An article on naturally ventilated teaching spaces with windcathers in secondary schools where the site is constrained by noise and air pollution.

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An investigation of naturally ventilated teaching spaces with windcathers in
secondary school where site is constrained by noise and air pollution
Article in Building Simulation · October 2014
DOI: 10.1007/s12273-014-0176-5
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2. BUILD SIMUL (2014) 7: 547–561
DOI 10.1007/s12273-014-0176-5
The original document is available with ‘www.springerlink.com’. Accepted on 13/4/2014
An investigation of naturally ventilated teaching spaces
with windcathers in secondary school where site is
constrained by noise and air pollution
Tanvir Morshed
Department of Architecture, Stamford University Bangladesh, 2/21, Lalmatia, Block-B, Dhaka 1207, Bangladesh
Abstract
Built environment consumes the bulk of the UK’s fossil fuel. Schools account for 15% of the public
sector’s carbon emissions. Energy efficient building design can play a vital role in achieving the
national carbon emission reduction target of 80% by 2050. Natural and mixed mode ventilation is
at the forefront of suggested energy efficient strategies for reducing carbon emissions from
schools while maintaining good indoor air quality and thermal comfort. However, it is challenging
to naturally ventilate many urban school buildings through side openings because of high noise
and particulate air pollution. An alternative strategy, such as multi floor operation of windcatchers
was assessed in this research as a sole source of fresh air in teaching spaces. Dynamic thermal
simulation (DTS) and computational fluid dynamics (CFD) simulations assessed the performance
of the adopted natural ventilation (NV) strategy in meeting the approved requirements for fresh
air, indoor air quality (IAQ) and summertime overheating. Simulation results show that it is
challenging to meet approved guidelines on air quality and thermal comfort, only when
windcatchers are employed for ventilation purpose. However, fan assisted ventilation in
conjunction with windcatchers provided satisfactory results. Detailed performance assessments
using CFD seem desirable to validate DTS based findings.
Keywords
school buildings,
natural ventilation,
thermal performance,
indoor air quality,
summertime overheating,
CFD simulation
1 Introduction
UK built environment is one of the major contributors to
anthropogenic changes in climate through its extensive use of
energy (DECC 2010). Therefore, buildings can play a vital
role in meeting the legally binding 80% reduction of CO2
emissions from the 1990 baseline level in the UK (DCSF
2010). Schools account for around 2% of UK greenhouse gas
(GHG) emissions, equivalent to 15% of the country’s public
sector emissions (Mavrogianni and Mumovic 2010). Typical
energy consumptions of a secondary school is 33 kWh/m2
per
year for electricity and 144 kWh/m2
per year for gas against a
benchmark of 25 and 110 kWh/m2
per year, respectively
(Cheshire et al. 2004; Bunn 2008). Energy efficiency
measures such as natural ventilation (NV) strategies are
promoted for achieving carbon emission reduction target from
schools. NV strategies have enabled designers to build low
energy teaching spaces at the recommended
E-mail: tanvir.morshed@gmail.com
level of indoor air quality (IAQ) and thermal comfort
(DCSF 2006). Adequate NV also ensures excellent IAQ for
optimum performance of the pupils which is mostly
characterised by concentrations of CO2 in teaching spaces.
Besides, study shows inadequate ventilations in schools
have also been linked with health problems among students
(Clements-Croome et al. 2008). However, NV with access
fresh air in winter represents energy penalty and air
exchange alone represents 20% to 50% of buildings total
thermal load (DCSF 2006).
Building Bulletin 101 (BB 101) sets out guidelines for
“ventilation of school buildings” for UK (DCSF 2006). It
recommends minimum level of ventilation is 3 L/s per person
and a minimum daily average of 5 L/s per person purpose-
provided ventilation rate in all teaching and learning spaces.
The guide also states that the ventilation solution also needs to
be able to achieve a minimum of 8 L/s per person at any time.
Furthermore, for IAQ, the maximum concentration

3. 2
of CO2 should not cross 5000 ppm during the occupied
teaching day with an ability to lower the concentration of CO2
to 1000 ppm at any occupied time. According to the guide, the
standards for avoidance of summertime over-heating are: (a)
There should be no more than 120 hours in a year when the
air temperature in the classroom rises above 28℃. (b) The
internal air temperature should be no more than 5℃ above the
external air temperature on average.
(c) The internal air temperature needs to be below 32℃
during occupied hours. Meeting any two of these standards
would meet summertime overheating criteria. On the other
hand, Building Bulletin 93 has set the upper limit for indoor
ambient noise level between 30 and 40 decibel (dB),
depending on the purpose, size and occupancy of teaching and
ancillary areas (Shield and Hopkins 2007). In reality, many of
the urban schools in the UK are located in congested sites and
surrounded by noisy roads with particle based pollutions.
Thus, the conflicting requirements of fresh air supply by
keeping side windows open for ventilation and the need to
maintain a lower ambient noise level in urban school
buildings, pose a dilemma for designers. Therefore, NV
solutions without the aid of the side windows are needed to
meet fresh air and noise level requirement as per standards.
This research employed dynamic thermal simulation (DTS)
and computational fluid dynamics (CFD) simulation programs
to assess the performance of selected NV strategy in meeting
BB101 criteria. DTS examines criteria like IAQ, control
strategy, thermal comfort, etc. and calculates the bulk air
temperature & humidity in a room (IES 2010). Unlike CFD
plots, a DTS result does not adequately provide various spot
measurement of temperature, air velocity, humidity, predicted
mean vote (PMV), predicted percentage of dissatisfied (PPD),
etc. CFD plots more precisely evaluate DTS obtained results.
Besides, CFD nowadays in environ-mental design of buildings
is gaining popularity mainly due to the newer and stringent
health and comfort regulations in built environment (Hong et
al. 2000). The simulation outputs of this research show that it
is difficult to maintain air quality and thermal comfort just by
the selected NV strategy and a fan assisted option improved
the space with fresh air & required comfort.
2 Natural ventilation for UK secondary schools
NV in reality works in the combined effect of wind &
buoyancy and is explored for enhanced air quality of teaching
spaces. Buoyancy uses differences in air temperature and
hence the density between the inside and outside of the
building. Whereas wind striking a building induces a positive
pressure on the windward face and negative pressure on
opposing or leeward faces & in the wake region of the side
faces for inducing airflow in a space. UK is windy and the
mean wind speed for summertime is in excess of 3.5 m/s
(Irving et al. 2005). The effects of the terrain type will or
may affect the apparent wind speed at the building location,
especially in dense urban settings. London is one of the
least wind exposed area in the UK having mean wind speed
of 3.46 m/s (Barrett et al. 2002). Most of the London city is
surrounded by low rise buildings and skyscrapers con-
centrated on certain part of the city. That is why urban
fabric of London poses no or insignificant hindrance to
wind speed and direction. Especially for school buildings
where there are always a big playground and much setback
space from other buildings to play with the design of NV.
Study at the University of Cambridge showed average
building height in London is 13.6 m compared to 51.3 m in
Los Angeles (Ratti et al. 2002). Moreover, London has just
over 1000 buildings that is 12 stories or more and 21
buildings having a height more than 110 m (British
Property Federation 2008). Furthermore, 24% of Greater
London is domestic garden land and 14% of it is vegetated
land (GIGL 2008). Even with all the facts, London is
densely populated and warmer than most places in the UK
due to urban heat island effect (Kolokotroni and Giridharan
2008). Therefore, this research is London based to mimic
the worst-case scenario of summertime urban UK.
2.1 Natural ventilation with windcatchers
Until recently, most of the UK school buildings were air-
conditioned. Given the drive to reduce energy use & related
CO2 emissions and to negate the impact of climate change,
there is an increasing trend to move away from air-conditioned
building to NV option (Cook 1998). Study showed increased
levels of CO2 which are in excess of 4000 ppm led to a
decrement in “power of attention” by approximately 5%
among students in schools (Clements-Croome et al. 2008).
Modern top down ventilation device (see Fig. 1(a) & (b)) like
windcatchers can ventilate spaces while reducing outdoor
noise level of 70 dB to 30 dB (Barrett et al. 2002) where the
figures are after BSRIA: The Illustrated Guide to Ventilation,
Assessment and Design (Pennycook and Bunn 2009).
Windcatchers are installed widely in UK. One leading
manufacturer fitted 70% of their installation in UK schools and
their technical data was employed during this research
(Monodraught 2010). Actually, Windcatcher’s terminal is
divided into four separate segments, which act as either inlet or
outlet ducts depending on the wind direction. The windward
segment(s) provides the supply of air to a space owing to the
pressure of the wind blowing on it. Other segments act as
extracts due to the suction force created by the low-pressure
region. The use of square windcatcher is advantageous instead
round one due to larger leeward negative pressure region
(Parker and Teekaram 2005).

4. 3
Fig. 1 (a) Typical windcatcher, (b) airflow pattern through
windcatcher & room, (c) ground floor plan, (d) first floor plan,
(e) east elevation, (f) transverse section, (g) south elevation and
(h) plan of finally selected windcatcher
2.2 Typical teaching spaces of UK secondary schools
Before adopting windcatchers, the typical scenarios of UK
secondary school buildings were identified and formulated
upon consulting guidelines and case studies (see Fig. 1(c), (d),
(e), (f), (g) & (h)). Study revealed typical secondary school
has Core Teaching Space for Information and Communication
Technology (ICT) classes and as gathering place which is
surrounded by number of 60 m2
(approx.) Typical Teaching
Spaces (Williamson et al. 2006). The orientation, position and
floor to floor height (4 m to aid successful buoyancy driven
ventilation) of the formulated teaching spaces along with
ancillary facilities were based on various books and guidelines
on schools (Shield and Hopkins 2007; Patel et al. 2007; DfES
2003; Littlefield 2008; Skistad 1994). Under-glazing can
make interior spaces dismal and gloomy. Whilst over-glazing
allows excessive solar gain in summer and excessive heat loss
in winter with attendants discomfort (Patel et al. 1999).
Therefore, windows (fixed single sided) in typical teaching
spaces and curtain walls along with rooflights in core teaching
space were optimised considering daylight factor (DF) to save
energy. DF is the
illuminance received at a point on indoors, from a uniformly
illuminated sky, expressed as a percentage of the horizontal
outdoor illuminance from an unobstructed hemisphere of the
same sky. 5% DF is required for optimum visual comfort of
the pupils (BRANZ 2007). For achieving 5% DF, 19 m2
of
glazing was needed for typical teaching space. These glazing
were not for ventilation purpose but to help in control of noise
and street level pollutions. Selection and size of windcatcher
are discussed later, upon discussing key issues related to NV
with windcatcher.
2.3 Natural ventilation of UK schools with windcatchers
An extensive literature review helped in determining the
ventilation rate, the size of the windcatcher and how it
behaves in NV of spaces. The review summarised here also
helped in shaping DTS and CFD simulation models. External
wind speed and direction are important for windcatcher’s
performance (Li and Mak 2007). Buoyancy has limited
positive effect on flow rates at lower wind speed and
negligible effect at higher wind speed (Su et al. 2008). CFD
study also showed that when outdoor wind speed is 1 m/s,
windcatcher barely supplied any fresh air and achieved at least
5.5 times of code required fresh air for the space, at wind
speed of 4.5 m/s (Bradshaw 2010). CFD simulation backed by
experimental study showed, a typical 500 mm2
windcatcher
can supply 100 L/s and can extract 80 L/s of air when
subjected to 3 m/s of wind speed (Khan et al. 2008).
Elmualim’s CFD study showed that when windcatcher’s
dampers or grille are fully opened at 3 m/s of wind speed, air
supply reduced in the experimented room by 20%, compared
to no damper or grille situation. Similarly, 50% for 1 m/s of
wind speed (Elmualim 2006). He also found average loss
coefficients due to dampers or grille is 0.10. Benjamin and
Ray’s semi-empirical method shows loss coefficient for
supply as 4.43+Kfrict and extract as 8.85+Kfrict where
components of windcatchers like top section, inlet, outlet,
duct, grill and damper’s loss coefficient were accounted for
(Jones and Kirby 2009). They did not investigate the effect of
buoyancy, the length of the duct and its performance. The
results of their model were compared with Huges and Ghani’s
CFD study and found that in high indoor tempera-ture & low
wind speed the performance of windcatcher is based on
buoyancy rather wind speed. The compared results vary only
5% for the volume flow rate (Hughes and Ghani 2008).
Elmualim found when temperature difference between indoor
& outdoor is 8℃ the windcatcher system can reduce indoor
air to 6℃, mitigating summertime overheating. For detailed
air movement and velocity related data he favours CFD study
(Elmualim 2006). The study also showed nor-malised airflow
in the square system is much higher than circular system.
Suggestion was given for modification of

5. 4
circular system’s louver design to increase the free area and
resulting performance, while maintaining rigidity & structural
stability (Elmualim and Awbi 2002). Experimental and CFD
studies revealed that louver angle of 35° provides optimum
performance for windcatchers (Hughes and Ghani 2010). Also
performance of windcatcher largely depends on the pressure
coefficients at the openings (Montazeri et al. 2010).
2.4 Selection of size of windcatchers for teaching spaces
Based on Parker and Teekaram’s method, selection of size
of windcatcher requires information like space attributes,
occupancy, the heat gain calculation, ventilation rate and
design wind speed calculations, which are described below
(Parker and Teekaram 2005).
2.4.1 Space attributes, occupancy, heat gain and
ventilation rate
Space attributes, occupancy and related heat gain were
calculated in Table 1. 50% diversity factor was considered as
more and more schools are now using laptops rather personal
computers (PCs) with a cathode ray tube (CRT) monitor
which considerably lower equipment related heat gain. Laptop
emits 70% less heat than typical PC (Knight and Dunn 2003).
The desired indoor temperature and average summertime
London outdoor temperature considered were 25℃ and 20℃
respectively (Bradshaw 2010). According to Chartered
Institution of Building Services Engineers (CIBSE) Guide A
and Guide B allowable infiltration rate for lecture room is 0.25
and 1.0 air change per hour (ACH) respectively (Humphreys
et al. 2006; Henderson et al. 2005). 0.75 ACH or 0.049 m3
/s
of fresh air was considered for all lecture rooms considering
the fact that the upper value would ensure good IAQ and the
highest value would have been too energy intensive. Heat gain
reduction due to infiltration related air
Table 1 Space attributes, occupancy and related heat gain
calculated for typical teaching space
Type Value Remarks/source(s)
Occupants 32 30 pupils, 1 teacher and 1 assistant
Floor area 63 m2
9 m × 7 m
Floor to ceiling height 3.75 m
Room volume 236.25 m3
0.12 kW
Solar gain from south-side glazing
(exposed area, solar irradiance and
shading factors were considered)
Heat gain
0.04 kW Structure
1.76 kW
Humans (55 W/person since most
occupants are children)
1.63 kW
Equipment (5 PCs, 1 printer, 1
projector and luminaries)
change was calculated using Eq. (1) and therefore, final
heat gains in teaching space calculated was 2.4 kW.
Pv = C v NV t
(1)
3600
where Pv = ventilation heat loss (W)
Cv = volumetric heat capacity of air (J/(m3
∙K))
N = air change rate (ACH)
V = volume of the space considered (m3
)
t = temperature difference (℃)
Similarly, the heat gain from lab teaching and core
teaching spaces was 1.96 kW and 5.92 kW respectively. The
air change rate stands at 3.9 ACH, since 8 L/s per person was
the fresh air requirement for the space. Again, the ventilation
rate for dissipation of heat gains in a typical teaching space
calculated by Eq. (2) was 6.85 ACH or 0.45 m3
/s, where Q is
the volume flow rate (m3
/s), q is the net heat gain (kW), ρ is
the density of air (kg/m3
), c is the specific heat capacity of air
(kJ/(kg∙K)) and dt is the temperature difference between the
external and internal temperatures (K).
Q =
q
(2)
ρ ⋅ c ⋅ dt
Amongst 3.9 and 6.85 ACH, the highest was accounted for.
After deduction of infiltration rate for a typical teaching space,
the final required ventilation rate was 0.4 m3
/s or 6.1 ACH.
2.4.2 Design wind speed calculation
In urban/suburban location the design wind speed varies
considerably and CIBSE Guide A provides reliable mean
wind speed equation at 8 m height where the inlets of
windcatchers were considered for this research (see Eq.
(3)) (Humphreys et al. 2006).
Vz = Vref C R ( z )CT (3)
where, Vz is wind speed at height z, Vref is regional wind
speed, CR(z) is roughness coefficient at height z and CT is
topography coefficient. Roughness coefficient at height z
(where z ≥ zmin) were calculated by Eq. (4), where KR is
terrain factor, z0 is roughness length and zmin is the
minimum height. These values are for suburban area with
no significant upwind or downwind area.
CR( z )= KR ln(
𝑧
𝑧 0
) (4)
Therefore, reference regional urban wind speed (Vz)
calculated by Eq. (3) was 2.39 m/s, which was the design
wind speed in this research.
2.4.3 Size of windcatcher
The initial size of the windcatcher considered was 600 mm2
with components like duct length, damper and diffuser will

6. 5
add to the system resistance i.e., “loss coefficient” value1
of
inflows and outflows of the system. Pressure loss calculated
using Eq. (5) at 0 to 5 m/s, where for four meters (4 m) duct,
K is 0.5, damper K is 0.35 and diffuser K is 0.35.
Ptotal = (0.35𝜌𝑣2
2
)damper
+
(0.35𝜌𝑣2
2
)diffuser
+(0.5∗4∗𝜌𝑣2
2
)duct
P = 1241.2Q2
-1.1535Q (6)
P = - 1008.5Q 2
- 8.391Q (7)
Table 2 Flow rates for each segment of 600 mm2
windcatcher
Face Cp Pressure at 2.39 m/s (Pa)
Windward 0.853 2.923
Sides –0.348 –1.193
Leeward –0.116 –4.000
Table 3 Air change rate against wind speed of 1 to 5 m/s for 800
mm2
windcatcher
Volume of Air change rate due to wind speed from
1 to 5 m/s (internal external temperature
Size of the space to
difference is 5℃)
windcatcher be served
(mm2
) (m3
) 1 m/s 2 m/s 3 m/s 4 m/s 5 m/s
800 236 4.4 7.3 10.5 13.8 17.1
Corresponding ventilation
288 490 688 905 1021
rate (L/s)
Fig. 2 (a) System resistance curve of 600 mm2
windcatcher,
(b) theoretical flow rates for each segment and (c) final flow
rate of each segment
1
Loss coefficient value K is the velocity pressure-loss factors, based on
experimental measurement of typical size windcatchers (Parker and
Teekaram 2005).
where ρ is density of air (kg/m3
) and v is velocity of air (m/s).
Volume flow rates were calculated by multiplying the cross
sectional area with velocities. The system resistance curve (see
Fig. 2(a)), was then added to the ventilator performance curve
for producing the system performance curve. Supply and
extract region of the system performance curve were derived
from Eq. (6) and Eq. (7) respectively,
where Q is the ventilation rate.
For pressure coefficients (Cp) of 600 mm2
windcatcher (see
Table 2) “BSRIA Guide: Wind Driven Natural Ventilation
Systems” were consulted and then multiplied by 0.5ρv2 to derive
the pressure for each quadrant of the windcatcher, at
2.39 m/s of design wind speed (Parker and Teekaram 2005).
The theoretical flow rate for windward segment was 0.049
m3
/s and leeward segment was –0.037 m3
/s and –0.067 m3
/s
(see Fig. 2(b)). As the supply flow rates were less than the
extract flow rate, the pressure was adjusted again in the
performance curve to make them equal, by adjusting all
segments of windcatcher with equal pressure in iterative steps
to fulfil the laws of conservation of mass flow (see Fig. 2(c)).
This provided the overall flow rate of 0.061 m3
/s through the
windcatcher and number of windcatcher derived was 6.39 or 7,
since ventilation requirement was 0.4 m3
/s. However, 7
windcatcher for 63 m2
of teaching space is not practical and
most likely would be aesthetically unpleasing despite meeting
the ventilation requirement. Therefore, desired size of the
windcatcher was changed to 800 mm2
and the above
calculation steps repeated. The numeric value of Cp of 800
mm2
windcatcher cannot be found in any printed literature.
Consequently, data from a web published document of a
leading windcatcher manufacturer were consulted as the basis
for calculation for selecting windcatchers of the teaching space
of same size, only the number of pupils were 15 compared to
32 in this research (Monodraught 2010). Table 3 gives an
account of the supply rate for an 800 mm2
windcatcher based
on 1 to 5 m/s of wind speed from the document. It states that
wind speed of 2 to 3 m/s is capable of providing 8.5 ACH for
dissipating 4.8 kW of heat gains. This was in sharp contrast
with Perker and Teekaram’s study of 800 mm2
windcatcher
which according to them is
(5)

7. 6
capable of supplying 180 L/s of air when the wind is blowing
at 2.8 m/s and they did not mention how this data was
gathered. Since this research was about typical market
products and their performance, the manufacturer’s data were
given preference and those data were verified and researched
in multiple universities across the UK. Besides, the selected
manufacturer has already installed more than 7000
windcatchers across the UK with many prestigious institutions
in the list where research on windcatchers was being carried
out for many years. Every product in the UK had to go through
stringent scientific data verification process based on available
knowledge to pass through product’s approval process, for
market availability. The required air change rate to dissipate
2.4 kW of heat was 5.62 ACH where volumetric heat capacity
was 1300 J/(m3
·K) (McMullan 2007). Now, windcatchers
needed to provide (5.62×236.25)/3600 = 0.369 m3
/s or 369 L/s
of fresh air to the typical teaching space for dissipating 2.4 kW
of heat. For worst case scenario of 1 m/s of wind speed, two
800 mm2
windcatchers were required based on supply rate of
the Table 3. Similarly, size and number of the windcatchers for
other teaching spaces were calculated and presented in Table
4.
Table 4 Derived number of 800 mm2
windcatchers for different
teaching spaces
Air change Fresh air
Teaching Area Volume Heat gain rate requirement Number of
spaces (m2
) (m3
) (kW) (ACH) (L/s) windcatchers
Typical 63 236.25 2.40 5.62 379 2
Core 796 4053 5.92 0.81 920 5¥
Lab 96.5 362 1.96 3.0 300 3±
¥
Although calculation showed 4 windcatchers required, 5 were employed as
this space will have higher number of occupants in some occasional instances.
±
Use of 3 windcatcher instead of 2 is justified due to occasional use of
fume cupboard and resulting higher heat gain.
3 Dynamic thermal simulation (DTS) and CFD
simulation
DTS in VE (by Integrated Environmental Solution Ltd.) and
CFD simulation in Phoenics 2009 (by CHAM Ltd.) were
carried out to assess ventilation performance of teaching
spaces with windcatchers. Worst performing DTS spaces were
identified for further CFD analysis with no and 1 to 5 m/s
wind speeds. When space was not thermally comfortable in
CFD plots, alternative design solutions were sought. For DTS
model, construction material and heat gain for spaces along
with MacroFlo annual, weekly and daily opening profile were
defined in both CIBSE Test Reference Year (TRY) and
Design Summer Year (DSY) (see Fig. 3(a)). Attributes for
openings of windcatchers and building fabric U-values (as per
Part L 2006 requirements) are presented in
Fig. 3 (a) DTS model in IES VE, (b) night cooling modulating
profile in summer months in IES VE and (c), (d), (e), (f), (g) &
(h) are CFD model view of the school building
Table 5 and Table 6 respectively. Here, the mentioned ramp
function was employed for occupied hours of the interior
spaces where at 21℃ windows open fully and at 16℃
windows close in full. Besides, if CO2 level reaches 1050 ppm
the windows close fully and if it reaches 1200 ppm the
windows open in full. Not only that with a step function
background cooling was controlled for weekdays where at
16℃ windows close fully and 20℃ windows open in full.
Also, before occupant arrives in the building i.e. at 8:00
o’clock, the interior temperature will not become too cold
with the adopted MacroFlo opening modulating profile
because at 12 am the openings were 10% open and gradually
open to 40% at 8 am (see Fig. 3(b)). Moreover, the openings
remain 40% open up till 9 am when the space is fully
occupied. From 9 am to 3:30 pm the openings can be opened
fully when satisfies the ramp function. From 3:30 pm to 12
pm the openings gradually close. However, the ramp and step
function explained earlier can override this opening mo-
dulating profile when space satisfies the function’s criteria.

8. 7
Table 5 Opening attributes & profiles for windcatchers in IES VE
Input type(s) Value
Louvre (constructed with doors with crack
length of 10%, considering the opening
Opening category
area after 5 years of operation. Crack flow
coefficient value considered was 1.2, for
non-weather stripped external doors with
median value)
Openable area 70% with coefficient of discharge 0.4
Exposure type Sheltered wall of urban area
Opening threshold Based on typical yearly modulating profile
temperature with summer month’s night cooling strategy
Functions
Occupied hours Ramp(ta,16,0,21,1)|gt(co2,1200,1050)
Night cooling Step function gt(ta,18,4)
Infiltration Rest rooms 6.0
(ACH) Other areas 0.75
Table 6 Employed U-value of the building fabric materials in IES
VE
Construction properties Total U-value
Type (composed of) (W/(m2
·K))
External wall
External renderings, insulating
0.2391
materials, concrete and plaster
Internal wall Gypsum plaster board and cavity 1.6598
Ceiling
Synthetic carpet, cast concrete and
1.6216
ceiling tiles
Ground/exposed London clay, cast concrete, mineral
0.2190
floor fiber slab, screed and synthetic carpet
Roof
Aluminum, mineral fiber slab and
0.2172
ceiling tiles
External glazing/ Pilkington 6 mm double glazed window
1.9773
window assembly with air cavity
Rooflight
Pilkington 6 mm double glazed low E
2.1030
and argon
On the other hand, the CFD model was divided into two
independent “Typical” and “Core” teaching space to avoid
lengthy computational time of large model (see Fig. 3(c), (d),
(e), (f) & (g)). The 800 mm2
windcatcher’s quadrants were
divided into 40 small 40 mm2
blockage elements which acted
as dividers. In setting up grid mesh for CFD model a
reasonable amount of grid-independence is necessary to
achieve correct flow fields, temperature plots etc (Cham
2010). A test case was carried out to define the best possible
number of cells or grids for a domain to get reasonably
accurate results and for convergence. Five types of grid
pattern were chosen, first with minimum possible grid or cells
regarded as worst case, second were with reasonable number
of gird or cells, next were Phoenics 2009 generated auto-grid
or cell numbers, the fourth case was slightly refined form of
auto-grid cells and finally a more refined grid or cell numbers.
A 500 iterations simulation with these five grid or cell
patterns were carried out and spot
Fig. 4 (a) Plot of cell numbers against temperature and immersol
solid temperature, (b) plot cell number against ACH, (c) top cell
setup view, (d) side 1 view, (e) side 2 view and (f) detailed cell or
grid distribution at plane probe location of the typical teaching
space. (g) & (h) are CFD Models of windcatcher’s inlet and outlet
where wind is blowing at 0° & 45° respectively
temperature, immersol solid temperature and ACH were
plotted. The plot showed that after 5 million cells the results
were stable and this value was adopted for all later simulations
(see Fig. 4(a) & (b)). In the area around “interest zone” or
“openings”, more grids were arranged than other area to
achieve flow simulation accuracy and to save computational
time. Steady state grid setup values of 1.000E–3 and 0.999000
for the minimum and maximum were employed respectively.
The partial solid treatment was kept “on”, owing to having
angled plate object at the bottom of the windcatcher duct in
the ground floor. The program automatically calculated

9. 8
the minimum and maximum cell factor for the adopted
manual mesh settings. The value of power ratio used was
1.9, which resulted in denser grid where intended (see Fig.
4(c), (d), (e) & (f)).
When the wind is blowing 0° towards the windcatcher,
there is only one inlet (supply quadrant) & three outlets
(extract/outlet quadrant) and both inlet & outlet were defined
as “opening” (Fig. 4(g) & (h)). For selecting con-vergence
related iteration number, an analysis was carried out with
typical teaching space for 500, 1000, 3000, 5000 and 10 000
iterations. Based on the numerical and graphical output, less
computationally intensive 1000 iterations were selected which
has given similar results to 10 000 iterations (see Fig. 5(a), (b)
& (c)). Boundary conditions were based on experimental and
CFD studies on windcatcher by researchers and from
manufacturer’s product specification. As described in Table 3
earlier, manufacturer’s windcatcher can deliver 288 L/s of air
at 1 m/s of wind speed. Therefore, inlet boundary condition
was set to 0.288 m3
/s of mass flow rate for 1 m/s of wind
speed. Similarly, 0.490 m3
/s of mass flow rate for 2 m/s and
so on.
At no wind situation all quadrants were set as opening
with “deduced” velocity setup for x, y and z direction; means
buoyancy will govern the airflow of the domain. As per Jones
and Kirby’s study, loss coefficient K was 1.5 for flow into the
room, 1.32 for flow out of the room and for the long duct it
was considered negligible (Jones and Kirby 2009). Turbulence
models K–ε was employed to predict pressure, velocity and
temperature in reasonable accuracy for airflow (Li and Mak
2007). In NV simulation “Boussinesq Approximation” was
adopted for buoyancy, which assumes the density to be
constant, except in the source term of the momentum equation
where the “temperature difference” was employed to represent
both wind and buoyancy effect (Su et al. 2008). Buoyancy
effect on turbulence intensity was also turned on with relax
value set as auto. Other boundary conditions were temperature
based “energy equation” and radiation “heat transfer equation”
(as immersol) which provides an economically-realisable
approximation to the precise mathematical representation of
radiative transfer (Cham 2009). For the stable region or value
of turbulence intensity, simulations were carried out with
value of 1% to 10%. Parameters used were spot value
temperature, immersol solid temperature, maximum domain
temperature and minimum domain temperature. From
simulation plots, 5% intensity showed stable regions for the
investigated parameters and this value was used for all later
simulation run (see Fig. 5(d) & (e)). Similarly, for accuracy of
post processor output & to manage the computational time of
simulation runs, 1000 iterations were chosen, instead of higher
numbers. The selection was based on an analysis, in which
iteration numbers were varied and the results were plotted to
ascertain
Fig. 5 (a) Temperature plot along seated height (1.5 m) for the
teaching space across room length in different iterations. (b) and
(c) are the temperature stratification plot at 1000 and 10 000
iterations respectively, (d) turbulence intensity plot between spot
value (temperature) and immersol solid temperature and (e)
turbulence intensity plot against maximum and minimum
temperature
the degree of variation (see Fig. 6(a)). A maximum of 4%
variation of results witnessed between 1000 and 10000
iterations. The result also supports earlier findings presented
above in Fig. 5(a). A simulation run of 5000 and 10 000
iterations also showed progression of convergence in higher
iterations for the typical teaching space (see Fig. 6(b) & (c)).
These plots also showed that for getting a converged solution
the model needs to be run for much higher iterations which
would have taken many days of simulation run if not

10. 9
Fig. 6 (a) Percentage of shift in temperature of results between 1000
and 10 000 iterations. (b) & (c) are convergence related data at 5000
and 10 000 iterations respectively for typical teaching space
months, considering the volume of data/results needed to
come to a solution. Typical PC could not have handled such
long term calculations for simulation runs and very high-end
PC is needed for such endeavour. That is why for final
calculations, 1000 iterations were chosen to achieve a fairly
acceptable convergence without causing calculation related
delays for large number of simulation runs. Besides, the
ambient temperature was set to 24℃, based on design dry
bulb temperature and worst summertime overheating
temperature higher than London mean summertime
temperature (Humphreys et al. 2006; Barrett et al. 2002).
3.1 DTS simulation results
Results from DTS in Table 7 for both TRY and DSY
weather file, show no teaching space was exceeding 120
hours of 28℃. Also the temperature never reached above
32℃, meeting BB101 summertime overheating criteria.
Air quality performance in Table 8 shows that at no point
of time the concentration of CO2 in the teaching spaces
exceeded 1860 ppm meeting BB101 criteria. However,
from Table 9 it is seen that ventilation requirement did not
adequately meet BB101 criteria, since for typical and core
teaching space the values are 379 and 920 L/s respectively
(see Table 4). Although DTS results for temperature and
CO2 were meeting BB101 criteria, CFD analysis will
reveal a more realistic scenario whether a space that to
some extent stands the DTS calculated results in meeting
BB101 criteria can be the same in fluid dynamic plots.
Table 7 The number of hours each teaching spaces reaches above
28℃
Ground
South facing North facing
Core
teaching
GD 01 GD 02 GD 03 GD 04 GD 05 GD 06
floor space
TRY 25 24 24 28 0 14 3
DSY 71 71 71 80 0 59 7
South facing North facing
Core
teaching
First floor 1st 01 1st 02 1st 03 1st 04 1st 05 1st 06 space
TRY 25 25 25 27 6 9 2
DSY 62 63 64 63 13 19 3
Table 8 Concentration of CO2 (ppm) at different teaching spaces
(ground floor)
Core
teaching
Ground floor GD 01 GD 02 GD 03 GD 04 GD 05 GD 06 space
Min 360 360 360 360 360 360 360
TRY Max 1799 1812 1808 1806 874 1789 487
Mean 721 722 722 721 499 720 383
Min 360 360 360 360 360 360 360
DSY Max 1838 1815 1819 1860 914 1813 475
Mean 740 742 742 740 505 749 384
Table 9 Ventilation rate (L/s) of inspected rooms
Room No. GD 02 GD 03 GD 04 1st 01 1 st 03 1 st 04
Volume Max 583.9 598.0 603.0 2580.2 2580.2 2580.2
flow in Mean 77.1 77.1 76.8 130.3 130.6 130.3

11. 10
3.2 CFD simulation results
Temperature stratification, PMV and PPD plots at seated
height are considered as the main determinant factor for
acceptable comfort level for successful NV of spaces. 4' 6"
is the seated height of adult humans and 4' 0" for child
(Chiara and Crosbie 2001). Here, the comfortable air
temperature stratification was based on American Society
of Heating, Refrigerating and Air Conditioning Engineers
(ASHRAE) guideline of room air temperature of summer
month for classrooms, which is from 23℃ to 26℃
(Bradshaw 2010). It is also known that humans are tolerant
of a wide range of temperatures in naturally ventilated
space (de Dear and Brager 1998). For this reason and for
having computational error counted in this research it was
assumed that if CFD plots of a space is well below 28℃ up
to 5' 0" height instead of 4' 6" , the space fulfils one of the
criteria of comfort condition. Same was the height of PMV
and PPD plots where PMV is the average comfort vote,
using a seven-point thermal sensation scale from cold (–3)
to hot (+3) with the ideal value zero. Comfort zone (–
0.5<PMV<+0.5) of PMV is based on six factors like air
temperature, mean radiant temperature, metabolic rate,
clothing insulation, air speed and humidity. Whereas, PPD
is a quantitative prediction of thermally dissatisfied people
in a space who vote on thermal sensation scale like that of
PMV for dissatisfaction and <10% are considered
thermally acceptable in PPD (Humphreys et al. 2006).
3.2.1 CFD simulation results for typical teaching space
Now the temperature contour plot of the teaching space at no
wind speed showed at seated height, ground floor temperature
was in the range of 23℃ to 26℃ while the first floor was as
high as 41℃ (see Fig. 7(a)). This was because the ground
floor was having the benefit of stack effect via long ducts,
which were less in the first floor due to the short duct length.
In Fig. 7(c) & (d), the pressure and velocity vector plots
showed the airflow in the ground floor was much higher than
that in the first floor confirming the temperature plot in Fig.
7(a). Similarly, PMV plot confirms the temperature, pressure
and velocity plots (see Fig. 7(b)). On the other hand, at 1 m/s
wind speed at both ground and first floor was having a
temperature in the range of 26℃ to 28℃ at seated height (see
Fig. 7(e) & (f)). Here, wind rather buoyancy was governing
the temperature distribution in the space. For 2 to 5 m/s of
wind speed, the ground floor never entirely met comfort
condition. It is because, at high wind speed the air entering the
ground floor had enough velocity for not distributing outdoor
cold air just below the inlets and those
Fig. 7 CFD plots: (a) temperature contour plot through west side
windcatchers at no wind, (b) PMV plot through east side
windcatchers at no wind, (c) pressure plot through west side
windcatchers at no wind, (d) velocity vector plot through west
side windcatchers at no wind, (e) temperature contour plot
through at west side windcatchers 1m/s of wind speed and (f)
temperature contour plot through east side windcatchers 1 m/s of
wind speed
regions stayed warm even though the average internal tem-
perature was within comfortable range (see Fig. 8(a), (b),
(c) & (d)). A solution with low energy fan assisted ventilation
was proposed later to solve the mixing of different strata of air
at different floors and in varying wind speed.
3.2.2 CFD simulation results for core teaching space
With no wind situation, core teaching space’s temperature
reaches above 28℃ at seated height, despite buoyancy
driven flow was active (see Fig. 9(a), (b), (c) & (d)). From
1 m/s to 5 m/s the space was meeting comfort criteria,
although the velocity of air within the space was virtually
zero even in high wind speed (see Figs. 10, 11 & 12).

12. 11
Fig. 8 Temperature contour plot for (a) 2 m/s, (b) 3 m/s, (c) 4 m/s
and (d) 5 m/s of wind speeds, through west side windcatchers
Fig. 10 Temperature and PMV plot: (a) transverse section through
east side windcatchers at 1 m/s, (b) transverse section through
east side at 2 m/s, (c) longitudinal section through south side
windcatchers at 2 m/s and (d) longitudinal section through south
side windcatchers at 3 m/s of wind speeds
Fig. 9 Temperature, pressure and velocity plot at no wind speed for
(a) transverse section through east side windcatchers, (b)
transverse section middle windcatcher, (c) transverse section
through west side windcatchers and (d) longitudinal section
through north side windcatchers
3.2.3 Proposed solution to the stratification of higher
temperature and resulting PMV & PPD at seated height
for the teaching spaces
As a solution, a low energy fan assisted stacks were devised to
assist mixing of cold incoming air to the air heated by internal
gains (see Fig. 13 (a), (b), (c) & (d)). In “typical teaching
space” two fans are placed at both ends of 6.37 m × 0.8 m
stack. In “Core Teaching Space” the fan was placed at the
middle of 4.22 m × 4.26 m (bigger one) and 4 m × 1.5 m
(smaller one) stacks. Heights of stacks were the same as
windcatchers height. Number of simulation run devised

13. 12
0.8 m by 0.4 m (high) inlets (wall punch) for the stacks at
room level of typical teaching space. Similarly, for core
teaching space only 0.8 m high (and full width of the stack)
inlets were devised at the bottom of the stack. Each fan was
providing 600 L/s of air at each side of the stack which was
conceived after number of test simulation run. Temperature
and PMV plot from this fan assisted solution showed that
the typical teaching space was well within comfortable
range (see Fig. 14(a), (b), (c), (d), (e) & (f)). Similar was
the case for core teaching space (see Fig. 15(a), (b) & (c)).
Fig. 11 Temperature and PMV plot: (a) transverse section
through east side windcatchers at 4 m/s, (b) longitudinal section
through south side windcatchers at 4 m/s, (c) longitudinal section
through middle windcatcher at 5 m/s and (d) transverse section
through west side windcatchers at 5 m/s of wind speeds
Fig. 12 Velocity plot for (a) transverse section through west side
windcatchers at 4 m/s and (b) transverse section through east side
windcatchers at 5 m/s of wind speeds
Fig. 13 Positions of newly added stack areas, wall punch and fans
within the stacks: (a) view of typical teaching space from south
east side, (b) top view of typical teaching space from south east
side, (c) top view of typical teaching space from north east and
(d) first floor plan with locations of stacks & fans

14. 13
Fig. 15 Fan assisted solution plots through north side windcatchers:
(a) temperature plot, (b) PMV plot and (c) velocity plot
Fig. 14 Fan assisted solution plots: (a) temperature plot through
west side windcatchers at no wind, (b) PMV plot through west
side windcatchers at no wind, (c) temperature plot through east
side windcatchers at 1 m/s, (d) temperature plot through east side
windcatchers at 2 m/s, (e) temperature plot through east side
windcatchers at 3 m/s and (f) temperature plot through east side
windcatchers at 4 m/s of wind speed
4 Discussions and conclusion
DTS and CFD simulations demonstrated that NV with fan
assisted windcatchers has the potential to meet the health and
comfort criteria set out in the approved guidelines. Moreover,
drawing fresh air away from urban canyon floor minimises the
risk of ingress of exterior noise and air pollution (Ghiaus et al.
2005). CFD results showed that buoyancy has a profound
impact on windcatcher’s per-formance at no wind speed
situation. In ground floor the air temperature at seated height
of typical teaching space was well within acceptable limits,
while the first floor remains uncomfortable. Ventilation in
ground floor was effective because of the length of the duct (4
m) which aided buoyancy driven airflow, compared to the first
floor and its shorter duct length. Therefore, there are potentials
for exploiting
buoyancy force at low or no wind speed with long ducts,
which are in contrast to the general wisdom that windcatchers
do not perform well in such circumstances. Moreover,
extending duct length may lead to a better performing
windcatcher when serving the immediate floor. At wind
speeds between 4 and 5 m/s, the first floor appeared to be
working effectively but the same could not be said for the
ground floor, mainly due to the cross sectional area of the
windcatcher’s delivery system and the speed of the incoming
airflow. Higher velocity of airflow had pushed the cold air
further into the room while depriving the spaces immediately
below the diffusers. Convection mixing between different
strata of air did not help mitigate this problem due to the speed
of the incoming air. This creates a pocket of heated air just
below the diffusers at higher wind speed. Therefore, the
placement of a windcatcher’s inlet and outlet point needs
further in-depth investigation to make it effective when it is
placed on the side of rooms or buildings, rather in the middle
of rooms’ roof space. A fan assisted ventilation with
windcatchers significantly improved thermal comfort
condition of space along with more uniform temperature
stratification owing to the better mixing of incoming air with
the room air.

15. 14
5 Recommendation and future work
A study of all-weather ventilation systems can be a long
term goal, similar to this kind of research. Some extended
research on NV with windcatchers may be like (a) NV
performance study, with different wind directions with
varying wind speed e.g., incident wind angle of 30°, 60°,
90°, etc; (b) optimum placement of inlets & outlets of
windcatchers for better distribution of airflow within the
room; (c) study of varying duct lengths of windcatchers
serving immediate or multi floors and (d) the placement of
windcatchers and distances between them, as turbulent
flow in and around one windcatcher has the potential to
affect the performance of the other.
Acknowledgements
I would like to thank my study supervisors for their
insightful comments and suggestions during the research. I
also wish to thank all who helped me with information and
books for this research.
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