distances of a few to several hundred kilometers (e.g.
thunderstorms) [46]. Numerical approaches at this scale are termed
as Numerical Weather Prediction (NWP) models [29,47,48] or
Mesoscale Meteorological Models (MMM) [49–51]. Urban climate
analysis at the mesoscale can be traced back to the early 1970s and
was mainly applied for 2D computational domains [52–57], investigat-
ing flow circulations occurring over urban areas, which were repre-
sented as localized heat sources. Later, mesoscale studies also included
3D applications for specific urban areas, such as St. Louis [58] and
Chicago [59]. Nowadays, many urban climate studies at the meteor-
ological mesoscale are being conducted [60–69] and some of the more
recent efforts are focusing on coupling mesoscale climate models with
finer scale models [50,51,70,71].
CFD at the meteorological microscale considers simulations at
horizontal distances up to about 2 km [29,72]. CFD microscale
simulations provide the possibility for the detailed modeling of every
building and the parameterization of other obstacles within an urban
area. Extensive reviews of CFD studies at the meteorological microscale
were published in the past [16,19,29,73,74]. In recent years, with the
advances in computational resources and the establishment of CFD
best practice guidelines on the relevant topics (e.g. [19,75–79]), CFD
studies at the meteorological microscale have gained popularity. CFD
studies at the meteorological microscale can be used to investigate
wind flow around buildings [45], pedestrian wind comfort [80–82],
pedestrian thermal comfort [81], wind-driven rain [83,84], pollutant
dispersion [85–90], snow drift [91,92] and other topics.
CFD can be utilized for the analysis of the microclimate around
individual buildings, which is classified as the building scale with
typical distances less than 100 m. There have been several review
papers on CFD studies at the building scale [19,29,45,73,74,81].
Specifically, natural ventilation studies [93–96] and studies on
Convective Heat Transfer Coefficients (CHTC) [97–100] are conducted
at this scale. Many studies adopted a 2D modeling approach focusing
on street canyons [101–118], on individual building shaped obstacles
[119] or on vegetation cover [120–122]. For individual buildings,
Building Energy Simulation (BES) is also employed for the analysis of
indoor climate, indoor human thermal comfort and building energy
consumption and recently, several studies have investigated the
possibility for coupling CFD and BES models [123–126].
The smallest scale at which CFD is employed for climatic analysis in
urban areas is the building indoor environment, where typical horizontal
distances are around 10 m and the focus is on indoor climate. Studies at
this scale have employed CFD mainly for ventilation studies [127–129]
and for topics related to HVAC design and building services engineering
[130]. Natural ventilation studies with CFD can also be performed at
multiple scales, by combining building and indoor scales, which enables
researchers to conduct coupled analyses [94,95,131–141].
Some review papers on the analysis of urban microclimate such as
Fig. 2. Schematic representation of the spatial scales in climate modeling, with typical horizontal dimensions.
2
Here, the authors refer to numerical simulations, as opposed to physical simulations
in e.g. atmospheric boundary layer wind tunnels.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
3
Erell and Williamson [142], Ooka [143], Mochida et al. [51] and Lun
et al. [144] have evaluated numerical models in general (including
EBMs), without a specific focus on any CFD approach. Mochida and
Lun [81] have reviewed CFD microclimate studies, but without
focusing on the coupling of velocity and temperature fields. As CFD
studies on urban microclimate are gaining popularity, it is important to
document the achievements and trends in this field for future research,
and this paper serves this purpose.
This paper reviews studies on the CFD analysis of urban micro-
climate. The scope of the review covers studies published in refereed
journals, in English, with 3D computational domains and with coupling
of velocity and temperature fields. To the best of our knowledge, the
first study that fits to this scope is from 1998. Therefore, this review
covers studies from what we consider as the first study in this field until
the ones from 2015. In Section 2, the investigated studies are listed and
classified based on the type of the urban area considered (generic
versus real urban) and methodology followed (with or without valida-
tion study). Section 3 contains a further analysis of the reviewed
studies and Section 4 presents a discussion with future perspectives.
Finally, Section 5 contains the conclusions.
2. Overview of studies on the CFD analysis of urban
microclimate
Within the above-mentioned scope of the review, a total of 183
studies are identified and investigated. The earliest study is from 1998
and the latest is from 2015. Fig. 3 shows the yearly distribution of the
studies, indicating the increasing popularity of the field. The figure
shows that the number of studies considered only in the last three years
constitute more than half of all the studies (104 of 183 studies).
The papers are categorized based on the type of urban area (generic
versus real) (see Fig. 4) and the methodology, without validation versus
with validation. We remark that a study containing validation of at least
one parameter from velocity and/or temperature field is classified as a
study with validation. Fig. 5 shows that most studies are focused on
real urban areas and are conducted without validation.
The studies are summarized in tables with the following entries:
–Author(s) and publication year;
–Reference number as listed in this paper;
–Urban setting/location
○For studies with generic urban areas, the urban geometries are
classified as follows (Fig. 6):
a) Building blocks: Multiple building blocks, distributed with a
generic structure;
b) Street canyon: Only one street canyon;
c) Open space: No obstructions, possibly investigating additional
features such as trees, water bodies etc.;
d) Urban street canyons: Multiple street canyons in an urban
setting;
e) Courtyard: Domains focusing on a single courtyard.
○For studies with real urban areas, the urban location is men-
tioned based on the information provided in the respective
papers.
–Approximate form of the governing equations solved and the
turbulence model/sub-grid scale model used. The investigated
studies employed either Reynolds-averaged Navier Stokes (RANS)
equations or Large Eddy Simulations (LES). The turbulence models
(for RANS) and sub-grid scale models (for LES) employed are:
a) For RANS Abe-Kondoh-Nagano (AKN) k-ε[149] (AKNKE);
Chen-Kim Extended k-ε(CKEKE) [150]; Durbin k-ε[151]
(DKE); Eddy Diffusivity [101] (ED); Low Reynolds Number k-ε
[149,152] (LRNKE); Miao E-ε[153] (MEE); Modified k-ε[131]
(MDKE); Realizable k-ε[154] (RKE); Re-Normalization Group
(RNG) k-ε[155] (RNGKE); Shear Stress Transport (SST) k-ω
[156] (SSTKW); Standard k-ε[157] (STKE); Yamada and Mellor
E-ε[158] (YMEE).
b) For LES DeardorffSubgrid-scale [159] (DSGS); Smagorinsky-
Lilly Subgrid-scale [160] (SLSGS).
–Validation/target parameters:
a) Temperature related: Air temperature (°C) (AT); Dry-bulb
temperature (°C) (DBT); Indoor air temperature (°C) (IAT);
Mean radiant temperature (°C) (MRT); Surface temperature (°C)
(ST); Wet bulb globe temperature (°C) (WBGT);
b) Thermal comfort related: Physiological equivalent temperature
(°C) [161] (PET); Predicted mean vote (-) [162] (PMV) and
Extended PMV (-) (EPMV) [163]; Standard effective tempera-
ture (°C) [164] (SET); Temperature of equivalent perception (°C)
[165] (TEP); Thermal Sensation Perception (-) [166] (TSP);
Universal thermal climate index (°C) [167] (UTCI);
c) Heat transfer related (includes radiation and reflectivity):
Convective heat transfer coefficient (W/m
2
K) (CHTC); Heat flux
(w/m
2
) (HF); Sky View Factor (-) (SVF); Solar access index (-)
(SAI); Solar radiation (W/m
2
) (SR);
d) Flow/ventilation related: Air change rate (1/hour) (ACH);
Pressure (coefficient) (CP); Turbulent kinetic energy (m
2
/s
2
)
(TKE); Turbulence dissipation rate (m
2
/s
3
) (TDR); Ventilation
rate (l/minute) (VR); Wind velocity (m/s) (WV);
e) Humidity/mass transfer related: Relative humidity (%) (RH);
Water vapor fraction (%) (WVF);
f) Dimensionless numbers/indices: Air quality index (-) (AQI);
Froude number (-) (Fr); Richardson number (-) (Ri);
Temperature-humidity index (-) (THI); Wind comfort index (-)
(WCI);
g) Other quantitative parameters: Building energy consumption
(W) (BEC); Economy (currency) (ECN); Pollutant concentration
(unit varies) (PC); Pressure distribution (PD); Statistical perfor-
mance indicators (various, e.g. correlation coefficient) (SPI);
Wind velocity vectors (WVV).
–Keyword categories: For every study, representative keywords are
specified based on the list of keywords provided in each publication.
Later, keywords with similar or interchangeable use are grouped and
in total 37 keyword categories are identified.
3
For papers with less
than five keywords in the list of keywords, first the title and then the
abstract is scanned for selecting suitable keyword categories.
Referring to the title and the abstract for selecting keywords has
its limitations but it was adopted due to the lack of a better
alternative. Some very general keywords, such as microclimate,
CFD and urban heat island (effect) are omitted from the categories.
At the end, five keywords categories per study are identified. In the
remainder of this paper, we will use the word “keyword”to refer to
these “keyword categories”. In alphabetical order, the categories are:
Fig. 3. The yearly distribution of the reviewed journal papers on CFD analysis of urban
microclimate.
3
For instance, various studies investigate the effect of building height, shape façade or
roof on urban microclimate. Studies that use one of these as keywords are grouped in the
keyword category called building form.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
4
1. Adaptation/mitigation;
2. Aspect ratio (i.e. building height/street width);
3. Building form (i.e. height, roof, façade, shape);
4. Canyon (i.e. urban canyon, street canyon);
5. Case comparison/case studies (i.e. scenario analysis);
6. (Convective) heat transfer coefficient (CHTC);
7. Climate (i.e. climate scenarios, climate change, heat wave);
8. Climate sensitive design (i.e. bioclimatic design, climatic de-
Fig. 4. Examples of CFD urban microclimate studies: a) Computational domain and grid for a generic urban domain [145]; b) Domain and grid for a real urban domain [146];c)
Contours of standard effective temperature inside a generic urban domain [147]; d) Contours of air temperature inside a real urban domain [148].
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
5
sign);
9. District comparison (comparison of neighborhoods, streets,
buildings in the same urban area);
10. Diurnal variation (i.e. of temperature, velocity);
11. Economy (i.e. feasibility, return of investment);
12. Energy (i.e. building energy demand);
13. Energy budget (i.e. Energy Balance Models);
14. Heat transfer (i.e. modeling, convection, conduction);
15. Human/pedestrian;
16. Materials/albedo (i.e. absorptivity, reflectivity, conductivity);
17. Model coupling (i.e. mesoscale –microscale, BES-CFD);
18. Model development (i.e. new model, tool, software)
19. Optimization (i.e. algorithms, parametric analysis);
20. Orientation;
21. Pollutant dispersion;
22. Radiation (modeling) (i.e. reflections, solar, shading, SVF);
23. Seasonal variation (i.e. temperature, relative humidity);
24. Specific forms (i.e. courtyards, squares);
25. Statistical analysis (i.e. regression, statistical performance in-
dicators);
26. Surface heating (i.e. heated facades, heated ground surfaces);
27. Sustainable/sustainability;
28. Thermal comfort/heat stress;
29. Thermal stability/instability
30. Turbulent heat fluxes (i.e. latent heat flux, storage heat flux,
anthropogenic heat flux);
31. Urban density (i.e. area density, building density);
32. Urban design/planning (i.e. regulations, design competition,
guidelines);
33. Urban forms/morphology (i.e. building distribution, urban
shape);
34. Vegetation (i.e. greenery, trees, urban parks, green roofs/
facades);
35. Ventilation (i.e. pedestrian level ventilation);
36. Water body (i.e. water ponds, fountains);
37. Wind/flow.
2.1. Studies for generic urban areas
CFD studies for generic urban areas typically comprise simple
building shapes, such as cubes or rectangular prisms. Early CFD
models employed for microclimate analysis considered generic do-
mains for model development and validation purposes. Later studies
were generally conducted to investigate generic aspects of fluid flow
and/or heat transfer in urban areas that can provide basic insights that
subsequently can be translated to understanding these processes in real
urban areas.
Fig. 7 depicts the number of publications and the percentage of
studies for generic urban areas among all the papers investigated in
this review. Generic urban areas in CFD microclimate analysis were
quite popular in the early years of this field. Even though the number of
publications of this sub-category kept increasing, with the development
of new models and successful model validations, their share among all
the studies seems to have declined in time. Of all publications reviewed
in this paper, 61 of 183 (33.3%) studies focus on generic urban areas.
2.1.1. Studies without validation
Most early studies on generic urban areas did not consider
validation. For example, this is the case for the five of the oldest
studies in this review: Bruse and Fleer in 1998 [168], Herbert el al. in
1998 [169], Herbert and Herbert in 2002 [170], Dimoudi and
Nikolopoulou in 2003 [171], and Baik et al. in 2003 [172]. The sub-
category “generic urban areas –without validation”contains 40
Fig. 5. Categorical distribution of the studies investigated in this paper. Categories are
based on the type of urban area (generic vs. real) and on the methodology followed (with
vs. without validation).
Fig. 6. Different urban geometries used in generic CFD studies.
Fig. 7. Number of publications and percentage of studies for generic urban areas among
all the papers investigated in this review.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
6
Table 1
Overview of studies in the sub-category “generic urban areas - without validation”.
# Authors (year) Ref. Urban setting Equations / Models Keywords Parameters
1 Bruse and Fleer (1998) [168] Building blocks RANS / YMEE Model development, vegetation, heat transfer, urban form, wind AT, WV
2 Herbert et al. (1998) [169] Street canyon RANS / STKE Material (albedo), seasonal variation, diurnal variation, canyon, energy budget AT
3 Herbert and Herbert (2002) [170] Street canyon RANS / STKE Canyon, aspect ratio, energy budget, heat transfer, building form (height) AT
4 Dimoudi and Nikolopoulou (2003) [171] Building blocks Not specified Vegetation, urban density, case comparison, radiation (SVF), orientation AT
5 Baik et al. (2003) [172] Street canyon RANS / ED Heat transfer, canyon, pollutant dispersion, wind (flow), turbulent heat fluxes AT, ST, WV
6 Robitu et al. (2004) [173] Open space (water pond) RANS / STKE Water body, heat transfer, building energy, turbulent heat fluxes, coupling AT, ST, WVF
7 Murakami (2004) [174] Building blocks RANS / MDKE Model development, urban morphology, vegetation, case comparison, thermal comfort AT, MRT, SET
8 Ali-Toudert and Mayer (2006) [175] Street canyon RANS / YMEE Thermal comfort, aspect ratio, orientation, urban design, canyon AT, SAI, PET
9 Murakami (2006) [176] Building blocks RANS / MDKE Model development, urban morphology, vegetation, case comparison, thermal comfort AT, MRT, SET
10 Grignaffini and Vallati (2007) [177] Building blocks, open space RANS / MDKE Vegetation, climate (scenario analysis), urban morphology, materials, wind AT, ST
11 Lin et al. (2008) [178] Building blocks RANS / STKE Vegetation, thermal comfort, urban form, pedestrian, case comparison AT, RH, SET, MRT, WV
12 Chen et al. (2008) [179] Building blocks RANS / MDKE Optimization, vegetation, model development, thermal comfort, coupling AT, MRT, SET, WV
13 Zhao et al. (2008) [180] Urban street canyon Not specified Aspect ratio, materials, orientation, building form (facades), canyon AT, SET, ST, WBGT
14 Ooka et al. (2008) [181] Building blocks RANS / MDKE Vegetation, optimization, thermal comfort, model development, economy ECN, SET, SVF
15 Dimitrova et al. (2009) [182] Urban street canyon RANS / ED Canyon, wind, heat transfer, model development, building form (facades) AT, WV
16 Okeil (2010) [183] Building blocks RANS / YMEE Building form, energy (building), urban form, radiation (solar), vegetation AT, WV
17 Hong et al. (2011) [184] Street canyon RANS / STKE Vegetation, wind, optimization, canyon, radiation (solar) AT, WV
18 Park et al. (2012) [185] Street canyon LES / DSGS Canyon, wind, heat transfer, surface heating, case comparison AT, TKE, WV
19 Berkovic et al. (2012) [186] Courtyard RANS / YMEE Thermal comfort, specific forms (courtyards), radiation (shading), orientation, case
comparison
AT, PMV, RH, SR
20 Qu et al. (2012) [145] Building blocks RANS / STKE Heat transfer, coupling, model development, radiation (solar), wind AT, ST, TKE, WV
21 Bo-ot et al. (2012) [187] Urban street canyon RANS / STKE Vegetation, energy (building), urban form, case comparison, optimization AT, BEC, WV
22 Mirzaei and Haghighat (2012) [188] Building blocks RANS / STKE Aspect ratio, material (albedo), building form (façade), thermal comfort, case
comparison
AQI, THI, WCI
23 Yang et al. (2012) [189] Building blocks RANS / YMEE Energy (building), urban form, vegetation, model coupling, case comparison AT, BEC, IAT, RH, WT
24 Lee et al. (2013) [190] Building blocks RANS / RKE Building form (height), urban form, ventilation, case comparison, wind WV
25 Johansson et al. (2013) [191] Building blocks, open space RANS / YMEE Building form (height), materials, vegetation, urban density, thermal comfort AT, MRT, RH, ST, TEP, WV
26 Yahia and Johansson (2013) [192] Building blocks RANS / YMEE Urban planning, aspect ratio, vegetation, orientation, thermal comfort PET, ST
27 Hong and Lin (2014) [193] Building blocks RANS / STKE Urban morphology, vegetation, thermal comfort, ventilation, case comparison AT, MRT, SET, VW, WV
28 de Lieto Vollaro et al. (2014) [194] Street canyon RANS / STKE Canyon, radiation (solar), aspect ratio, model development, case comparison AT, WV
29 Wang et al. (2014) [195] Urban street canyon RANS / STKE Canyon, wind, ventilation, heat transfer, building form (façade) HF, PD, VR, WVV
30 Kim et al. (2014) [196] Urban street canyon RANS / STKE Canyon, wind, vegetation, case comparison, building form (roof) AT, TKE, WV, WVV
31 Perini and Magliocco (2014) [197] Building blocks RANS / YMEE Urban density, aspect ratio, vegetation, thermal comfort, case comparison AT, MRT, PMV
32 Bottillo et al. (2014) [198] Street canyon RANS / STKE Canyon, wind, heat transfer, solar radiation, model development CHTC, RI, ST, WV
33 Yahia and Johansson (2014) [199] Building blocks RANS / YMEE Urban design, aspect ratio, vegetation, orientation, thermal comfort PET, ST
34 Ma et al. (2015) [200] Building blocks RANS / DKE Thermal comfort, statistical analysis, model development, pedestrians, diurnal
variation
MRT, SET, WV
35 Liu et al. (2015) [201] Building blocks LES / SLSGS; RANS / RKE, SSTKW CHTC, energy (building), urban density, wind, model development BEC, CHTC, ST
36 Hong and Lin (2015) [147] Building blocks RANS / STKE Urban form, vegetation, optimization, thermal comfort, pedestrian PD, SET, WV
37 Allegrini et al. (2015) [202] Building blocks RANS / STKE Energy (building), urban form / morphology, mitigation, case comparison, urban
density
AT, BEC, ST, WV
38 Liu et al. (2015) [203] Building blocks Not specified Energy (building), climate, diurnal variation, model coupling, model development AT, BEC, WV
39 Botham-Myint et al. (2015) [204] Building blocks RANS / STKE Materials (albedo), building form, thermal comfort, urban morphology, pedestrian AT
40 Allegrini et al. (2015) [205] Building blocks RANS / STKE Turbulent heat fluxes, energy (building), building form, ventilation, urban morphology AT, HF, WV
Abbreviations: AKN k-ε[149] (AKNNE); Chen-Kim Extended k-ε(CKEKE) [150]; DeardorffSubgrid-scale [159] (DSGS); Durbin k-ε[151] (DKE); Eddy Diffusivity [101] (ED); Low Reynold Number k-ε[149,152] (LRNKE); Miao E-ε[153]
(MEE); Modified k-ε[131] (MDKE); Realizable k-ε[154] (RKE); RNG k-ε[155] (RNGKE); Smagorinsky-Lilly Subgrid-scale [160]; SST k-ω[156] (SSTKW); Standard k-ε[157] (STKE); Yamada and Mellor E-ε[158] (YMEE). Air change rate (1/
hour) (ACH); Air quality index (-) (AQI); Air temperature (°C) (AT); Building energy consumption (W) (BEC); Convective heat transfer coefficient (W/m
2
K) (CHTC); Dry-bulb temperature (°C) (DBT); Economy (currency) (ECN); Froude number
(-) (Fn); Heat flux (w/m
2
) (HF); Indoor air temperature (°C) (IAT); Mean radiant temperature (°C) (MRT); Physiological equivalent temperature (°C) [161] (PET); Predicted mean vote (-) [162] (PMV); Extended PMV (-) (EPMV) [163]; Pressure
(coefficient) (CP); Pollutant concentration (%) (PC); Pressure distribution (PD); Relative humidity (%) (RH); Richardson number (-) (Ri); Sky view factor (-) (SVF); Solar access index (-) (SAI); Solar radiation (W/m
2
) (SR); Standard effective
temperature (°C) [164] (SET); Statistical Performance Indicators (various, e.g. Correlation coefficient)(SPI); Surface temperature (°C) (ST); Temperature-humidity index (-) (THI); Temperature of equivalent perception (°C) [165] (TEP); Thermal
Sensation Perception [166] (TSP); Turbulence dissipation rate (m
2
/s
3
) (TDR); Turbulent kinetic energy (m
2
/s
2
) (TKE); Universal thermal climate index (°C) [167] (UTCI); Ventilation rate (l/minute) (VR); Water vapor fraction (%) (WVF); Wet
black globe temperature (°C) (WBGT); Wind comfort index (-) (WCI); Wind velocity (m/s) (WV); Wind velocity vectors (WVV).
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
7
Table 2
Overview of studies in the sub-category “generic urban areas –studies with validation”.
# Authors (year) Ref. Urban setting Equations / Models Validation
parameter
Keywords Parameters
1 Gu et al. (2010) [216] Open space, street canyon LES / SLSGS WV Vegetation, thermal stability, canyon, pollutant dispersion,
surface heating
AT, WV
2 Li et al. (2010) [217] Street canyon LES / SLSGS Ri Surface heating, canyon, thermal stability, pollutant dispersion,
flow
AT, PC, Ri, WV
3 Mirzaei and Haghighat (2010) [218] Building blocks, urban street
canyon
RANS / STKE WV Ventilation, mitigation, thermal comfort, pedestrian, thermal
stability
AT, Ri, WV
4 Kwak et al. (2011) [219] Street canyon RANS / RNGKE ST Canyon, diurnal variation, wind (flow), radiation, surface
heating
AT, HF, ST, WV
5 Luo and Li (2011) [220] Building blocks RANS / RNGKE and SSTKW WV Ventilation, wind (flow), canyon, surface heating, building form
(height)
ACH, AT, WV
6 Qu et al. (2011) [207] Building blocks RANS / STKE ST Coupling, model development, heat transfer, wind (flow),
diurnal variation
CHTC, HF, ST
7 Haghighat and Mirzaei (2011) [221] Building blocks RANS / RNGKE AT, WV Surface heating, thermal stability, ventilation, material
(albedo), canyon
AT, PC, WV
8 Pillai and Yoshie (2012) [215] Building blocks RANS / LRNKE AT, HF, WV CHTC, urban form, building form (height), case comparison,
urban density
AT, CHTC, HF, WV
9 Mirzaei and Carmeliet (2013) [208] Building blocks RANS / RNGKE WV Canyon, wind, model development, case comparison,
orientation
ST, WV
10 Vidrih and Medved (2013) [209] Open space RANS / RNGKE AT Vegetation, urban density, climate, case comparison, model
development
AT
(urban park)
11 Pillai and Yoshie (2013) [214] Building blocks RANS / LRNKE HF CHTC, flow, thermal stability, surface heating, case comparison AT, CHTC, HF
12 Liu et al. (2013) [210] Building blocks LES / SLSGS; RANS / SSTKW,
RKE
CHTC, ST CHTC, urban density, building energy, model development,
case comparison
CHTC, ST, WV
13 Yaghoobian et al. (2014) [222] Street canyon LES / DSGS Fr, WV Diurnal variation, wind(flow), canyon, thermal stability,
material (albedo)
Fr, HF, PD, ST, TKE, WV
14 Taleghani et al. (2014) [223] Building blocks, courtyards RANS / YMEE AT Thermal comfort, orientation, urban form, case comparison,
specific forms (courtyards)
AT, MRT, PET, WV
15 Qaid and Ossen (2014) [224] Street canyon RANS / YMEE AT Aspect ratio, building form (height), climate (hot and arid),
diurnal variation, case comparison
AT, ST, WV
16 Bottillo et al. (2014) [211] Street canyon RANS / STKE AT, WV Canyon, radiation, model development, heat transfer, wind
(flow)
AT, CHTC, ST, WV
17 Santiago et al. (2014) [212] Street canyon RANS / STKE AT, WV Radiation (solar), orientation, heat transfer, wind (flow), model
development
AT, WV
18 Nazarian and Kleissl (2015) [225] Building blocks RANS / RKE ST Material (albedo), aspect ratio, heat transfer, canyon, diurnal
variation
HF, SR, ST, WV
19 Ghaffarianhoseini et al. (2015) [226] Courtyards RANS / YMEE DBT Thermal comfort, building form, albedo, vegetation, specific
forms (courtyards)
AT, DBT, MRT, PET, PMV,
RH
20 Xue et al. (2015) [213] Open space (water body) RANS / STKE DBT, RH Water body, heat transfer, model development, wind (flow),
adaptation
DBT, RH
21 Yumino et al. (2015) [206] Building blocks RANS / DKE HF, ST Adaptation / mitigation, climate, vegetation, materials (albedo),
building form
BEC, HF, MRT, SET, ST,
WBGT
Abbreviations: AKN k-ε[149] (AKNNE); Chen-Kim Extended k-ε(CKEKE) [150]; DeardorffSubgrid-scale [159] (DSGS); Durbin k-ε[151] (DKE); Eddy Diffusivity [101] (ED); Low Reynold Number k-ε[149,152] (LRNKE); Miao E-ε[153]
(MEE); Modified k-ε[131] (MDKE); Realizable k-ε[154] (RKE); RNG k-ε[155] (RNGKE); Smagorinsky-Lilly Subgrid-scale [160]; SST k-ω[156] (SSTKW); Standard k-ε[157] (STKE); Yamada and Mellor E-ε[158] (YMEE). Air change rate (1/
hour) (ACH); Air quality index (-) (AQI); Air temperature (°C) (AT); Building energy consumption (W) (BEC); Convective heat transfer coefficient (W/m
2
K) (CHTC); Dry-bulb temperature (°C) (DBT); Economy (currency) (ECN); Froude number
(-) (Fn); Heat flux (w/m
2
) (HF); Indoor air temperature (°C) (IAT); Mean radiant temperature (°C) (MRT); Physiological equivalent temperature (°C) [161] (PET); Predicted mean vote (-) [162] (PMV); Extended PMV (-) (EPMV) [163]; Pressure
(coefficient) (CP); Pollutant concentration (%) (PC); Pressure distribution (PD); Relative humidity (%) (RH); Richardson number (-) (Ri); Sky view factor (-) (SVF); Solar access index (-) (SAI); Solar radiation (W/m
2
) (SR); Standard effective
temperature (°C) [164] (SET); Statistical Performance Indicators (various, e.g. Correlation coefficient)(SPI); Surface temperature (°C) (ST); Temperature-humidity index (-) (THI); Temperature of equivalent perception (°C) [165] (TEP); Thermal
Sensation Perception [166] (TSP); Turbulence dissipation rate (m
2
/s
3
) (TDR); Turbulent kinetic energy (m
2
/s
2
) (TKE); Universal thermal climate index (°C) [167] (UTCI); Ventilation rate (l/minute) (VR); Water vapor fraction (%) (WVF); Wet
black globe temperature (°C) (WBGT); Wind comfort index (-) (WCI); Wind velocity (m/s) (WV); Wind velocity vectors (WVV).
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
8
studies, which are summarized in Table 1.
Most early studies focused on new model developments, demon-
strating the suitability of CFD for microclimate analysis. For instance,
the study by Bruse and Fleer [168] focused on surface, plant and air
interaction at the microscale and is considered as the original
documentation of the CFD microclimate software ENVI-Met, which is
a tool increasingly employed by researchers in later years.
In the late 1990s, EBMs were the main tool used for the numerical
analysis of urban microclimate. In the early years of CFD microclimate
analysis, the influence of EBM methodology on CFD simulations is
evidenced in the following ways: (1) CFD studies have averaged most of
the turbulent heat fluxes in urban canopies similar to the way they were
used in EBMs [169,170,172,173]; (2) the terms, which were popular in
EBMs, such as “energy budget”and “turbulent heat fluxes,”were very
often used as the main keywords in these early CFD studies.
The five most commonly used keywords in this sub-category are
vegetation (19 of 40 studies), thermal comfort (16 of 40 studies), case
comparison (15 of 40 studies), wind (flow) (12 of 40 studies) and
canyon (12 of 40 studies). Keywords such as climate sensitive design,
sustainable and thermal stability do not occur as keywords in any of
these studies.
4
2.1.2. Studies with validation
Validation of CFD studies for generic urban areas is typically
performed with data from wind-tunnel measurements [15,19] whereas
validation with field measurements is less common [206]. Many
studies have been performed on the CFD validation of urban flow
patterns in terms of velocity fields [29] but these studies are often
conducted for isothermal conditions and as such are not within the
scope of this review. As mentioned at the beginning of this section, a
study is considered “with validation”as long as there is at least one
parameter related to velocity or temperature fields, which is compared
with measurement data. The sub-category “generic urban areas –with
validation”contains 21 studies, which are summarized in Table 2.
Some of the validation studies are conducted to investigate and
demonstrate the suitability and accuracy of newly developed CFD
approaches [207–213]. Others focus on the CHTC of individual
buildings in urban areas [210,214,215], which in turn can be used
for coupling CFD with BES [210]. Validation studies on generic urban
areas can be the first step towards justification of a CFD approach in
modeling the cooling effect of adaptation measures, before implement-
ing the same approach on real urban areas. Some of the studies for
instance propose validated approaches for the cooling effect from
vegetation sources [209] and from water bodies [213] on generic
urban domains.
The five most commonly used keywords in this sub-category are
wind (flow) (10 of 21 studies), canyon (9 of 21 studies), case
comparison (7 of 21 studies), model development (7 of 21 studies)
and surface heating (6 of 21 studies). Note however that keywords such
as climate sensitive design, energy budget, optimization, seasonal
variation, sustainability and urban design do not occur as keywords
in any of these 21 studies.
5
2.2. Studies for real urban areas
The term “real urban areas”can cover only a few buildings to a
portion of a city. CFD simulations on real urban areas are performed
either as practical case studies or –in case of studies with validation –
to investigate the possibilities and limitations of CFD for real urban
areas that are generally characterized by a complexity that substantially
exceeds that of generic urban areas.
Fig. 8 depicts the number of publications and the percentage of
studies for real urban areas among all the papers investigated in this
review. The number of publications for real urban areas has been
rapidly increasing especially in the last five years. Of all the publica-
tions reviewed in this paper, 122 of 183 (66.6%) studies focus on real
urban areas.
2.2.1. Studies without validation
CFD studies on real urban areas without validation are generally
comparative studies, where several urban configurations, design para-
meters, neighborhoods (districts) within the same urban area are
compared. Most of these studies aim to reach best-case scenarios
based on the optimization of a target parameter (e.g. outdoor thermal
comfort). The sub-category “real urban areas –without validation”
contains 65 studies, rendering this sub-category the most popular one.
These studies are summarized in Table 3.
The table indicates that studies in this sub-category have two
characteristics. First, all studies are conducted using the RANS
equations, which may be attributed to the high computational require-
ments of LES. Second, most studies are characterized by practical
rather than fundamental keywords. The five most commonly used
keywords in this sub-category are vegetation (39 of 65 studies), case
comparison (36 of 65 studies), thermal comfort/heat stress (29 of 65
studies), urban design/planning (27 of 65 studies) and materials/
albedo (19 of 65 studies). Keywords which might indicate that a
particular study’s focus is on fundamental aspects, such as CHTC, heat
transfer or wind (flow), are not used often (1 of 65, 2 of 65 and 9 of 65
studies respectively). Keywords such as energy budget, optimization,
statistical analysis, surface heating and thermal stability do not occur
as keyword
6
in any of these 65 studies.
2.2.2. Studies with validation
Validation of CFD studies for real urban areas is typically per-
formed with data from on-site (field) measurements. Wind-tunnel
measurements focusing on real urban areas can be challenging as the
representative models of real, complex urban formations are more
difficult (and often more expensive) to build than generic forms and
they are often larger, hence might be difficult to fit into a wind tunnel.
Similar to CFD in general and for CFD studies on urban micro-
climate, verification and validation are essential actions towards
accurate and reliable results [16,29,45,74–77,80,82,86,128,292–
304]. Even in today’s era of numerical climate models, new field
measurements are being conducted for this purpose [17,305].
CFD urban microclimate studies in the sub-category “real urban
areas –with validation”include validation based on one or more of the
simulation parameters with measurements. We note that studies on the
CFD validation of urban flow patterns in terms of velocity fields are
often conducted for isothermal conditions and as such are not within
the scope of this review. For real urban areas, the field measurement of
Fig. 8. Number of publications and percentage of studies for real urban areas among all
the papers investigated in this review.
4
Although these words might be used within the text itself, here, they are not present
in the keyword categories as extracted from abstract and the list of keywords.
5
Although these words might be used within the text itself, here, they are not present
in the keyword categories as extracted from the abstract and the list of keywords.
6
Although these words might be used within the text itself, here, they are not present
in the keyword categories as extracted from the abstract and the list of keywords.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
9
Table 3
Overview of studies in the sub-category “real urban areas –without validation”.
# Authors (year) Ref. Location (Country) Equations / Models Keywords Parameters
1 Fang et al. (2004) [227] Fangzhuang, Beijing (China) RANS / MEE Urban planning, coupling (mesoscale - microscale), model development,
vegetation, water body
ST, WV
2 Robitu et al. (2006) [228] Fleuriot Square, Nantes (France) RANS / STKE Vegetation, water body, thermal comfort, model development, case
comparison
MRT, PMV, ST, WV
3 Yu and Hien (2006) [229] Bukit Batok Nature Park and Clementi Woods Park
(Singapore)
RANS / YMEE Vegetation, district comparison, case comparison, diurnal variation,
urban form
AT
4 Wong et al. (2007) [230] National University of Singapore (Singapore) RANS / YMEE Vegetation, urban form, case comparison, diurnal variation, materials AT
5 Li an Yu (2008) [231] Wuhan City (China) Not specified Urban planning, water body, vegetation, building form (height), model
development
AT, ST
6 Huang et al. (2008) [232] Kawasaki City (Japan) RANS / STKE Pollutant dispersion, diurnal variation, turbulent heat fluxes, wind, heat
transfer
AT, ST, WV
7 Andrade and Alcoforado (2008) [233] Telheiras, Lisbon (Portugal) RANS / YMEE Thermal comfort, urban form , seasonal variation, district comparison,
radiation (SVF)
AT, MRT, PET
8 He and Hoyano (2009) [234] Tonami (Japan) RANS / AKNKE Thermal comfort, coupling, model development, materials, vegetation AT, MRT, SET, WV
9 Chen et al. (2009) [235] Otemachi and Kyobashi (Japan) RANS / STKE Mitigation, thermal comfort, turbulent heat fluxes, district comparison,
case comparison
AT, WV
10 Fahmy and Sharples (2009) [236] 5th Community, Cairo (Egypt) RANS / YMEE Case comparison, vegetation, urban form, aspect ratio, building density PMV
11 Fahmy et al. (2010) [237] Misr Al-Gadida, Cairo (Egypt) RANS / YMEE Vegetation, surface fluxes, radiation, diurnal variation, district
comparison
AT, MRT, RH
12 Hsieh et al. (2010) [238] Tokyo (Japan) RANS / STKE Material (albedo), wind, water body, vegetation, urban planning AT, ST, WV
13 Al-Sallal and Al-Rais (2011) [239] Al-Ras, Dubai (United Arab Emirates) RANS / STKE Ventilation, urban form, canyon, model development, seasonal variation AT, WV
14 Ashie and Kono (2011) [240] Nihonbashi, Tokyo (Japan) RANS / STKE Urban design, case comparison, wind, climate-sensitive design, coupling
(mesoscale –microscale)
AT, WV
15 Bouyer et al. (2011) [241] Lyon (France) RANS / STKE Energy (building), vegetation, urban design, coupling, materials AT, BEC
16 Fintikakis et al. (2011) [242] Tirana (Albania) RANS / STKE Climate sensitive design, materials, vegetation, thermal comfort, case
comparison
AT, ST, WV
17 Kaoru et al. (2011) [243] Osaka City (Japan) RANS / STKE Radiation, diurnal variation, case comparison, model development,
coupling
AT, ST, WV
18 Fahmy and Sharples (2011) [244] 5th Community, Cairo (Egypt) RANS / YMEE Urban design, case comparison, thermal comfort, urban form, urban
density
AT, PMV
19 Synnefa et al. (2011) [245] Ag. Paraskevi, Athens (Greece) RANS / STKE Materials (albedo), radiation (reflections), mitigation, diurnal variation,
urban design
AT
20 Boukhabla and Alkama (2012) [246] Street of the Republic, Biskra (Algeria) RANS / YMEE Vegetation, heat transfer, case comparison, urban form, diurnal variation AT, RH, SR, WV
21 Al-Sallal and Al-Rais (2012) [247] Al-Mankhool, Dubai (United Arab Emirates) RANS / STKE Ventilation, urban form, canyon, thermal comfort, seasonal variation AT, WV
22 Lenzholzer (2012) [248] Grote Markt, Groningen (Netherlands) RANS / YMEE Urban design, case comparison, thermal comfort, specific forms
(squares), vegetation
PMV
23 Tominaga (2012) [249] Niigata City (Japan) RANS / DKE Urban form, building form (height), ventilation, case comparison, wind ACH, AT, WV
24 Baik et al. (2012) [250] Central Region, Seoul (South Korea) RANS / RNGKE Building form (roof), vegetation, canyon, urban density, case comparison AT, PC
25 Carfan et al. (2012) [251] Consolocao and Fontes do Ipiranga State Park, Sao Paulo
(Brazil)
RANS / YMEE Thermal comfort, vegetation, materials, district comparison, building
form (height)
MRT, PMV, WV
26 Stavrakakis et al. (2012) [252] Gazi, Heraclion (Greece) RANS / STKE Climate-sensitive design, thermal comfort, specific forms (squares),
vegetation, diurnal variation
AT, EPMV, ST
27 Palme and Ramirez (2013) [253] Avenida Brasil, Antofagasta (Chile) RANS / YMEE Urban design, vegetation, sustainability, climate (dry and arid), radiation
(SVF)
AT, MRT, SVF, WV
28 Maragkogiannis et al. (2013) [254] 1866 Square, Chania (Greece) RANS / CKEKE Case comparison, materials, thermal comfort, climatic design, specific
forms (squares)
AT, ST, WV
29 Dütemeyer et al. (2013) [255] Elisabeth-Stift Erle, Gelsenkirchen (Germany) RANS / YMEE Thermal comfort, adaptation, vegetation, urban planning, climate (future
scenarios)
AT, PET, WV
30 Declet-Barreto et al. (2013) [256] The Latino Urban Core, Phoenix (USA) RANS / YMEE Vegetation, climate (heat wave), mitigation, urban design, case
comparison
AT, ST
31 Taleb and Hijleh (2013) [257] Jumairah and Bastakiyah, Dubai (United Arab Emirates) RANS / YMEE Urban design, case comparison, urban form, seasonal variation, wind AT, SVF, WV
32 Egerhazi et al. (2013) [258] Szeged (Hungary) RANS / YMEE Seasonal variation, diurnal variation, thermal comfort, radiation
(shading), vegetation
PET
33 Radhi et al. (2013) [259] Amwaj Islands and Wadi Al-Sail (Bahrain) RANS / RNGKE District comparison, wind, thermal comfort, urban density, urban form AT, PMV, WV
(continued on next page)
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
10
Table 3 (continued)
# Authors (year) Ref. Location (Country) Equations / Models Keywords Parameters
34 Miao et al. (2013) [260] Zhongguancun, Beijing (China) RANS / STKE Coupling, pollutant dispersion, wind, diurnal variation, model
development
AT, TKE, WV
35 Frohlich and Matzarakis (2013) [261] The Place of the Old Synagougue, Freiburg (Germany) RANS / YMEE Radiation (SVF), case comparison, thermal comfort, urban design,
specific forms (squares)
PET, SVF
36 Egerhazi et al. (2013) [262] Szeged (Hungary) RANS / YMEE Materials, vegetation, water body, urban design, case comparison MRT, PMV
37 Tiwary and Kumar (2014) [263] Not mentioned RANS / YMEE Vegetation, seasonal variation, wind, materials (albedo), pollutant
dispersion
AT, RH, WV
38 Taleb and Taleb (2014) [264] Dubai International Academic City, Dubai (United Arab
Emirates)
RANS / YMEE Thermal comfort, urban planning, orientation, case comparison,
vegetation
AT, MRT, PMV, RH, WV
39 Ambrosini et al. (2014) [265] Old town, Teramo (Italy) RANS / YMEE Building form (roof), vegetation, materials (albedo), case comparison,
diurnal variation
AT, RH, WV
40 Gros et al. (2014) [266] Pin Sec district, Nantes (France) RANS / STKE Materials (albedo), case comparison, energy (building), coupling, model
development
AT, BEC, ST
41 Ketterer and Matzarakis (2014) [267] City center, Stuttgart (Germany) RANS / YMEE Thermal comfort, model development, vegetation, case comparison,
urban planning
AT, PET
42 Ketterer and Matzarakis (2014) [268] Stuttgart-West, Stuttgart (Germany) RANS / YMEE Urban planning, case comparison, vegetation, aspect ratio, orientation AT, MRT, SVF, WV
43 Yi and Peng (2014) [269] Weston Park, Sheffield (England) RANS / YMEE Climate (future scenarios), case comparison, energy (building), coupling,
thermal comfort
AT, BEC, SR
44 Peng and Elwan (2014) [270] New Cairo, Cairo (Egypt); RANS / YMEE Climate (future scenarios), energy (building), coupling, urban design,
case comparison
AT, MRT, RH, WV
Sheffield University Campus, Sheffield (England)
45 Lehmann et al. (2014) [271] Inner city, Dresden (Germany) RANS / YMEE Vegetation, urban form, adaptation, climate (change), urban design AT
46 Ciaramella et al. (2014) [272] CityLife urban district and Milan, Milan (Italy) RANS / YMEE District comparison, thermal comfort, urban design, urban form,
seasonal variation
AT, PC
47 Taleghani et al. (2014) [273] Portland State University, Portland (USA) RANS / YMEE Specific forms (courtyards), water body, materials (albedo), mitigation,
thermal comfort
AT, MRT
48 Sodoudi et al. (2014) [274] 6th urban district, Tehran (Iran) RANS / YMEE Materials (albedo), vegetation, case comparison, mitigation, urban
density
AT, RH
49 Gromke et al. (2015) [275] City center, Arnhem (Netherlands) RANS / RKE Vegetation, adaptation, climate (heat wave), building form (façade, roof),
case comparison
AT, WV
50 Djukic et al. (2015) [276] Central zone, Leskovac (Serbia) RANS / YMEE Climate sensitive design, urban design, specific forms (squares),
vegetation, case comparison
AT
51 Tsilini et al. (2015) [277] Chalepa, Chanie (Greece) RANS / YMEE Vegetation, case comparison, seasonal variation, bioclimatic design,
urban design
AT, ST
52 Peng et al. (2015) [278] Dazhimen neighborhood, Wuhan City (China) RANS / RKE Urban planning, case comparison, urban form, sustainability, thermal
comfort
ST, WV
53 O’Malley et al. (2015) [279] West Kensington (Seagrave Site), London (England) RANS / YMEE Vegetation, materials (albedo), water body, case comparison, urban
design
AT, ST
54 Peng et al. (2015) [280] Old district, Wuhan City (China) RANS / RKE Urban form, urban design, thermal comfort, radiation (solar), wind AT, ST, WV
55 Conry et al. (2015) [281] Chicago Metropolitan Area, Chicago (USA) RANS / YMEE Coupling, energy (building), seasonal variation, climate (change), model
development
BEC, MRT, PMV
56 Middel et al. (2015) [282] The City of Phoenix, Phoenix (USA) RANS / YMEE Vegetation, materials (albedo), climate (future scenarios), building form
(roof), mitigation
AT
57 Peng et al. (2015) [283] Wuhan (China) RANS / RKE case comparison, urban design / planning, pedestrian, thermal comfort,
ventilation
ST, WV
58 Salata et al. (2015) [284] Rome (Italy) RANS / YMEE thermal comfort, mitigation, vegetation, materials (albedo), specific
forms (courtyard)
AT, MRT, PMV
59 Yang et al. (2015) [285] Taipei (Taiwan) RANS / RKE case comparison, thermal comfort, urban design, vegetation, specific
forms (squares)
AT, WV
60 An et al. (2015) [286] Hong Kong (China) RANS / YMEE case comparison, water body, vegetation, district comparison,
sustainability
AT
61 Wang et al. (2015) [287] Toronto (Canada) RANS / YMEE Vegetation, materials (albedo), district comparison, mitigation, urban
form
AT, MRT, ST
62 Cao et al. (2015) [288] Guangzhou (China) Not specified Orientation, thermal comfort, urban design, ventilation, materials
(albedo)
AT, WV
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11
air temperature is relatively straightforward and especially in the last
years, many campaigns are undertaken for measurement data,
although the suitability and availability of these data for scientific use
and especially for validation can be a limitation. The reason is that the
complexity and inherent variability of the meteorological conditions
not only require careful measurement of a large number of parameters
(to be used as boundary conditions in the simulations) but also a very
complete reporting of urban area, measurement set-up, measurement
accuracy, etc., without which a detailed and thorough validation
exercise will not be possible [296,301,306,307]. The sub-category “real
urban areas –with validation”contains 57 studies, 47 of which use air
temperature as one of the validation parameters. The studies belonging
to this sub-category are summarized in Table 4.
Similar to the studies on real urban areas without validation, all the
studies in this sub-category employed the RANS equations. However,
different from the studies without validation, studies in this sub-
category have radiation as a relatively popular keyword (16 of 57
studies). That is mostly because recent studies on human thermal
comfort demonstrated the importance of thermal radiation (e.g. mean
radiant temperature) on thermal comfort levels. Therefore, studies try
to validate their CFD simulation results based on radiation parameters.
The five most commonly used keywords in this sub-category are
thermal comfort/heat stress (29 of 57 studies), vegetation (28 of 57
studies), materials/albedo (19 of 57 studies), case comparison (20 of 57
studies) and radiation (16 of 57 studies). On the other hand, keywords
such as energy budget, economy, optimization, surface heating and
thermal stability do not occur as keywords
7
in any of these 57 studies.
3. Comparative analysis of CFD studies on urban
microclimate
3.1. Urban setting/location investigated
Considering generic urban areas, five studies in this sub-category
[177,191,216,218,223] considered more than one type of urban
setting. The majority of generic studies focused on generically dis-
tributed building blocks (36 of 61 studies, or 59.0%), followed by street
canyons (15 of 61 studies, or 24.6%), open spaces (6 of 61 studies, or
9.8%), urban street canyons (6 of 61 studies, or 9.8%) and courtyards
(3 of 61 studies, or 4.9%).
Studies for generically distributed building blocks are mostly case
comparisons without validation and they focus on the effect of
different urban geometries (e.g. orientation, density) [174,176,
178,189,197,223], vegetation patterns [171,193,199], building mate-
rials [188] and building forms [190,191]. Studies on building blocks
that include validation are in most of the cases targeted at more
fundamental fluid flow or heat transfer aspects [207,210,
214,215,225]. Studies for street canyons and urban street canyons
typically investigate canyon related aspects, such as the effect of
aspect ratio [175,180,194,224] and wind/ventilation [172,182,184,
185,195,196,222].
The majority of the studies on real urban areas are conducted for
locations in mid-latitude climates and in the developed regions of the
world (Fig. 9). Fig. 9 seems to indicate a lack of variety in the study
locations. Although this review comprises 122 studies on real urban
areas, the number of different cities in these studies is only 74 and the
number of countries is only 30. Ranked according to the number of
studies, the top five urban locations are Phoenix (USA) (7 studies),
Hong Kong (China) and Cairo (Egypt) (both 6 studies), Tokyo (Japan)
(5 studies) and Wuhan (China) (4 studies). Similarly, the top five
countries are China (23 studies), Japan (12 studies), USA (11 studies),
Germany (9 studies) and Greece (8 studies).
Table 3 (continued)
# Authors (year) Ref. Location (Country) Equations / Models Keywords Parameters
63 Lobaccaro et al. (2015) [289] Bilbao (Spain) RANS / YMEE Canyon, vegetation, thermal comfort, case comparison, aspect ratio AT, MRT, PET, RH, ST, WV
64 Radhi et al. (2015) [290] Amwaj islands (Bahrain) RANS / RNGKE energy (building), thermal comfort, urban design, vegetation, water body AT, BEC, MRT, PMV, WV
65 Girgis et al. (2015) [291] Cairo (Egypt) RANS / YMEE, STKE turbulent heat fluxes, case comparison, CHTC, thermal comfort, specific
forms (square)
AT, ST
Abbreviations: AKN k-ε[149] (AKNNE); Chen-Kim Extended k-ε(CKEKE) [150]; DeardorffSubgrid-scale [159] (DSGS); Durbin k-ε[151] (DKE); Eddy Diffusivity [101] (ED); Low Reynold Number k-ε[149,152] (LRNKE); Miao E-ε[153]
(MEE); Modified k-ε[131] (MDKE); Realizable k-ε[154] (RKE); RNG k-ε[155] (RNGKE); Smagorinsky-Lilly Subgrid-scale [160]; SST k-ω[156] (SSTKW); Standard k-ε[157] (STKE); Yamada and Mellor E-ε[158] (YMEE). Air change rate (1/
hour) (ACH); Air quality index (-) (AQI); Air temperature (°C) (AT); Building energy consumption (W) (BEC); Convective heat transfer coefficient (W/m
2
K) (CHTC); Dry-bulb temperature (°C) (DBT); Economy (currency) (ECN); Froude number
(-) (Fn); Heat flux (w/m
2
) (HF); Indoor air temperature (°C) (IAT); Mean radiant temperature (°C) (MRT); Physiological equivalent temperature (°C) [161] (PET); Predicted mean vote (-) [162] (PMV); Extended PMV (-) (EPMV) [163]; Pressure
(coefficient) (CP); Pollutant concentration (%) (PC); Pressure distribution (PD); Relative humidity (%) (RH); Richardson number (-) (Ri); Sky view factor (-) (SVF); Solar access index (-) (SAI); Solar radiation (W/m
2
) (SR); Standard effective
temperature (°C) [164] (SET); Statistical Performance Indicators (various, e.g. Correlation coefficient)(SPI); Surface temperature (°C) (ST); Temperature-humidity index (-) (THI); Temperature of equivalent perception (°C) [165] (TEP); Thermal
Sensation Perception [166] (TSP); Turbulence dissipation rate (m
2
/s
3
) (TDR); Turbulent kinetic energy (m
2
/s
2
) (TKE); Universal thermal climate index (°C) [167] (UTCI); Ventilation rate (l/minute) (VR); Water vapor fraction (%) (WVF); Wet
black globe temperature (°C) (WBGT); Wind comfort index (-) (WCI); Wind velocity (m/s) (WV); Wind velocity vectors (WVV).
7
Although these words might be used within the text itself, here, they are not present
in the keyword categories as extracted from the abstract and the list of keywords.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
12
Table 4
Overview of studies in the sub-category “real urban areas –with validation.”.
# Authors (year) Ref. Location (Country) Equations / Models Validation
parameter
Keywords Parameters
1 Takahashi et al. (2004) [308] Several locations in Kyoto City (Japan) RANS / MDKE ST Turbulent heat fluxes, heat transfer, coupling, model
development, diurnal variation
AT, ST
2 Chen et al. (2004) [309] Shenzhen City (China) RANS / MDKE ST Heat transfer, thermal comfort, turbulent heat fluxes,
building form (façade), coupling
AT, MRT, RH, SET, ST, WV
3 Huang et al. (2005) [310] Shinjuku Park Tower, Tokyo (Japan) RANS / STKE AT, WV Coupling, model development, thermal comfort,
pedestrians, heat transfer
AT, RH, SET, WV
4 Emmanuel and Fernando
(2007)
[311] Pettah, Colombo / Sri Lanka and Central Business
District, Phoenix (USA)
RANS / YMEE AT Climate sensitive design, vegetation, urban density,
materials (albedo), urban form
AT, MRT
5 Emmanuel et al. (2007) [312] Colombo (Sri Lanka) RANS / YMEE AT Urban morphology, materials (albedo), vegetation,
thermal comfort, radiation (shading)
AT, MRT, PET, ST
6 Ashie et al. (2007) [313] Central Tokyo, Tokyo (Japan) RANS / STKE AT Wind, model development, building form (height), district
comparison, materials
AT, WV
7 Yamaoka et al. (2008) [314] Mido-Suji Street, Osaka (Japan) RANS / MDKE AT Canyon, water body, building form (height), materials,
vegetation
AT, MRT, SET, WV
8 Priyadarsini et al. (2008) [315] Central Business District, Singapore (Singapore) RANS / STKE AT, WV Building form (façade), materials, mitigation, canyon,
aspect ratio
AT, WV
9 Kakon et al. (2009) [316] Motijheel, Dhanmondi, and Siddeswari, Dhaka
(Bangladesh)
RANS / YMEE AT Canyon, diurnal variation, thermal comfort, urban density,
urban planning
AT, RH, ST, SVF, THI, WV
10 Kakon et al. (2010) [317] Dhanmondi, Dhaka (Bangladesh) RANS / YMEE AT Building form (height), thermal comfort, urban density,
pedestrian, canyon
AT, MRT, RH, ST, SVF, THI,
WV
11 Jee et al. (2010) [318] Suwon (South Korea) RANS / STKE WV Vegetation, materials, coupling, turbulent heat fluxes,
radiation (shading)
AT, ST, WV
12 Krüger et al. (2011) [319] XV de Novembro Street, Curitiba (Brazil) RANS / YMEE WV Thermal comfort, radiation (SVF), urban planning, wind
(flow), urban form
MRT, SVF, WV
13 Yang et al. (2011) [320] Pudong New District, Shanghai (China) RANS / YMEE AT Climatic design, materials (albedo), vegetation, thermal
comfort, case comparison
AT, MRT, PET
14 Gaitani et al. (2011) [321] Messolongiou Square, Athens (Greece) Not specified AT, WV Bioclimatic design, materials (albedo), vegetation,
radiation (shading), case comparison
AT, WV
15 Chow et al. (2011) [322] Arizona State University Campus at Tempe,
Phoenix (USA)
RANS / YMEE AT Material, vegetation, diurnal variation, urban form,
orientation
AT
16 Chen and Ng (2012) [323] The Central Market, Hong Kong (China) RANS / YMEE AT, MRT Thermal comfort, vegetation, urban planning, case
comparison, pedestrians
AT, MRT, PET
17 Zhang et al. (2012) [324] The Hong Kong Polytechnic University Campus,
Hong Kong (China)
RANS / RNGKE,
STKE
PR Ventilation, thermal comfort, orientation, seasonal
variation, urban density
PR, PMV, WV
18 Chow and Brazel (2012) [325] Tempe and West Phoenix, Phoenix (USA) RANS / YMEE AT Vegetation, case comparison, mitigation, sustainability,
thermal comfort
AT, MRT
19 Liu et al. (2012) [326] Downtown Beijing (China) LES / SLSGS AT, WV Coupling, pollutant dispersion, wind, model development,
heat transfer
AT, HF, WV
20 Shahidan et al. (2012) [327] Persiaran Perdana, Putrajaya (Malaysia) RANS / YMEE AT, ST Urban design, mitigation, vegetation, energy (building),
materials (albedo)
AT, BEC, ST
21 Ma et al. (2012) [328] Shenzhen (China) RANS / DKE ST, WV Radiation (solar), pedestrian, model development,
coupling, materials
AT, RH, ST, WV
22 Ng et al. (2012) [329] Tsuen Wan, Hong Kong (China) RANS / YMEE AT Urban density, vegetation, aspect ratio, urban planning,
case comparison
AT
23 Maras et al. (2013) [330] Aachen Central Station, Aachen (Germany) RANS / YMEE PMV Heat stress, vegetation, urban density, specific forms
(squares), case comparison
PMV
24 Yang et al. (2013) [331] Metropolitan Guangzhou / (China) RANS / YMEE AT, HF, ST Radiation (solar), heat transfer, materials (albedo) , heat
fluxes, diurnal variation
AT, HF, RH, ST
25 Carnielo and Zinzi (2013) [332] Prati RANS / YMEE ST Materials (albedo), case comparison, energy (building),
diurnal variation, radiation (reflections)
BEC, ST
Neighborhood, Rome (Italy)
26 Peng and Jim (2013) [333] Several locations in Hong Kong (China) RANS / YMEE AT Vegetation, case comparison, sustainable, diurnal
variation, thermal comfort
AT, PET, PMV
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13
Table 4 (continued)
# Authors (year) Ref. Location (Country) Equations / Models Validation
parameter
Keywords Parameters
27 Müller et al. (2013) [334] Oberhausen (Germany) RANS / YMEE AT, RH Adaptation, thermal comfort, vegetation, radiation
(shading), water body
AT, RH, PET
28 Goldberg et al. (2013) [335] Friedrichstadt and Altstadt, Dresden (Germany) RANS / YMEE AT, SR Urban planning, thermal comfort, pedestrian, district
comparison, case comparison
AT, SR, UTCI
29 Srivanit and Hokao (2013) [336] Honjo Campus of Saga University, Saga (Japan) RANS / YMEE AT, RH, SR, WV Vegetation, pedestrian, case comparison, building form
(roof), diurnal variation
AT, RH, SR, WV
30 Su et al. (2014) [337] HoHai University Campus, Nanjing (China) RANS / YMEE AT Vegetation, coupling, sustainable, urban form, district
comparison
AT
31 Hedquist and Brazel (2014) [338] Central Phoenix, Phoenix (USA) RANS / YMEE AT Thermal comfort, seasonal variation, district comparison,
pedestrian, materials
AT, PMV, ST
32 Middel et al. (2014) [339] North Desert Village, Phoenix (USA) RANS / YMEE AT, ST Urban form, urban design, vegetation, case comparison,
radiation (shading)
AT, ST
33 Maggiotto et al. (2014) [340] Several locations in Lecce (Italy) RANS / YMEE AT Coupling, diurnal variation, model development, statistical
analysis, heat transfer
AT, RH, SPI
34 Zoras et al. (2014) [341] Central Florina, Florina (Greece) Not specified AT, ST, WV Bioclimatic design, materials, thermal comfort, case
comparison, radiation (reflections)
AT, ST, WV
35 Park et al. (2014) [342] Nanaimo, British Columbia (Canada); Changwon
(South Korea)
RANS / YMEE SR Pedestrian, thermal comfort, district comparison,
coupling, urban form
AT, MRT, RH, SR, ST, UTCI,
WV
36 Tang et al. (2014) [343] Shang-gan-tang village (China) RANS / STKE AT Bioclimatic design, urban planning, vegetation,
sustainable, water body
AT, WV
37 Minella et al. (2014) [344] Railway Station, Geneva (Switzerland) RANS / YMEE AT, MRT, RH, SR, WV Thermal comfort, vegetation, case comparison, urban
design, urban form
AT, MRT, RH, SPI, SR,
UTCI, WV
38 Skelhorn et al. (2014) [345] Several locations in Manchester (England) RANS / YMEE AT, ST Vegetation, adaptation, climate (change), building form
(height), district comparison
AT, ST
39 Du et al. (2014) [346] Shuangjiang Town, Chongqing (China) RANS / STKE AT, WV Building form, thermal comfort, diurnal variation, wind,
energy (building)
AT, BEC, WV
40 Dimoudi et al. (2014) [347] Center of Serres (Greece) RANS / STKE AT, ST, WV Materials, bioclimatic design, mitigation, case comparison,
radiation (reflections)
AT, ST, WV
41 Acero and Herranz-Pascual
(2015)
[348] Several locations in Bilbao (Spain) RANS / YMEE AT, MRT, WV Thermal comfort, district comparison, diurnal variation,
statistical analysis, urban form
AT, MRT, PET, WV
42 Wang et al. (2015) [349] Assen (Netherlands) RANS / YMEE AT Radiation (shading), vegetation, diurnal variation,
seasonal variation, thermal comfort
AT, PMV, SPI
43 Tominaga et al. (2015) [148] Central Hadano (Japan) RANS / RNGKE AT, RH Water body, case comparison, model development, wind,
diurnal variation
AT, RH, ST, WV
44 Tan et al. (2015) [350] Tsim Sha Tsui and Sham Shui Po, Hong Kong
(China)
RANS / YMEE MRT, ST Vegetation, mitigation, urban density, urban form,
radiation (SVF)
AT, HF, MRT, ST, SVF, WV
45 Salata et al. (2015) [351] Sapienza University, Rome (Italy) RANS / YMEE AT, MRT, RH, SR Thermal comfort, case comparison specific forms
(courtyards), material, mitigation
AT, MRT, RH, SR, WV
46 Emmanuel and Loconsole
(2015)
[352] Several locations in Glasgow (Scotland) RANS / YMEE AT Coupling, vegetation, different districts, adaptation,
climate (future scenarios)
AT, ST
47 Gracik et al. (2015) [353] Penn State Campus, University Park (USA) RANS / RNGKE, RKE AT Urban density, diurnal variation, coupling, energy
(building), wind
AT, BEC, ST
48 Toparlar et al. (2015) [146] Bergpolder Zuid, Rotterdam (Netherlands) RANS / RKE ST Building form, adaptation, thermal comfort, model
development, urban form
AT, ST, WV
49 Janicke et al. (2015) [354] Berlin Institute of Technology, Berlin (Germany) RANS / YMEE AT, MRT, RH vegetation, thermal comfort, case comparison, adaptation,
building form (façade)
AT, MRT, RH
50 Song and Park (2015) [355] Several locations in Changwon City (South Korea) RANS / YMEE AT Vegetation, materials, district comparison, statistical
analysis, radiation (reflections)
AT, SPI, ST
51 Liu et al. (2015) [356] Penn State Campus, University Park (USA) RANS / MDKE AT Building form, energy (building), CHTC, coupling, urban
density
AT, BEC, CHTC, ST
52 Elnabawi et al. (2015) [357] Cairo (Egypt) RANS / YMEE AT, MRT, RH Thermal comfort, climate, urban form, pedestrian,
radiation
AT, MRT, RH, SR
53 Wang and Zacharis (2015) [358] Beijing (China) RANS / YMEE AT Vegetation, mitigation, sustainability, thermal comfort,
urban design
AT, ECN, MRT
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14
3.2. CFD equations/models
Among the investigated 183 studies, 7 of them did not specify the
approximate form of the governing equations used. As for the remain-
ing 176 studies, 169 (96.0%) used only RANS, 5 used only LES (2.8%)
and 2 used both LES and RANS (1.1%) as approximate form of the
governing Navier-Stokes equations.
A microclimatic CFD simulation that couples the temperature and
velocity fields has a higher computational cost and the choice of LES
over RANS evidently will increase this cost. It is expected that the
increased computational cost and the often sufficient accuracy of RANS
[146,304,363,364] are the two main reasons why the vast majority of
studies was performed with RANS, even though LES is generally
considered to be more accurate than RANS [19,29,73,86
,89,292,300,304,363–368]. Apart from RANS and LES, the third
approach often used in CFD simulations, that is Direct Numerical
Simulation (DNS) is not utilized among the microclimate studies
investigated here. Due to its dominant use in the investigated studies,
the remainder of this section will focus on the RANS approach.
Fig. 10 shows the distribution of the use of turbulence models in
these studies. Among the 171 studies using RANS, 6 of them
[201,210,220,291,324,353] have considered two or more turbulence
models.
The most commonly used turbulence model is the Yamada and
Mellor E-ε[158] turbulence model (used in 86 studies, or 49% of total).
Although this turbulence model is not explicitly recommended or
adopted in the CFD best-practice guidelines [19,75–78], its popularity
results from it being the only available turbulence model option in the
microclimate simulation tool ENVI-Met [168]. The second most
popular turbulence model is the standard k-ε[157] model (used in
45 studies –25%). The RNG k-ε[155], Realizable k-ε[154] and
Modified k-ε[131] turbulence models appear to have similar popular-
ity compared with each other. The standard k-εmodel [157] is one of
the most popular turbulence models among CFD studies [76] but as
argued in several publications [75,95,364], some improved models,
such as the realizable k-εmodel [154] can show better performance in
resolving the mean flow field. Fig. 11 illustrates the use of turbulence
models over the years 2010–2015 and the popularity of YMEE (the
turbulence model used in ENVI-met) as mentioned before. The figure
also shows that except for the year 2014, more recent CFD studies are
now using turbulence models other than standard k-εmore often.
3.3. Target parameters
As shown in the foregoing tables, the majority of the studies
considered more than one target parameter. Fig. 12a shows the
distribution of the target parameter categories used and Fig. 12b shows
the distribution of the seven most used target parameters. Most studies
considered temperature related parameters, especially air temperature
for comparison, evaluation or validation purposes. This parameter is
followed by wind velocity, surface temperature and mean radiant
temperature.
Target parameters related to fundamental fluid flow or heat
transfer, such as TKE and CHTC, are mostly used in studies with
generic urban areas. Among the eight studies that considered CHTC as
a target parameter, seven were conducted in generic urban domains
and for the TKE, this ratio is found to be 4/5. This is further evidence
that studies on real urban areas do not typically consider parameters
related to fundamental flow aspects, which can be explained by their
larger complexity or by their difficulty for collecting measurement data.
Economic parameters and statistical performance indicators can be
used in communicating the results from urban microclimate studies to
professionals from other disciplines, such as policy makers, as these
aspects may lead to more generalized conclusions. However, although
these target parameters are considered in some recent CFD urban
microclimate studies, their use is still limited, with only 2 of 183
Table 4 (continued)
# Authors (year) Ref. Location (Country) Equations / Models Validation
parameter
Keywords Parameters
54 Yang et al. (2015) [359] Singapore (Singapore) RANS / YMEE AT, MRT, RH, WV Aspect ratio, thermal comfort, urban form, case
comparison, radiation
AT, MRT, PET, RH, WV
55 Peron et al. (2015) [360] Venice (Italy) RANS / YMEE AT Mitigation, case comparison, materials (albedo),
vegetation, building form
AT
56 Zoras (2015) [361] Arta (Greece) RANS / SSTKW AT, ST, WV Climate sensitive design, adaptation, thermal comfort,
specific forms (open space), case comparison
AT, ST, TSP, WV
57 Duarte et al. (2015) [362] Sao Paulo (Brazil) RANS / YMEE AT, SR Vegetation, urban form, urban density, thermal comfort,
climate
AT, PET, SR, ST, TEP
Abbreviations: AKN k-ε[149] (AKNNE); Chen-Kim Extended k-ε(CKEKE) [150]; DeardorffSubgrid-scale [159] (DSGS); Durbin k-ε[151] (DKE); Eddy Diffusivity [101] (ED); Low Reynold Number k-ε[149,152] (LRNKE); Miao E-ε[153]
(MEE); Modified k-ε[131] (MDKE); Realizable k-ε[154] (RKE); RNG k-ε[155] (RNGKE); Smagorinsky-Lilly Subgrid-scale [160]; SST k-ω[156] (SSTKW); Standard k-ε[157] (STKE); Yamada and Mellor E-ε[158] (YMEE). Air change rate (1/
hour) (ACH); Air quality index (-) (AQI); Air temperature (°C) (AT); Building energy consumption (W) (BEC); Convective heat transfer coefficient (W/m
2
K) (CHTC); Dry-bulb temperature (°C) (DBT); Economy (currency) (ECN); Froude number
(-) (Fn); Heat flux (w/m
2
) (HF); Indoor air temperature (°C) (IAT); Mean radiant temperature (°C) (MRT); Physiological equivalent temperature (°C) [161] (PET); Predicted mean vote (-) [162] (PMV); Extended PMV (-) (EPMV) [163]; Pressure
(coefficient) (CP); Pollutant concentration (%) (PC); Pressure distribution (PD); Relative humidity (%) (RH); Richardson number (-) (Ri); Sky view factor (-) (SVF); Solar access index (-) (SAI); Solar radiation (W/m
2
) (SR); Standard effective
temperature (°C) [164] (SET); Statistical Performance Indicators (various, e.g. Correlation coefficient)(SPI); Surface temperature (°C) (ST); Temperature-humidity index (-) (THI); Temperature of equivalent perception (°C) [165] (TEP); Thermal
Sensation Perception [166] (TSP); Turbulence dissipation rate (m
2
/s
3
) (TDR); Turbulent kinetic energy (m
2
/s
2
) (TKE); Universal thermal climate index (°C) [167] (UTCI); Ventilation rate (l/minute) (VR); Water vapor fraction (%) (WVF); Wet
black globe temperature (°C) (WBGT); Wind comfort index (-) (WCI); Wind velocity (m/s) (WV); Wind velocity vectors (WVV).
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
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studies using economical parameters and 4 of 183 using statistical
performance indicators.
As CFD has demonstrated its capability for involving multiple
scales, there is an increasing interest in linking the results from
microclimate analysis with building scale aspects, such as building
energy consumption and indoor air temperature. Even though these
parameters are not yet considered quite often (BEC 16 of 183 studies
and IAT 1 of 183 studies), while the oldest study specifying one of these
parameters is only from the year 2011 [182], 11 of these studies are
from the last two years, demonstrating the increasing interest.
3.4. Keywords
As described in Section 2, keywords are selected either directly from
the provided keywords list or from the titles and abstracts of
investigated papers. Keywords with similar or interchangeable use
were grouped in 37 keyword categories and five keywords per study
were identified, as listed in Tables 1–4. According to this procedure,
the most used three keywords in CFD urban microclimate studies are:
vegetation (90 studies), case comparison (78 studies) and thermal
comfort/heat stress (77 studies).
Apart from the number of studies, in this paper, we suggest and
apply additional metrics for documenting the annual use of the
keywords. One of them is the first year a keyword is introduced in
CFD urban microclimate studies (First Year Index = FYI). A second
metric called “Weighted Year Index”(WYI) is defined as the weighted
average year of a particular keyword’s usage:
Fig. 9. Distribution of the locations of the CFD microclimate studies focusing on real urban areas. Every orange dot represents one study.
Fig. 10. Distribution of the turbulence models in studies with RANS simulations.
Abbreviations: AKN k-ε[369] (AKNKE); Chen-Kim Extended k-ε(CKEKE) [150];
Durbin k-ε[151] (DKE); Eddy Diffusivity [101] (ED); Low Reynold Number k-ε
[149,152] (LRNKE); Miao E-ε[153] (MEE); Modified k-ε[131] (MDKE); Realizable k-
ε[154] (RKE); RNG k-ε[155] (RNGKE); SST k-ω[156] (SSTKW); Standard k-ε[157]
(STKE); Yamada and Mellor E-ε[158] (YMEE).
Fig. 11. Percentage distribution of the turbulence models used in the last 6 years for
closure in RANS studies. ‘Remaining turbulence models’include AKNKE, CKEKE, DKE,
ED, LRNKE, MDKE, MEE, RKE, RNGKE and SSTKW.
Fig. 12. Distribution of the studies based on the target parameters considered. a) Target
parameter categories; b) Seven most used target parameters.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
16
W
YI
yk
k
=
∑*
∑
y
y
y
y
y
y
n
n
0
0(1)
with ythe year, y
0
the year of the earliest study, y
n
the year of the latest
study (as investigated in this paper), and k
y
is the number of times a
keyword is used in the y
th
year. This metric indicates the year associated
with a keyword’s average use. A lower WYI means that the use of a
particular keyword is mainly situated in earlier years, while a higher WYI
means that the keyword use is mainly situated in recent years.
According to the analysis, of all 37 keywords, the keyword with the
highest FYI and WYI is “statistical analysis”(FYI = 2014, WYI =
2014.8), while that with the lowest FYI is “wind / flow”(FYI = 1998)
8
and that with the lowest WYI is “energy budget”(WYI = 2000). The
annual use of these keywords along with that of the most common
keyword (vegetation) is illustrated in Fig. 13.
A more comprehensive view of the relationship between the
historical use of keywords and the associated number of studies, or
between the FYI and the WYI and number of studies, is given in
Fig. 14. This chart graphically demonstrates the number of times each
keyword is used (by size of the circle), whether a keyword is relatively
new or old (indicated by FYI on horizontal axis) and whether a keyword
is used more often in earlier or more recent years (indicated by the WYI
on the vertical axis). The average overall metrics for the ensemble of all
keywords are: average number a keyword is used: 24.7, average FYI:
2004.3 and average WYI: 2011.9. The latter two numbers define the
origin of the coordinate system in Fig. 14.
Fig. 14 shows that the keyword use in CFD studies of urban
microclimate has transitioned from keywords related to model devel-
opment (heat energy budget, turbulent heat fluxes, model develop-
ment) to keywords related to urban scale adaptation measures (e.g.
adaptation, climate sensitive design) and energy (e.g. building energy,
sustainable). Highly used keywords with large circles in Fig. 14, such as
Fig. 13. The annual use of four of the a) Statistical analysis (keyword with the highest WYI and FYI), b) wind / flow (keyword with the lowest FYI), c) Energy budget (keyword with the
lowest WYI) and d) vegetation (the most common keyword).
8
As every study has five keywords, and the earliest study is from the year 1998, the
keyword wind / flow shares the lowest FYI = 1998 along with other keywords from the
same study [168], namely model development, vegetation, heat transfer and urban
form / morphology.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
17
vegetation, case comparison, thermal comfort, materials/albedo and
urban design/planning are keywords which are used since the early
years of this research field and are still very commonly employed.
4. Discussion and future perspectives
4.1. Validation studies
According to the American Institute of Aeronautics and
Astronautics (AIAA), validation is “the process of determining the
degree to which a model is an accurate representation of the real world
from the perspective of the intended uses of the model”[370].
According to several CFD best practice guidelines [19,75–78,292],
CFD simulations should be evaluated critically and the results should
only be considered reliable after comparing with some measurement
data. If measurement data are hard to obtain for a specific case, sub-
configuration validation can be performed for the intended use of the
model [19]. One example of a sub-configuration validation is the study
by Gromke et al. [275] where the authors used a vegetation model
which is validated with measurement data in separate CFD simulations
and then used the same model in a real urban area. The advantage of
sub-configuration validation can be relevant for complex studies with
different physical models, as the classical validation of the whole
simulation setup can be difficult to conduct due to the uncertainty in
parameters.
According to this review, the majority of the CFD urban micro-
climate studies (105 of 183 studies) are conducted without validation.
The percentage share of the studies without validation seems to have
remained rather stable in the last years, as demonstrated in Fig. 15.
However, it is imperative that CFD urban microclimate studies include
validation much more often to ensure the desired reliability and
predictive capability.
The most common reason for the absence of validation in CFD
microclimate studies might be the lack of relevant and well-documen-
ted measurement data. Measurement campaigns on urban areas can
have some challenges such as logistic difficulties, data quality issues
(e.g. ventilated vs non-ventilated temperature measurements) and
problems with spatial representativeness.
Although these challenges remain, difficulties in obtaining relevant
data can be overcome in a much better-connected World. Internet
resources can be a good alternative for measurement data as large
datasets are now within reach. For instance, surface temperature data
for various locations around the world can now be obtained from the
National Oceanic and Atmospheric Administration’s (NOAA) satellite
imagery dataset. In one of the plenary session presentations during the
9th International Conference on Urban Climate (ICUC9), Chapman
[371] mentioned the possibility of using low-cost air temperature
sensors with WiFi network, distributed vastly in urban areas. Such a
network could have the potential of becoming a part of Internet of
Things [372,373] and consequently could provide a large amount of
measurement data for microclimate researchers. In the future, re-
searchers can investigate such resources carefully to find relevant
measurement datasets to validate their CFD simulation results.
4.2. Urban locations
Fig. 9 illustrated the limited variety of studied urban locations. In
the article entitled Urban Climatology: History, status and prospects,
Mills [17] identifies one of the major challenges in the urban
climatology field as: “acquiring information on the climates of cities
in less prosperous regions, which are growing rapidly and many of
which have tropical climates in which there have been few studies”.
Although this statement was made referring to urban climatology
Fig. 14. Chart of historical keyword analysis. The X-axis location of a keyword is determined by the first year a keyword is used (FYI); the Y-axis location is determined by the weighted
year index (WYI), and the amount of studies with each keyword is indicated by the size of the respective orange circle.
Fig. 15. Distribution of the studies with and without validation for the last five years.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
18
studies in general, CFD urban microclimate studies are following a
similar route. According to the UN World Urbanization Prospects [2],
most of the urbanization expected to occur in the 21st Century will take
place in the developing regions of the World. Therefore, in the future,
more studies are needed focusing on urban areas in these regions,
especially on the ones with high/increasing populations (e.g. African
continent, India, Latin America).
Another observation in the location analysis is the relation between
the study location and the physical location of the respective research
groups. Mostly, research groups with expertise in CFD urban micro-
climate studies investigate the city in which they are located or to which
they are closely situated. In the future, researchers should aim at
expanding the CFD knowledge to various parts of the World and should
not limit themselves to their own vicinity.
Most of the reviewed papers focus on the negative consequences of
the UHI effect (e.g. on heat stress, energy demand). Therefore,
adaptation/mitigation measures aiming mostly at temperature reduc-
tions are proposed and scientifically tested for implementation in the
various parts of the World. However, the UHI effect may not
necessarily always cause negative consequences. The numerical study
by Hirano and Fujita [11], focusing on the climate and building stock of
Tokyo has shown that for residential buildings, the UHI effect can have
a net positive effect on the building energy demand on a yearly basis. It
is safe to assume that if a city is located in colder climate zones, the
UHI might actually be beneficial. Future CFD studies can investigate in
more detail the consequences of the UHI effect in the colder parts of
the World.
Furthermore, the reviewed studies are mostly conducted for cities
in the mid-latitudes and cities near the arctic circles are not considered
often. Among the reviewed studies, the urban area closest to the Arctic
or Antarctic Circle, was Glasgow [352]. Some higher latitude cities,
such as Oslo, Stockholm or Moscow can be considered in future CFD
urban microclimate studies and new information can be gained. Note
that the lack of urban planning and urban microclimate studies in the
arctic regions is mentioned in a recent study by Ebrahimabadi et al.
[374]. Similarly, (sub)tropical regions are not often considered in CFD
microclimate studies. According to Roth [375], among all the urban
climate studies, studies on sub(tropical) regions constitute less than
20% and according to the present review, this ratio is 8% (15 of 183
studies).
4.3. CFD equations/models
Almost half of the investigated studies used the ENVI-Met [168]
software. This software combines several physical phenomena (e.g.
fluid flow, heat transfer, mass transfer, vegetation interactions) for
urban microclimate analysis. Limited modeling options in the software,
such as the availability of only one turbulence model (Yamada and
Mellor E-ε[158]), the limited options for grid generation and the lack
of information about wall functions, can be considered as drawbacks.
Such limitations and their possible implications on the results are
mentioned in several studies which were included in this review paper
[175,312,319,333,339,349].
After the Yamada and Mellor E-ε[158], the second most
commonly used turbulence model is the standard k-εturbulence
model [157]. Over the years, the standard k-εturbulence model has
been used in many CFD studies but it is well-known that with
this turbulence model, the production of kinetic energy near the
frontal corners of buildings is overestimated and that the turbulent
kinetic energy in wake regions is underestimated (e.g. [76,376]).
Various CFD best practice guidelines [19,76,77] referred to this
problem and advised the use of other turbulence models, such as
realizable k-ε[154]. Among the reviewed studies, the standard k-ε
turbulence model [157] is used many times (45 of 183 studies) but
turbulence models such as realizable k-ε[154] (usedin11of183
studies) or RNG k-ε[155] (used in 11 of 183 studies) are gaining
popularity. Among the 22 studies using one of these two turbulence
models (realizable and RNG k-ε),17arepublishedinthelastthree
years.
In many CFD review papers and best practice guidelines on urban
flow fields, LES is reported as superior to RANS simulations in terms of
results accuracy compared with measurements [19,29,73,86,89,292,
300,304,363–368]. According to this review, the LES approach is not
yet very popular for urban microclimate studies, as only less than 5% of
studies followed this approach. Computational requirements and the
lack of best practice guidelines for the use of LES can be the two main
reasons for this. However, in the near future, rapid developments in the
computational resources can and should result in more studies with
LES approach.
4.4. Target parameters
Most of the CFD urban microclimate studies focused on parameters
related to temperature, wind flow, thermal comfort and heat transfer.
CFD has repeatedly demonstrated its predictive capability in validation
studies focusing on different parameters. The three most commonly
used parameters are air temperature (151 of 183 studies, 82.5%), wind
velocity (90 of 183 studies, 49.2%) and surface temperature (64 of 183
studies, 35.0%).
Measurement data for air temperatures is relatively easier to obtain
compared with surface temperatures and thus are used more often. The
accurate prediction of surface temperatures can be harder as surface
heat transfer modeling requires appropriate near-wall modeling.
Therefore, validation of surface temperatures can be a more vigorous
test for the predictive capability of CFD simulations.
Some past studies used the percentage difference in predicted and
measured temperatures for evaluating the simulation performance.
Such comparisons can be improved in the future, as the outcome of
percentage difference in temperatures would be different for every
temperature unit. For validation studies, dimensionless parameters
and/or statistical performance measures can be used more frequently
to evaluate the performance of simulation approaches.
CFD and complementary numerical tools can be used to link
climate studies from different scales. For instance, the results from
urban microclimate simulations can be employed as boundary condi-
tions for simulations at building or indoor scale. This is necessary to
accurately assess the effect of urban microclimate on building energy
demand and/or indoor ventilation. This review showed that target
parameters such as building energy consumption and ventilation rate
are not very common; though parameters related to building energy
consumption have an increasing popularity. Future studies can focus
on these parameters, which can create a deterministic link between
urban design and building design [377].
The relevance of urban climatology for urban designers and policy
makers is mentioned in several review studies [17,378–380].Policy
makers and/or professionals responsible for public resources (e.g.
local municipalities) are interested in the economic consequences of
planned adaptations or modifications in urban areas [381].Inthe
past, microclimate studies (not with CFD approach) have showed the
positive economic consequences of adaptation measures on key issues
such as energy demand, thermal comfort and human productivity.
CFD simulations can be coupled with financial models to obtain
deterministic results on the economic aspects of adaptation mea-
sures. So far, the reviewed studies showed that the economic aspects
are almost never considered with a relevant target parameter. The
missing link between fundamental microclimate studies and the
economic consequences can be an important aspect in the near
future.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
19
4.5. Keywords
The keywords can be classified in four categories based on the
quadrants in Fig. 14.
Keywords with low FYI and low WYI, such as heat transfer, model
development, energy budget and turbulent heat fluxes were mainly
used in the earlier studies and are not used very often in the later
studies. With more validated CFD approaches, research efforts have
shifted from the development of new models to case studies.
Keywords with low FYI and high WYI, such as materials/albedo and
vegetation are used in CFD urban microclimate studies since the early
2000s and they are still used very often. Vegetation is the most
common keyword as many studies investigated the effect of street
trees, urban parks and green roofs/facades since the early years of this
research field and still, similar studies are conducted for different cities.
Keywords with high FYI and high WYI are not used very often yet,
because they are very recent. Among the new keywords with high WYI
values, adaptation/mitigation and climate sensitive design are gaining
popularity not only among the CFD studies but also among studies with
different methodologies [20,382]. New keywords such as “sustainable”
and “climate”demonstrate the effect of the popularized sustainable
development challenge on this research field.
Keywords with high FYI but with low WYI values, such as model
coupling, thermal stability and optimization refer to studies, which are
recent but are not very common. Typically, studies with these keywords
are very specialized. For instance, the “optimization”keyword belongs
to this group and the parametric optimization of CFD results would
require many simulations, with a dedicated campaign, which may have
affected its FYI and WYI values.
In the future, many of the new keywords are expected to continue to
increase their popularity. Among these new keywords, statistical
analysis should play an important role in testing new models and
simulation cases more effectively. The economic aspects of adaptation/
mitigation strategies should be evaluated with new methods, possibly
by linking multiple scales. Even though the effects of vegetation and
materials on urban microclimate seem to be well understood, it might
continue being investigated with new case studies and with studies
performed on generic urban areas to provide general conclusions (or
guidelines) for professionals from other disciplines. CFD is a useful tool
for deterministic judgement and researchers in these other disciplines
should benefit from this.
4.6. Limitations of the review
Given the large scope and large number of publications in this topic,
some studies had to be omitted from this review. As denoted in Section
2, this review identified CFD microscale studies, which couple velocity
field with temperature field on 3D computational domains. CFD studies
on urban microscale investigating pedestrian wind comfort
[82,383,384] and thermal comfort [385], are not investigated in this
review if their focus was only on the modeling of velocity field, without
the coupling with temperature. In addition, this review paper focused
only on journal papers which are prepared in English language but
surely, valuable studies on CFD urban microclimate analysis have been
published in the past as conference papers (e.g. [386–388]) or in other
languages (e.g. [389,390]).
5. Conclusions
Considering the trend towards urbanization and the challenge of
sustainable habitats, studies on urban microclimate will continue to gain
popularity in the 21st century. Numerical methods to analyze urban
microclimate are essential tools for engineers, architects, urban planners
and policy makers to compare urban design alternatives and to manifest
guidelines. CFD is one of these numerical tools, which is frequently used
in the urban climate at various spatial scales. CFD studies on the
meteorological microscale, where typical spatial distances are less than
2 km, are gaining popularity due to their advantages such as the explicit
modeling of urban and building geometry and resolving the flow field
with high spatial resolution. Though gaining popularity, to the best of
our knowledge, there has been no review paper yet that is dedicated to
CFD studies on urban microclimate.
This paper presented a systematic review and analysis of the CFD
urban microclimate studies that were published in peer-reviewed
international journals from 1998 until the end of 2015. A total of
183 studies were identified which include 3D computational domains
and couple the velocity and temperature fields. The studies were
categorized based on the types of urban areas investigated, real or
generic, and on the methodology followed, studies with and without
validation.
For every sub-category, the studies were listed in tabular form
based on their publication year, location, CFD equations/models,
validation parameter (if any), keywords and target parameters. A
comparative analysis was provided based on the findings.
From this review paper, the following conclusions can be made:
–CFD results should be subjected to detailed validation in the future.
This review documented that 58% of the existing CFD microclimate
studies have not considered any validation. In order to improve the
reliability and the predictive capability of CFD simulations, future
studies should collect and employ relevant measurement data to
support simulation results.
–Even though CFD urban microclimate studies are gaining popular-
ity, the urban locations investigated do not have a large variety.
Among the 122 studies focusing on real urban areas, only 74 cities
from 30 countries have been the subject of CFD simulations.
Especially for the cities located in the developing regions and in
the tropics, very few studies can be identified. CFD urban micro-
climate knowledge needs to be expanded to the developing regions
of the World.
–The review documented that 96% of the studies considered RANS
simulations only, even though LES has the potential to be more
accurate in predicting flow field.
–Among the RANS simulations, 74% used either Yamada-Mellor E-ε
or standard k-εturbulence models. The choice of turbulence models
can be limited with the availability of the software packages.
However, detailed validation studies and the resulting increased
accuracy and reliability of CFD studies will benefit from the
availability and/or use of more turbulence models.
–The results from validation studies can be communicated using
statistical performance indicators, which were not used in the past
studies often. Moreover, the target parameters can be selected from
other spatial scales (e.g. building energy demand) or from generic
terms (e.g. economy, emissions) for a thorough analysis on the effect
of microclimate and adaptation measures on humans, buildings and
urban infrastructure.
–The themes of CFD urban microclimate studies were documented by
investigating the keywords they used. According to this investiga-
tion, the early CFD urban microclimate studies had been conducted
for model developments and case studies. In the past few years,
more studies about urban scale adaptation measures and thermal
comfort are conducted. Future studies might focus more on sys-
tematic studies with multiple scales (e.g. mesoscale, building scale)
and aspects (e.g. economical) to transfer the gained knowledge from
urban climatology to routine building and urban design guidelines.
These recommendations are in fact against the current trends
observed among CFD urban microclimate studies. Future studies on
this topic can consider these recommendations to push the boundaries
of this field for acquiring new knowledge on urban climatology.
Y. Toparlar et al. Renewable and Sustainable Energy Reviews xxx (xxxx) xxx–xxx
20
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