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5
Overview of natural cross-
ventilation studies and the latest
simulation design tools used in
building ventilation-related
research
Masaaki Ohba and Isaac Lun
Abstract
High-performance insulation, draught stripping and double glazing have effectively sealed off
the fresh air routes and have made adequate natural ventilation impossible inside the modern
energy-efficient home nowadays. Cross ventilation, a passive cooling method for buildings, is
a major type of natural ventilation. Various study approaches have been reported; however,
there is still no appropriate simulation tool that can predict the indoor thermal environment of
natural ventilation. The typical building energy simulation for investigating a naturally
ventilated building adopts thermal simulation and an airflow network. However, the
airflow network approach for airflow estimation in building energy simulation cannot
accurately predict indoor airflow by solving the pressure-flow algebraic equation, the
mass balance equation and hydrostatic pressure variations. Recent advances in computer
performance and computational fluid dynamics (CFD) software integrated with building
energy simulation have made it possible to improve the accuracy to assess the
performance of natural ventilation and also to give more realistic predictions of airflow in
buildings. This chapter overviews and discusses various network airflow models
integrated with CFD in the natural ventilation of buildings. Examples of results obtained
with this approach are given to demonstrate the significant effects of such a coupling
programme on natural ventilation prediction accuracy.
BKeywords – building energy simulation; coupling; energy saving; local dynamic similarity model; multi-
zone airflow network model; natural ventilation
INTRODUCTION
The world population has exceeded 6 billion to date, with more than half of these living in
urban areas, and the urban population is expected to swell to almost 5 billion people by
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
doi:10.3763/aber.2009.0405 Bª2010 Earthscan BIS SN 1751-2549 (Print), 1756-2201 (Online) Bwww.ea rthscan.co.uk/journals/aber
2030 (UNFPA, 2007). Urbanization is progressing rapidly in many Asian cities. The process
of urbanization modifies land use from a natural environment into a built environment for
making new buildings (residential and commercial buildings, schools, playgrounds,
shopping malls and public facilities). Although rapidly expanding urban populations can
give rise to many advantages, such as job opportunities, education, quality living and
information technology, the high rates of population growth and continued urbanization
will also induce various problems, such as an increase in energy consumption and
greenhouse gas emissions, which affect our daily lives and, most importantly, the
environment.
Buildings play an important role in our communities. By intuition, one may become
aware that buildings fall into two categories: good and bad. The former create
aesthetics, provide shelter to the occupants, and also give the occupants a safe, clean
and comfortable living environment. The latter are intensive energy consumers, not to
mention that their construction already involved the consumption of a huge amount of
resources, and thus was a major cause of greenhouse gas emissions. Figure 5.1 shows
the world final energy consumption by end-use sectors in 2002; it can be seen that
about one-third of energy consumption worldwide is in buildings. Figure 5.2 shows the
worldwide estimated carbon dioxide mitigation potential at various sectoral levels in
2030. Furthermore, the construction and demolition of buildings generate large amounts
of solid waste and other emissions to air, water and land.
The quality of the indoor environment deserves serious attention. The Environmental
Protection Agency ranks indoor air pollution among the top four environmental risks in
America today (CLI Group, 2008). Since an average person spends approximately 90 per
cent of his/her life indoors (National Association of the Remodeling Industry, 2007), poor
indoor air quality (IAQ) can have serious impacts on the health, well-being and work
efficiency of occupants. The emergence of the term ‘sick building syndrome’ (SBS)
highlights the prevalence of IAQ problems in buildings worldwide. Well-designed
buildings can maximize the utilization of natural ventilation and daylight and minimize
the reliance on mechanical systems for indoor thermal comfort control, which will not
only minimize adverse impacts on the environment but also greatly enhance the
liveability of such buildings.
Nowadays, most newly built houses are airtight. In recent years, for instance in Japan,
the insulation and air-tightness levels of newly constructed residential buildings have been
improved for the purpose of energy saving. However, a reduced design of air permeability
(i.e. a high level of air tightness) will provide insufficient air through infiltration, resulting in a
significant and negative impact on a healthy environment. Indoor areas, especially at home
and at the workplace, can be unsafe to live in and unfit to work in if strict hygiene and safety
standards are not maintained. Indoor contamination is primarily due to pollutants released
from two sources: non-biological (such as carpets, building materials, cleaning chemicals,
environmental tobacco smoke, cooking and heating, and various volatile [easily
evaporated] organic compounds) and biological (such as bacteria, pollen, dust mites,
animal allergens [derived from skin, saliva and urine] and moulds). Inadequate aeration
can aggravate and increase indoor pollutant levels by not bringing in enough outdoor air
to dilute indoor source emissions and also by not carrying out of the home enough
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
potentially unhealthy indoor air pollutants. Thus, good IAQ and thermal comfort can only be
achieved if the building is well ventilated, the temperature and humidity are controlled at
comfortable levels, and air contaminant levels are low.
With an increased awareness of the cost and environmental impacts of energy use,
natural ventilation has become an increasingly attractive method not only for reducing
energy use and cost, but also for providing acceptable indoor environmental quality and
maintaining a healthy, comfortable and productive indoor climate rather than the more
prevailing approach of using mechanical ventilation. This chapter elaborates on various
related studies for natural ventilation in buildings, including a brief introduction of natural
ventilation, outlines some historical events in ventilation evolution, and classifies and
comments on various study approaches for building ventilation research. In particular,
the state of the art of building simulation design tools, such as multi-zone airflow
network models, for natural ventilation are discussed. The results achieved from this
type of model, developed by the research group of the current authors, are given as
examples in the final part of this chapter.
FIGURE 5.1 World final energy consumption by end-use sectors in 2002
FIGURE 5.2 Estimated mitigation potential at sectoral level in 2030 from bottom-up studies, compared with the
respective baselines assumed in the sector assessments (IPCC, 2007)
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
ANCIENT PHILOSOPHIES FOR HARMONY AND BALANCE OF THE
ENVIRONMENT
Natural ventilation is not a new concept. It was the primary method of ventilation and
cooling for many centuries. For instance, Feng Shui, the ancient Chinese method of
creating a harmonious environment, has been practised in China since the Qin Dynasty
beginning in about 221 BC. The philosophy of Feng Shui has received much attention
over the past decade in the western world. The literal translation of Feng Shui is ‘wind
and water’ and it is an ancient art that has been around for more than 6000 years. The
term Feng itself means wind, which implies stopping Chi (universal energy) being
dispersed by the wind or air, while Shui means water, which implies retaining Chi with
water. In a broader sense, Shui embraces all physical circumstances such as rivers,
lakes, mountains and the landscape. In a modern environment, it also refers to
buildings, roads and highways. Therefore, Shui covers the totality of the physical
environment, whereas Feng refers to the more abstract or intangible forces of the
universe, which, like the wind, are invisible to the human eye.
The combination of Feng and Shui is concerned with the effect of the environment
on the structure and interiors of buildings and also offers human beings a way of living
in harmony with nature while creating an empowering environment. The emergence of
the concept of ‘green design’ in buildings and the ancient Chinese philosophy of
Feng Shui share common themes. The criteria for ‘green design’, utilizing non-toxic
and sustainably produced materials, are very similar to many Feng Shui principles.
Green architecture or green design is an approach to reduce the harmful effects
of buildings on human health and the environment, while Feng Shui emphasizes
harmony and balance between humans and nature. In Feng Shui, Chi, which is
described as the vital life force energy that is everywhere, approaches from the
eight directions; some of these energies will be beneficial and some less so in
the present time. Nevertheless, it is considered good Feng Shui to have ventilation.
Most of the Hutongs (ancient city alleys or lanes in China) in Beijing run east– west or
north–south. This is because most Siheyuans were built along such axes according
to the rules of Feng Shui, so as to take in more sunshine and to benefit from
natural cross ventilation as well as to resist cold winds from the north. Therefore,
one should open windows regularly so that energies can circulate gently around
the dwelling place or the workplace. In other words, Feng Shui implies that the
indoor environment has a significant impact on human health and comfort, and also
highlights the importance of utilizing natural ventilation for the well-being of building
occupants.
NATURAL VENTILATION: A SOLUTION TOWARDS SUSTAINABILITY IN
ARCHITECTURE
Before the advent of mechanical ventilation, all buildings were naturally ventilated.
Buildings constructed before the 1950s were almost always designed for natural
ventilation, and it often makes sense to retain that function when renovating such
buildings. Despite the fact that mechanical ventilation is capable of providing a
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controlled rate of air change and responds to the varying needs of occupants, pollutant
loads and irrespective of the vagaries of climate, natural ventilation offers several
advantages over mechanical ventilation; in an ecologically sustainable sense, it is very
cheap and maintenance free, is well accepted and welcomed by inhabitants, uses no
energy to move the air, never fails nor breaks down, can provide small or very large
airflow rates, and does not have the noise associated with mechanical ventilation. In
addition, by opening windows and doors, this approach can maximize natural ventilation
so that the risk of airborne contagion is much lower than that with costly,
maintenance-requiring mechanical ventilation systems. Increasing the availability of
natural ventilation in dwellings helps reduce residents’ reliance on mechanical ventilation
such as air-conditioning, and hence the associated amount and cost of energy use for
thermal comfort control. This will also lead to better IAQ and more sustainable building
development in modern cities. The potential energy cost savings would be especially
valuable to households with low incomes, as the energy expenditure would account for a
greater proportion of the household income compared with financially better-off
households.
The outbreak of severe acute respiratory syndrome (SARS) in 2003 (Consensus
Statement on SARS Guideline, 2005) and the more recent outbreak of swine flu in
Mexico and America (Hong Kong Red Cross, 2009) both provided good evidence to
support the importance of natural ventilation in buildings because natural ventilation
directly affects human health, comfort and well-being. In other situations, insufficient
ventilation can cause tiredness, lack of concentration or a headache. In the past, due to
the variability and uncertainty of the driving forces, natural ventilation was perceived as
not being as reliable as mechanical ventilation. However, recently natural ventilation has
regained popularity in residential and commercial buildings. This is, on the one hand,
due to the need to introduce energy efficiency and sustainability measures. On the other
hand, the new developments in control systems have substantially improved the ability
of natural ventilation to satisfy occupant thermal comfort and IAQ demands. Thus,
natural ventilation has received more and more attention in recent years. A wide variety
of studies have been seen over the years in the literature across various ventilation
disciplines such as natural ventilation in buildings using the computational fluid dynamics
(CFD) approach (Jiang and Chen, 2001; Wong and Loke, 2001; Jiang et al, 2003), effect
of wind on cross ventilation (Jiang and Chen, 2002; Sapian, 2004), stack ventilation and
cooling (Gage, 1997; Gage et al, 2001; Li, 2002; Chenvidyakarn, 2005; Chenvidyakarn
and Woods, 2005) and the new emerging topic of night ventilation (Santamouris et al,
1996, 1997; Geros et al, 1999; Kolokotroni and Aronis, 1999; Liddament, 2000; Shaviv
et al, 2001; Axley and Emmerich, 2002; Santamouris, 2004; J. Wu et al, 2007; Z. Wu et al,
2007). These studies can be roughly classified into three categories: theoretical
and analytical (Hiramatsu et al, 1998; Cho, 1999; Wang and Deltour, 1999; Mathur and
Mathur, 2006a), measurement (Straw, 2000; Sasaki and Saito, 2003; Kotani and
Yamanaka, 2006; Song and Wong, 2006) and computation, including building energy
simulation tools and CFD (Jiang and Chen, 2001, 2002; Jiang et al, 2003, 2004; Gratia
et al, 2004; Sapian, 2004; Bustamante Go
´mez, 2006; Chang, 2006; Seifert et al, 2006;
Abdel Aziz et al, 2007).
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CATEGORY OF ARCHITECTURAL VENTILATION
Commonly, there are four types of natural ventilation (Figure 5.3): single-side ventilation
(i.e. openable windows), cross-flow ventilation (i.e. high- and low-level louvres), stack
ventilation (passive stack turrets) and top-down ventilation (i.e. wind-catcher systems).
Single-sided ventilation is supplied and extracted through the same louvres in the room,
as shown in Figure 5.3(a). With single-sided ventilation, the openings should equate to 4
per cent of the floor surface. This system is less efficient, but is applicable almost
everywhere and the internal doors may remain closed. Figure 5.3(b) illustrates cross-flow
ventilation. In this type of ventilation strategy, the ventilation supply and extraction take
place on the same level in a building. The air is supplied and extracted through louvres.
The internal doors must be opened or equipped with transit ventilation grilles. This
system generally achieves good results, except under no wind conditions. The stack
ventilation measure is described in Figure 5.3(c). Two ventilation openings, a low-level
grille and a high-level one, are typically placed above the door. Outside air enters
through the louvres and is extracted through a chimney. In this system, there will be
ventilation even when there is no wind. The areas that need to be cooled must be in
direct contact with the chimney or via efficient transit grilles. Figure 5.3(d) depicts the
top-down ventilation system, which uses roof turrets that capture wind from any
direction. It has been proved to be one of the most reliable and popular forms of natural
ventilation, simply because it uses the natural elements of wind movement to capture
FIGURE 5.3 Types of natural ventilation
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relatively clean, fresh air from above roof level and the wind pressure pushes that fresh air
supply through the wind-catcher device down into the building below.
EVOLUTION OF THE VENTILATION STUDY APPROACH – A SUMMARY
Before the invention of mechanical ventilation, buildings took advantage of natural
ventilation to provide fresh air and maintain comfort. To date, buildings are often
designed to depend solely on expensive and complex mechanical climate-control
systems. These systems can fail to deliver either adequate fresh air or thermal comfort,
and the costs of heating and cooling are also considerable. In contrast, natural cross
ventilation and cooling strategies can greatly reduce energy costs, improve comfort and
occupant satisfaction, and reduce negative environmental impacts. Simultaneously,
improved air quality can lead to increased productivity and a better quality of life for
building occupants.
The invention of ventilation, natural ventilation to be more precise, cannot be ascribed
to a certain date. The first attempt was probably made in the Stone Age period of
prehistoric times, when man used fire to produce heat, prepare food and keep predators
away, and then discovered the need to have an opening in the roof to let out the smoke
and simultaneously to supply air to keep the fire burning. Because the fire warmed the
space to a more comfortable temperature, thermal comfort was initially linked to
ventilation. Natural ventilation is a versatile concept. Its application is not limited only to
architectural engineering and building construction, but spreads across different
disciplines from large scale to small scale, for instance hot air in an urban canopy,
leakage from gas modules on offshore oil and gas platforms, overheated chips on a
computer circuit board in a computer compartment; all these situations require
ventilation to move away the heat that is built up or trapped between obstacles. As in
many other fields, a knowledge of the application of ventilation and/or natural ventilation
was gained in steps within the various areas of study, each advancing on the strength of
the others. Table 5.1 presents a number of historical events, direct and indirect, in the
evolution of ventilation, each of which represents a breakthrough, at its time, in
understanding, technique or application. Figure 5.4 gives a summary of the evolution of
ventilation study approaches.
Numerical methods have been known since the time of Newton in the 1700s. Methods
for the solution of ordinary differential equations or partial differential equations were
conceptually conceived, but only on paper. The development of numerical methods for
the solution of the basic equations of fluid mechanics was actually started from the
middle of the 20th century and created opportunities leading to numerical solutions for
practical flow problems. Hence, the second half of the 20th century brought fluid
mechanics and the measuring and computational methods that are required for the
solution of practical building ventilation problems. The combined application of the
experimental and numerical methods as well as the combined simulation design models
available today, at the beginning of the 21st century, will permit various topics of
building natural ventilation investigations that were not possible until the present time
because of the lack of suitable investigation methods.
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TABLE 5.1 Some historical events in ventilation evolution
YEAR NAME EVENT
1895 The American Society of Heating & Ventilating Engineers (ASHVE) was formed
1905 The American Society of Refrigerating Engineers (ASRE) was established
1907 William Napier Shaw First idealized buildings as single control volumes linked to the
outdoor environment via flow-limiting orifices
1910 Lewis Fry Richardson (English
mathematician and physicist)
Presented a paper on the first FDM solution for the stress
analysis of a masonry dam
1913 Sharman Kingsley Suggested that open-air schools would be a good prophylactic
measure for healthy children
1919 American Society of Heating and
Ventilating Engineers
Established a comfort chart that quantified the environmental
determinants of comfort
1922 Harvard School of Public Health Constructed a psychrometric chamber to refine the standards for
human comfort
1936 Constantin Yaglou (research
engineer)
Established a paradigm for using ventilation as a means of
achieving odour and thermal comfort in the living environment
1950s Iterative methods were employed leading to the eventual development of computational fluid dynamics
(CFD) in the late 1960s to early 1970s
1951 James B. Dick Laid out the key principles of the macroscopic building airflow
analysis
1954 ASHVE changed name to the American Society of Heating & Air Conditioning Engineers (ASHAE)
1959 ASHAE merged with ASRE and became the American Society of Heating, Refrigerating & Air
Conditioning Engineers (ASHRAE)
1960s Building simulation began with studies of fundamental theory and algorithms of load and energy
estimation
1970s Network airflow models were introduced
1973 ASHRAE proposed the first major modification to ventilation codes and published Standard 62-73 for
Natural and Mechanical Ventilation
1973 Department of Energy Funded the development of computerized energy calculation
procedures
1976 US Army Construction Engineering
Research Laboratory
BLAST (Building Loads Analysis and System Thermodynamics)
was developed
1977 Energy R&D Administration (ERDA),
and the State of California
CAL-ERDA Program, predecessor of DOE-2 Computer Program,
was introduced
1978 Lawrence Berkeley National
Laboratory
DOE-2 energy analysis program was released evolving from
previous versions that were developed in the public sector
1980s Multi-zone network models started as a research and design tool for air distribution analysis, smoke
control, etc.
1981 ASHRAE Standard 62-1981 Reduced minimum outdoor airflow rates and introduced IAQ
procedure
1988 Povl Ole Fanger (late
world-renowned professor)
Suggested at least 15 cfm per person was needed to dilute
occupant odours
Continued
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
PREVIOUS NATURAL VENTILATION STUDIES
Over the years, a wide range of efforts, nationwide and worldwide, on natural ventilation
studies have been observed in the literature and are continually being presented all over
the world. This section highlights some of the previous natural ventilation studies that were
reported in the literature, and Figure 5.5 depicts the main approaches adopted in those
works. It can be seen that the general trend of the study approach has been changed from
conventional methods to computational methods. Computational modelling seems to be
the way of studying problems or solutions for buildings today. Whatever the approach,
these methods, strictly speaking, cannot be segregated. A balance is needed: that is,
software development, computational hardware and experimental capability are integrated
with each other to converge to solutions for solving ‘real-world’ problems.
NATURAL VENTILATION STUDIES BY THE CONVENTIONAL APPROACH:
EXPERIMENTAL, THEORETICAL, ANALYTICAL AND MEASUREMENT
Interest in the application of natural ventilation in buildings is growing due to the energy,
IAQ and environmental problems associated with mechanically ventilated buildings.
Natural ventilation occurs as a result of two forces: wind driving force or buoyancy
driving force due to two causes; wind driving force or buoyancy driving force (stack
effect) due to the temperature difference between indoor and outdoor air temperatures.
A great deal of natural ventilation-related research, including wind-driven ventilation and
TABLE 5.1 Continued
YEAR NAME EVENT
1989 ASHRAE Standard 62-1989 Tripled and quadrupled the minimum non-smoking ventilation
rate of 1981
1990s Methods to integrate multi-zone airflow analysis with building thermal and contaminant-dispersal
analysis were proposed
1999 ASHRAE Standard 62-1999 Made several minor changes and clarifications that did not
impact the minimum required outdoor airflow rates
2000s Coupling airflow network model and CFD model became popular in building natural ventilation studies
2001 ASHRAE Standard 62-2001 Converted from Standard 62-1999, a little more change in
minimum outdoor airflow rates
2003 Outbreak of severe acute respiratory syndrome (SARS) caused a total of 8098 people worldwide to
become sick; 813 of these died
2004 ASHRAE Standard 62.1-2004 New ventilation rate procedure and many lower rates were
prescribed
2004 Yuguo Li and co-workers Found evidence that the SARS virus was transmitted by air when
studying the infection spread in a hospital
2006 ASHRAE Standard 2006 Supplement Contained new requirements for separation of environmental
tobacco smoke (ETS) spaces from ETS-free space
2007 ASHRAE Standard 62.1-2007 Increase in ventilation rates for high-rise residential occupancies
2009 Outbreak of swine influenza killed over 700 people worldwide in one month; WHO advised taking
adequate infection control precautions (e.g. natural ventilation) at home
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stack or buoyancy-driven ventilation, performed experimentally both full- and model-scale
as well as analytically, has been reported in the literature and a few are cited in Table 5.2. It
should be noted that the list of references in this table can by no means be considered
complete, but it gives an indication of the cutting-edge research in natural ventilation for
cooling purposes.
Although these natural ventilation (wind-driven and buoyancy-driven) systems can be
found either individually or collectively in naturally ventilated buildings, sometimes one
type can dominate the other. On the other hand, complex building geometries, such as
multiple floors that are directly or indirectly connected, may increase the difficulty of
evaluating the forces that drive natural ventilation flow. It is in part this complexity
combined with the lack of understanding of the physical mechanisms involved in both
wind- and buoyancy-driven natural ventilation that reduces the effectiveness of natural
ventilation performance in buildings.
The shape of a building influences the ventilation characteristics by its height,
influencing stack effect ventilation, and its shape in relation to the prevailing wind speed
and direction, which affects wind-induced ventilation. The relation of a building to
FIGURE 5.4 Summary of the evolution of ventilation study approaches
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
surrounding buildings affects the wind pressure distribution over the building shell and
therefore the ventilation rate (Hunt and Linden, 1999). The rate of ventilation depends on
the wind speed. The rate of ventilation increases very slightly in higher floors. Hence the
type, size, shape and location of the window apertures have to be well studied.
When evaluating naturalcross ventilation, that is, ventilation effectiveness, the path of the
air from entry to exit point must be considered.This is done on a macro anda micro scale: both
global airflow into andout of the building, andat a more detailed level with spaceby space. The
speed at which air enters into a space is part of what determines its impact on the conditions
within the space. Air velocity must be controlled within a space to avoid draught conditions,
which can cause not only occupant discomfort due to increased evaporative cooling if the
skin is exposed, but also disruption of objects in the occupied space. So far, experimental
work and theoretical predictions have been conducted mainly to estimate the airflow
through large openings and the resulting IAQ in the room. Research work on the estimation
of air distribution and the resulting thermal comfort and draught conditions is still limited.
Desirable air movement, direction and motion, in an occupied space as well as within the
building as a whole, can impact energy usage, IAQ and thermal comfort. Thus,
understanding how air flows within a space can be indicative of the effectiveness with
which fresh air is reaching occupants and stale exhaust air is leaving the building.
FIGURE 5.5 Tools for natural ventilation studies
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
TABLE 5.2 Previous work done through conventional approaches in natural ventilation investigation
FACTOR STUDY
APPROACH
REFERENCE NOTE
Discharge
coefficient
Experimental Chu et al (2009) Turbulence effects on discharge coefficient and mean
flow rate
Kobayashi et al
(2007)
Illustrated the problem of the orifice equation to predict
discharge coefficient by the chamber method
Ohba et al (2002,
2004, 2006)
Evaluated the ventilation performance of various types
of openings using the local dynamic similarity model
Kurabuchi et al
(2002, 2004)
Proposed a local dynamic similarity model of
cross-ventilation using dynamic similarity around
openings
Endo et al (2004) Investigated the structure of airflow around outflow
openings
Kato (2004) Investigated mechanical energy conservation in a
wind-driven cross-ventilation building and proposed
mechanical power balances applying to multi-zone
airflow analysis
Sandberg (2002,
2004)
Evaluated the discharge coefficients for wind-driven
cross-ventilation using circular openings in an
isothermal free (uniform) flow wind tunnel
Etheridge (2004) Analysed the natural ventilated building with large
openings from the viewpoint of scale model
measurement and the envelope flow model
True et al (2003) Investigated the effect of opening size on discharge
coefficients for simple disc and cylinder shapes
Heiselberg et al
(1999, 2001, 2002)
Studied the side and bottom hung windows with
different opening configurations and found that
discharge coefficient may decrease, increase or remain
almost constant with opening porosity, depending on
the configuration
Carey and Etheridge
(1999)
Carried out direct measurements of ventilation rates in a
wind tunnel for sharp-edged orifices
Murakami et al
(1991)
Showed an increase in the value of discharge
coefficient with porosity
Vickery and
Karakatstanis
(1987)
Reported non-standard C
d
values for a rectangular
outlet opening with opening porosity
Full-scale
measurement
Sawachi et al
(2004)
Measured discharge coefficients at building openings
under different wind directions
Sawachi (2002) Conducted measurements in a specially designed wind
tunnel that can accommodate a very large-scale model
Continued
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
TABLE 5.2 Continued
FACTOR STUDY
APPROACH
REFERENCE NOTE
Field
measurement
Flourentzou et al
(1998)
Performed measurements in a real building to determine
flow coefficients for large openings and confirmed the
values, around 0.6, found in the literature
Empirical Andersen (1996) Applied the Bernoulli equation to determine discharge
coefficient
Air velocity/
airflow rate
Field
measurement
Lam et al (2006) Pointed out that the building orientation is more
influential on air change rate rather than building height
Aluclu and Dalgic
(2005)
Pointed out that buildings should be ventilated hourly in
order to obtain necessary airflow rates for comfort
conditions if openings are all closed
Lin and Deng (2003) Reported on field studies monitoring indoor overnight
CO
2
levels and outdoor ventilation rates in bedrooms
using air-conditioners
Chao et al (1997) Found that an increase in natural ventilation rates could
reduce the indoor radon levels effectively in residential
units
Iwashita and
Akasaka (1997)
Reported on the background air change rate of eight
apartments within a 16-storey residential building
Experimental Wang and Deltour
(1999)
Investigated the lee side ventilation induced air
movement in a multi-span glasshouse
Flow regimes/
flow structure
Experimental Fitzgerald and
Woods (2007)
Carried out an analogous experiment to simulate the
natural convective flow in the building
Experimental
and
theoretical
Livermore and
Woods (2007)
Developed theoretical models to explore the conditions
under which each flow regime occurs and validated
each regime using a small-scale analogue experimental
system
Full-scale
measurement
Nishizawa et al
(2007)
Pointed out that rebounding and changing flow
direction, deflected flow, surface flow and circulating
flow were very important for cross ventilation
Ventilation
openings
Experimental Livermore and
Woods (2006)
Extended the natural ventilation of buildings to drive the
flow of different floors by the use of stacks
Niachoua et al
(2005)
Pointed out that appreciable ventilation rates can be
obtained with natural ventilation, especially when cross
ventilation with two or more windows is measured
Prianto and
Depecker (2002)
Investigated the combined effect of balcony, opening
and internal division on indoor airflows pattern of a
two-storey building
Continued
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ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
TABLE 5.2 Continued
FACTOR STUDY
APPROACH
REFERENCE NOTE
Field
measurement
Ryu et al (2009) Studied the wind characteristics effect on thermal
comfort in a Korean house
Ogunjimi et al
(2007)
Illustrated that the amount of ventilation opening and
building orientation have significant effects on the
thermal comfort level of a building
Chao et al (2004) Indicated that a good natural ventilation system could
provide an air-exchange efficiency that is even better
than the mechanical ventilation system
Raja et al (2001) Studied the thermal comfort of workers in natural
ventilated office buildings
Heiselberg et al
(2001)
Found that the bottom-hung window is the best among
most types of windows in winter for single-sided
ventilation
Eftekhari (1995) Measured air velocities and temperatures in a
single-sided ventilated office
Walker and White
(1992)
Investigated the local mean age of air in a single-sided
natural ventilated office and estimated the penetration
of fresh air in the office space
Theoretical Wang and Deltour
(1998)
Studied the natural ventilation flux in a single
greenhouse with roof opening and side wall openings
Building
orientation
Field
measurement
Lam et al (2005) Measured air changes per hour under natural ventilation
conditions with different orientations of individual flats
and different heights
Others (building design feature/parameter)
Roof solar
collectors
Field
measurement
Khedari et al (2000) Pointed out that large air gap and large and equal
size openings would induce the highest rate of airflow
rate
Experimental Khedari et al (1997) Did extensive studies on a roof solar collector using
minimum dimensions of air gap size, opening vents and
the length of collector to induce adequate airflow rate
Khedari et al (1996) Showed the impact of length variable for solar chimney
ventilation
Solar chimney Experimental Burek and Habeb
(2007)
Studied the effect of varying the solar intensity and the
channel depth on mass flow rate through the channel
Chungloo and
Limmeechockai
(2007)
Studied the effect of a solar chimney and/or water
spraying over a roof on natural ventilation
Continued
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Each window type, for example sliding, hinged and rotating, has its own effective opening
area, or the percentage ofthe overall window area through which air can flow, and amount of
leakage. Both these factors can affect the selection of a window for a given climate and
application. In addition, the types of window opening (simple opening, horizontal-vane
opening and vertical-vane opening) also have a significant impact on airflow rates. Windows
that are at a single height are not as efficient for buoyancy-driven flow, whereas windows at
two heights have a larger potential for flow through a space. The discharge coefficient,
which takes into account the effect of contraction at a window opening, affects the amount
of uncertainty within modelling naturally ventilated windows. The hydraulic resistance
across an opening influences the airflow through that opening and depends on the
geometry of the window and the Reynolds number. The majority of research has been on a
rectangular opening, and not on other geometries, such as the awning-type window.
Another passive cooling method, commonly used in tropical climatic regions, is the
stack ventilation strategy. This strategy relies on heating the building fabric by solar
TABLE 5.2 Continued
FACTOR STUDY
APPROACH
REFERENCE NOTE
Mathur et al (2006) Evaluated the possibility of making use of solar
radiation to induce room ventilation in hot climates
Mathur and Mathur
(2006a, b)
Investigated the effect of using a solar chimney for
enhancing natural ventilation
Afonso and Oliveira
(2000)
Evaluated the height parameters of a solar chimney to
satisfy the needed average flow rate
Theoretical Matrti-Herreo and
Heras-Celemin
(2007)
Proposed a mathematical model to evaluate the energy
performance of a solar chimney with a concrete wall as
thermal storage
Analytical Macias et al (2006) Presented a practical approach to improve the passive
night ventilation in social housing by applying the solar
chimney concept
Bansal et al (1994) Investigated a wind tower coupled with a solar chimney
design and found that a twofold mass flow rate could be
achieved in comparison with a stand-alone wind tower
Bansal et al (1993) Applied an analytical model to study the effect of stack
ventilation on buildings
Solar wall Experimental Hirunlabh et al
(2001)
Studied various configurations of roof solar collectors
with a metallic plate as the absorber plate and obtained
results regarding their orientation and incorporation into
the building roof
Stack effect Experimental Priyadarsini et al
(2004)
Studied the application of passive and active stack
systems to enhance natural ventilation in public housing
and gave a conclusion on the energy efficiency of the
stack system used in a hot and humid climate region
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radiation, resulting in a greater temperature difference. The most common passive devices
are a solar chimney, a solar roof and a trombe wall. The solar chimney is an effective
practical way of enhancing space natural ventilation. Thus, the solar chimney is an
attractive idea for many researchers in different fields. A number of studies in the
literature have investigated the use of a solar chimney, with different configurations, in
ventilation improvement. Some researchers are interested in analysing the vertical
chimney, whereas others are interested in the inclined chimney. However, detailed
information on air velocity distribution as air passes through the chimney and the flow
pattern in the air gap is hardly seen. The latter plays an important role in the ventilation
rate as increasing the flow area width would increase the mass flow rate. Also, there
was no validation for the proposed correlations of flow rate with both intensity and
chimney depth. Thus it would be useful if the effect of chimney inlet size measured
from the room floor, flow distribution in the air gap between the glass and the absorber,
and air velocity variation through the chimney under different operating conditions are
taken into consideration.
NATURAL VENTILATION STUDIES BY THE COMPUTATIONAL
APPROACH: BUILDING ENERGY SIMULATION TOOLS AND CFD
The study of natural ventilation in residential buildings is of significant importance as it not
only directly affects human health, comfort and well-being, but also can create a clean and
healthy indoor environment as well as save energy compared with mechanical ventilation
systems. In hot and humid cities such as those found in South East Asia, natural ventilation
is the most cost-effective way of minimizing the physiological effect of the high humidity to
achieve acceptable indoor thermal comfort conditions. In residential buildings, good cross
ventilation should be provided.
The energy requirements of a building depend not only on the individual performance
of envelope components such as walls and windows, and heating, ventilating and
air-conditioning (HVAC) as well as lighting systems, but also on their overall performance
within the unique building as an integrated system. For a large commercial building, as
an example, the complex and dynamic interactions that it has with its environment,
systems and plants need to be modelled and simulated for analysis. The technique
available to architects and building engineers is building energy simulation. Before the
advent of computer-aided building energy simulation, architects and building services
engineers relied heavily on manual calculations using pre-selected design conditions and
often turned to the ‘rule-of-thumb’ method. However, this approach frequently led to
oversized plant and system capacities and poor energy performance due to excessive
part-load operations. Computational power has increased dramatically over the past
decade, and this allows architects and engineers to run building energy simulation
programs on personal computers and test out new designs before proceeding to
construction and installation.
BUILDING ENERGY SIMULATION
Building energy simulation is an effective tool for understanding how a building consumes
energy and also for assessing new control or building design strategies aimed at improving
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building energy efficiency. Building energy simulation is a popular method for studying
naturally ventilated building design. Thermal simulation and airflow network are two
fundamental modules in the building energy simulation method. Building energy
simulation programs can be classified into two categories: design tools and detailed
simulation programs. The former is more purpose-specific and is often used in the early
design phases because it requires less and simpler input data. Since the design tools
are easy to develop and test, they are mostly developed for in-house use. On the other
hand, detailed simulation programs often incorporate computational techniques such as
finite difference, finite element, state space and transfer function for building load and
energy calculations. Some common simulation programs are DOE-2 (a public domain
program that performs hourly simulation of a building’s energy consumption and energy
cost given a description of the building’s climate, architecture, materials, operating
schedules and HVAC equipment), COMIS (a detailed program for modelling multi-zone
airflow and IAQ), ESP (a public domain transient energy simulation system capable of
modelling the energy and mass flows within combined building and plant systems) and
TRNSYS (a simulation program for passive and active solar design as well as HVAC
systems). Building energy simulation began in the 1960s and became a hot topic within
the energy research community in the 1970s. Over the past 50 years, literally hundreds
of building energy programs have been developed, enhanced and are in use. A recent
up-to-date comparison of the features and capabilities of 20 major building energy
simulation programs can be found in the study by Crawley et al (2008).
In building design, the prediction of ventilation can be difficult; situations such as
wind-driven single-sided ventilation, where the effects of turbulence dominate, are
particularly problematic to simulate. Recent advances in computer performance and CFD
software integrated with building energy simulation have made it possible to improve the
accuracy to assess the performance of natural ventilation and also give more realistic
predictions of airflow in buildings. For the study of natural ventilation and wind
microclimate, CFD is most widely used and perceived as an appropriate tool with
reasonable accuracy (Yau, 2002; Yau and Lee, 2003). It can be applicable to architectural
or engineering fluid dynamics and transport phenomena, including airflow inside and
outside a building (Versteeg and Malalasekera, 1995; Emmerich and McGrattan, 1998;
Murakami, 1998; Zhang and Chen, 2000). It can handle calculations involving temperature,
velocity, pressure and particle dispersion such as exhaust from the kitchen and bathroom.
DISCUSSION ON BUILDING ENERGY SIMULATION AND CFD
Both building energy simulation and CFD can play an important role in building design by
providing complementary information of the building performance. However, separate
applications of them usually cannot yield an accurate prediction of building thermal and
flow behaviour due to the assumptions used in the applications. Most energy simulation
programs assume that the air in an indoor space is well mixed, the air temperature and
contaminant are uniformly distributed in a zone, the momentum effects are neglected,
etc. Since momentum effects are neglected, intra-room air movement cannot be studied
and local surface convective heat and mass transfer is poorly represented as a result of
the low resolution.
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Moreover, the convective heat transfer coefficients used in these programs are usually
empirical and may not be accurate. Most building energy simulation programs cannot
determine accurate airflow entering a building by natural ventilation, while room air
temperature, and heating and cooling loads heavily depend on the airflow. On the other
hand, CFD can provide detailed information on air temperature, air velocity and
contaminant concentration within the building and its external environment. It has
become a reliable tool for the evaluation of thermal environment and contaminant
information. However, the thermal comfort prediction of naturally ventilated buildings
solely used with CFD simulation is not an easy task because the computation embodies
two scales of environment, macro-scale (building clusters) and micro-scale (room), and
the impacts of climate conditions (ambient temperature, solar radiation, wind, humidity)
provide, in boundary conditions, the calculations of heat transfer and fluid dynamics
involved in indoor thermal environment. In addition, the computational domain will
contain two different sizes of grids, large and small. The large grid size is placed outside
the building for climatic conditions, whereas the small grid size is used in the naturally
ventilated room. The inconsistency of grid sizes complicates the prediction tasks with
CFD simulation, makes it difficult to obtain stable convergence and requires more
computational capacity. This implies that with CFD simulation alone it is difficult to
accomplish the prediction of the thermal environment of naturally ventilated buildings.
Hence CFD needs assistance from building energy simulation outcomes as inputs:
heating and cooling loads, wall surface temperatures, for instance. The interrelation
between building energy simulation and CFD is ‘mutual dependence’; this approach is
therefore very attractive.
CONVENTIONAL APPROACH VERSUS COMPUTATIONAL APPROACH
Analytical models are probably the oldest method for predicting ventilation performance.
These models are derived from fundamental equations of fluid dynamics and heat
transfer such as mass, momentum, energy and chemical-species conservation
equations. Simplified geometry and thermo-fluid boundary conditions are adopted in
order to obtain a solution; hence this method is still used today. However, it may not be
accurate for complicated ventilation cases and the results may not be informative. A few
recent works are as follows: Holford and Woods (2007) used analytical models to study
the thermal buffering of naturally ventilated buildings through internal thermal mass;
Coffey and Hunt (2007) developed different analytical models of calculating ventilation
effectiveness to evaluate mixing and displacement ventilation; J. Wu et al (2007) and
Z. Wu et al (2007) used an analytical solution to assess an airflow network model for
calculating the air temperature and flow rate for complicated ventilation systems.
Full-scale measurements for a building site can provide reliable ventilation information,
such as ventilation rate and airflow distributions around and inside a building (Katayama
et al, 1992; Dascalaki et al, 1995). However, on-site experiments are time-consuming,
and the measurement data are normally limited to a few points so that they are not
easily generalized. Furthermore, wind varies over time, in terms of magnitude and
direction; thus it is difficult to assess these influences on the ventilation performance of
a building.
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Wind tunnel approaches are often used to study natural ventilation. Compared with
field measurement using full-scale objects, wind tunnel tests typically use small-scale
models, which make it possible to change the shape or size of the object and then
analyse the data. For instance, Kang and Lee (2008) used a scale model to study the
improvement of natural ventilation in a large factory building using a louvre ventilator.
This approach can minimize the cost and produces a large volume and range of data in
a short time. However, the measurement data from those wind tunnels are limited to a
few points. Besides, the flow patterns can easily be disturbed by the instrumentation
used for the velocity measurement, and hence lead to accuracy problems. More
commonly, wind tunnel measurements of mean surface pressure at openings are often
used to provide boundary condition data for the numerical calculation of ventilation
networks. However, these measurements and simulations often neglect turbulence and
so underestimate or ignore the effect of single-sided ventilation. In addition, scaling
down the test model, as well as the airflow properties, can affect the results, and the
size difference must be calculated to ensure that the results are valid.
CFD provides an alternative approach to calculate ventilation rate and detailed
airflow distributions in and around buildings, etc. This approach is becoming popular
due to its informative results and low labour and equipment costs, as a result of
the development in turbulence modelling and in computer speed and capacity. It is
becoming feasible to model a domain containing the building or even including
non-stationary obstacles (Mochida and Lun, 2008), its surroundings and its interior
spaces. However, a shortcoming of CFD is that accurate calculation of the flow field
requires adequate experimental data in order to determine whether the problem has
been modelled correctly.
DESIGN TOOLS FOR NATURAL VENTILATION
AIRFLOW NETWORK MODELS
Airflow network models calculate bulk airflow movement through a building with known
leakage or openings under given weather and shielding conditions. The airflow entering
the building through the openings is determined by the outside weather conditions,
particularly wind and temperature. The wind condition at a building is determined not
only by the overall wind information from weather statistics, but also from the geometry
of the building itself as well as its surroundings. Besides, features such as overhangs,
alcoves, neighbouring buildings and trees influence the local wind situation significantly.
The way in which this localized wind information is implemented in the simulation
programs is through discharge coefficients, C
p
, values. Thus, any errors in the C
p
values
will translate into the simulated overall building performance. Airflow network models
can be generally divided into three categories: singlezone, multi-zone and zonal.
lA single-zone model assumes that the structure (e.g. a room) can be described by a
single and well-mixed zone. The applications of this technique are limited because of its
oversimplified approach. The application of this model is often used to study the energy
consumption of a building by representing each room, or sometimes a single-family
house, by a single node.
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lA multi-zone airflow network model is developed from the single-zone model. This
model can determine the airflows in a complex ventilated building subject to internal
and external loads; hence it is extensively used in ventilation simulation. This type of
model is capable of predicting airflow and pressure distribution within a building by
dividing it into zones and flow paths. Airflows and their distribution in a given building
are caused by pressure differences that can be induced by wind, buoyancy effect,
mechanical force or a combination of these factors. Thus, this model requires extensive
information about flow characteristics and pressure distribution. This type of airflow
network model is based on the mass balance equation. A comprehensive background
and theory of multi-zone models can be found in Axley (2007). Unlike the one-node
approach, where there is only one internal pressure to be determined, the multi-zone
model must solve one pressure for each of the zones. Although this may additionally
increase the complexity of the numerical solving algorithm, this approach offers great
potential in analysing infiltration and ventilation airflow distribution.
lA zonal model is an intermediate approach between a single-zone (one-node) model
and a CFD model. This type of model treats the building and systems as a collection of
nodes (or sub-zones) representing rooms, parts of rooms and system components,
with inter-nodal connections representing the distributed flow paths associated with
cracks, doors and ducts. The assumption is made that there is a simple, non-linear
relationship between the flow through a connection and the pressure difference across
it. Conservation of mass for flows into and out of each node leads to a set of
simultaneous, non-linear equations that can be integrated over time to characterize the
flow domain. A recent review of zonal models is given by Megri and Haghighat (2007).
DISCUSSION ON AIRFLOW NETWORK MODELS
The main differences between multi-zone and zonal models are the zones and fluid flow
equations. The former model requires users to identify and describe all the zones (rooms)
of interest and the links (flow paths) between those zones (with outside air). The network
of links is described by a series of flow equations that are solved simultaneously to
provide airflow rates between rooms. Assuming that airflow patterns are not affected by
any contaminant, a mass balance calculation in each zone at each time can be included in
a multi-zone model to predict the variation of concentrations with time.
The multi-zone models use average values for the parameters describing the
conditions in a single zone (pressure, temperature, etc.). Although these models may be
used to predict airflows into and out from a room, they cannot resolve airflow patterns
or temperature distributions or contaminant concentration within a room. Thus, if such
distribution is important, the multi-zone model may not be appropriate.
In comparison with the multi-zone model, the zonal model may be used when it is
required to model distributions within a single zone. When describing the flow
characteristics of sub-zones in a single room, the zonal model must include some
models more specific than the flow and mass models used in the multi-zone model. For
instance, in a more complex room involving wall thermal plumes generated from a local
heat source, the zonal model is much more complex than the multi-zone model but it
cannot be as widely applied as CFD.
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CFD MODELS
Another approach for modelling thermal distribution and airflow movement is CFD. For
natural ventilation, this method can be used for internal building flows as well as
external building flows. For internal building flows, CFD can provide details about the
airflow distribution within naturally ventilated spaces. If there are stagnant zones, CFD
simulation will identify their location, and mitigation measures can thus be looked into in
additional studies. Calculations of airspeeds in different locations within spaces,
temperature distribution (areas that are too hot or too cold), radiative heat transfer
(showing the effect of radiant heating and cooling), direct solar radiation through
windows and openings, etc., can all be done by CFD simulation study.
In order to obtain accurate results as an input for internal building energy simulation,
information from the external environment (such as pressure) is needed. Boundary
conditions are needed to represent the atmospheric boundary layer profile that is
appropriate for the upwind terrain. For instance, a power-law representation for the
velocity profile, incoming turbulence properties and the characteristics of turbulent
fluctuations are all important inflow boundary condition information for external flow
modelling using CFD. Besides, other details of importance include the use of adequate
cell resolution, grid independence and convergence. In some cases, the presence of
very large openings in the building can affect the flow around the building, and this
needs to be assessed.
DISCUSSION ON AIRFLOW NETWORK MODELS AND CFD MODELS
CFD modelling can assist in the design of a building’s natural ventilation system. However,
it has some advantages and disadvantages compared with the airflow network model. The
very detailed level of information from CFD enables detailed thermal comfort evaluations.
This increased level of detail and information comes at a cost, because the computational
time is much longer than that for an airflow network model. Thus, only snapshots in time or
brief transient simulations can be calculated. To obtain information on the frequency of
events (such as overheating) over the course of a typical year, it is better to use airflow
network models.
CFD modelling has trouble predicting the flow around the downstream side of a
building. This is important for natural ventilation. The flow leaving the downstream side
of the upstream building impacts the building of interest, and the wind-driven pressures
at the rear and top of the design building are critical for appropriate representation of
the building ventilation flows.
Both airflow network models and CFD models need information about the air inflow/
outflow conditions. Even though CFD models for exterior building flows are not
sufficiently accurate at this point in time to replace wind tunnel testing, they
nevertheless can provide good qualitative information on the local wind climate around a
building that is considered for natural ventilation. Particularly in the early conceptual
stages this can be very helpful while keeping the effort (time and cost) below that of a
wind tunnel test. These types of CFD models can be used as a basis for early C
p
value
estimates for a range of options.
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The accuracy of CFD studies for external flows is still quite limited and needs to be
carefully considered. However, a CFD study of the airflow around the building will
provide an expert with more information on the specific conditions for the particular
building. This additional information helps to improve the quality of an estimate of the
wind pressure coefficients. Thus, the development and use of CFD-based urban climate
assessment tools are necessary (Lun et al, 2009).
NEW STUDY TREND: COUPLING STRATEGY OF NATURAL VENTILATION
DESIGN TOOLS
As described previously, a building energy simulation study can be categorized into three
approaches: building energy simulation model, airflow network model and CFD. Each
approach has its own merits and drawbacks (Hensen et al, 1996). By taking the concept
of conflating these models, for example the airflow network model and the CFD model,
in the building ventilation simulation study, this approach can provide complementary
information of a building and save significant computer resources and time. The
improvement of coupled model predictions can also be enhanced.
COUPLING BUILDING ENERGY SIMULATION WITH CFD
In the field of building-related engineering, both the CFD method and the building energy
simulation method have their own disadvantages. In view of this, the coupling of building
energy simulation with CFD is necessary (to provide an accurate solution) and has become
increasingly important in the study of building natural ventilation, and has also been turning
into an active research area in recent decades. The CFD program has been integrated into
building energy simulation for air-conditioned rooms to improve the evaluation of building
energy consumption (Negrao, 1995; Zhai et al, 2002). Djunaedy et al (2003, 2005) further
extended the coupling program to external coupling between ESP-r (thermal simulation
program) and a CFD commercial code for mechanical ventilation. For natural ventilation,
Tan and Leon (2005) coupled a multi-zone airflow model with CFD by a static strategy.
Figure 5.6 shows a schematic diagram of the coupling strategy between building energy
simulation and CFD.
COUPLING BUILDING ENERGY SIMULATION WITH THE AIRFLOW NETWORK
MODEL
In building energy simulation, the temperature (buoyancy effect) has an effect on
ventilation due to the stack effect, and the airflow rates influence the heat balance
equations. Different methods have been proposed to couple an airflow model with a
thermal model (Hensen et al, 1996). Figure 5.7 shows an example of a coupling airflow
and thermal model of airflow and energy balance calculations, integrated with the ‘onion
coupling method’ (two models iterate within one time step until some criteria of
convergence are achieved). Ventilation components are defined in the airflow model
block, which calculates incoming and outgoing airflows, depending on the external
conditions given by the weather data and the internal conditions given by ‘Indoor_air’. At
the same time, the thermal model uses the value of airflow to calculate the indoor air
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temperature. The coupling model will oscillate between these two models until the criteria
of convergence are achieved.
COUPLING AN AIRFLOW NETWORK MODEL AND CFD
Airflow network models (or multi-zone network models) quintessentially assume each
room of a building as a zone with uniform temperature and pressure and neglect the
airflow momentum preserved inside a zone. For flow with a strong momentum effect,
these assumptions may affect the accuracy of the results. Murakami et al (1991) noted
that multi-zone network models fail to account for the preservation of the kinetic energy
of airflow. Schaelin et al (1993) pointed out that the local variables, for example air
velocity and temperature, near the flow paths within each zone could have a strong
influence on multi-zone model predictions. Clarke (2001) indicated that the building
airflow network approach has significant limitations. Because momentum effects are
neglected, intra-room airflow and temperature distribution cannot be determined.
FIGURE 5.6 Coupling strategies between building simulation and CFD (Wang and Wong, 2006a)
FIGURE 5.7 Coupling airflow model and thermal model
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The idea of coupling an airflow network model (a multi-zone model) and CFD in
building airflow simulations was first proposed by Schaelin et al (1993). The author and
co-workers proposed a strategy called the ‘method of detailed flow path values’, in which
the perfect mixing assumptions of multi-zone models were rectified by the detailed
pressures, velocities and contaminant concentrations of flowpaths given fromCFD prediction.
Yuan and Srebric (2002) compared the results from multi-zone and CFD models for
contaminant transport simulations. In order to avoid the problem of airflow coupling, the
authors used pre-defined airflow rates in contaminant transport simulations. Recently,
Jayaraman et al (2004) developed an algorithm for airflow coupling and demonstrated
the algorithm on a two-dimensional building with a large space. The study showed that
coupling CFD with a multi-zone model can result in more realistic predictions of airflow
and contaminant transport in large-space buildings. This coupling simulation approach
has been seen and discussed in various publications over the past decade or so.
Beausoleil-Morrison et al (2001) described the study of the conflation of CFD and building
energy simulation, which was a further study of their previous work (Beausoleil-Morrison,
2000). The form of the CFD model used was described, and the coupling method used
to integrate the building thermal and network airflow models was outlined (Figure 5.8).
The major limitation of the accuracy of a CFD code in building applications is the
difficulty of defining the boundary conditions to the problem. Setrakian and McLean
(1991) described, through two cases of a shopping mall and a clean room, how it is
possible to obtain high-quality boundary condition information by simulating the problem
initially by the ESP building energy simulation code.
Wang and Wong (2006a) carried out a validation study on the coupling program using
full CFD simulation and field measurement. In the full CFD validation, a multi-zone case was
investigated to validate coupling simulation results with full CFD results, whereas in the
field measurement validation, the measuring results for a typical naturally ventilated
FIGURE 5.8 Coupling network flow and CFD models (Beausoleil-Morrison et al, 2001)
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building were used to compare with coupling simulation results. Pressure was taken as the
opening boundary conditions for the coupling program rather than taking velocity as the
boundary conditions as recommended by their previous study (Wang and Wong, 2006b).
The authors concluded that it is difficult to accurately predict the indoor thermal
environment using building energy simulation alone, and suggested that a coupling
program between building energy simulation and CFD is able to accurately and quickly
predict natural ventilation by taking pressure boundary conditions for indoor CFD
simulation. Yik and Lun (2009) used the coupling method to assess the natural
ventilation performance of residential building designs. These included prediction of
wind pressures on window openings in the building facade by using the CFD simulation
program, natural ventilation rate prediction by using the flow network simulation model
COMIS, and indoor free-float temperature and air-conditioning energy use predictions by
using building heat transfer simulation program HTB2 and air-conditioning energy
simulation program BECRES. Additionally, a statistical approach was taken to deal with
random variations in the speed and direction of wind.
DISCUSSION ON COUPLING MODELS FOR NATURAL VENTILATION
As mentioned in the previous section, CFD models are becoming more and more popular in
design practice. The application of CFD has had significant success in many
ventilation-related studies (Wang et al, 1991; Li and Fuchs, 1993; Borchiellini et al, 1994;
Chen, 1996). There is no doubt that CFD calculation is one of the most important
methods for natural ventilation study (Chen, 2009) and it will continue to be a research
tool for predicting ventilation performance in buildings.
In contrast with multi-zone methods, CFD simulations are more time-consuming to
establish and execute than multi-zone methods. Because of limitations in computer
power, it may not be possible to use CFD to simulate a complex building with a large
number of rooms. However, CFD is capable of predicting detailed flows in each room/
zone of the building or in part of a complex building. On the other hand, multi-zone
methods offer opportunities for whole-building performance modelling. Although the
accuracy of the multi-zone model simulation is not very accurate in each zone due to the
assumptions used, this type of model is a very powerful design tool particularly for
calculating airflow in a large building. As both CFD and building energy simulation have
their own limitations in the prediction of natural ventilation in buildings, the accuracy in
each zone can be fixed by integrating a multi-zone model with a detailed airflow
program, such as a CFD model (Wang and Chen, 2007a).
In recent years, the coupling strategy (building energy simulation models with CFD
models) is becoming increasingly popular. Energy simulation was coupled with CFD to
improve the accuracy in natural ventilation prediction with reduced computing costs
(Wang and Wong, 2007, 2008) and a multi-zone airflow program was coupled with CFD to
improve the prediction of airflow and contaminant in an entire building (Wang and Chen,
2007b). So far, the results of coupled simulations have been satisfied and substantiated:
that is, the integration of CFD with building energy simulation can improve the accuracy
and efficiency for the indoor thermal environment as well as provide fruitful information
Overview of natural cross-ventilation studies 151
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
for understanding natural cross ventilation. Hence, this coupled model can be used as a
convenient and essential tool for natural ventilation study in buildings.
CASE STUDY OF COUPLING BUILDING ENERGY SIMULATION OF TRNSYS
WITH MULTI-ZONE AIRFLOW NETWORK MODEL OF COMIS-LOCAL
DYNAMIC SIMILARITY MODEL
The recent dramatic increase in computational power available for numerical modelling and
simulation promotes the significant role in natural ventilation analysis. Various modelling
approaches have been employed for studying ventilation-related problems, and some of
the most popular methods, for example the thermal network model, multi-zone airflow
model, etc, used by different researchers, have been described in the previous section.
The airflow network model (multi-zone type network model) for natural cross-ventilation
simulation has been studied extensively by the research group of the current authors in
recent years (Kurabuchi et al, 2004, 2005, 2009; Ohba et al, 2004, 2006, 2008a, b, 2009;
Tsukamoto et al, 2009). In this second part of the chapter, a newly developed multi-zone
airflow network model, COMIS-Local Dynamic Similarity Model (LDSM) (Kurabuchi et al,
2004; Ohba et al, 2004), which was proposed for evaluating the discharge coefficient
and flow angle at an inflow opening for cross ventilation, is described. The descriptions
of LDSM and a developed ventilation model, based on LDSM theory, coupled with
COMIS and TRNSYS, are highlighted. Simulation results, as an example using this
developed ventilation model, on the cooling load of a typical Japanese detached house
(Figure 5.9) are given.
OUTLINE OF LDSM
The pressure field at the inflow opening (Figure 5.10) can be illustrated by dynamic
pressure normal to the opening (P
n
), dynamic pressure tangential to the opening (P
t
) and
ventilation driving pressure (P
r
). Thus, the total pressure (P
T
) at the inflow opening is
equal to P
n
þP
t
þP
S
. LDSM assumes that P
n
, which is directly related to ventilation
FIGURE 5.9 Floor plan of typical Architectural Institute of Japan detached house model
152 M. OHBA AND I. LUN
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
flow rate (Q), is uniquely determined by P
t
and P
r
, and that there are dynamic similarities in
the relationships among P
n
,P
t
and P
r
, when the ratios of P
r
to P
t
are coincident. The ratio of
P
r
to P
t
is defined as dimensionless room pressure (P
R
*) by Equation 5.1 while the discharge
coefficient (C
d
) and the inflow angle (
b
) are described by the ratios of P
n
to P
r
and P
t
to P
n
,
which are given by Equations 5.2 and 5.3, respectively, as shown in Table 5.3. The
characteristics of ventilation performance through an opening can be represented
by Equations 5.6 and 5.7 (see Figure 5.11). Appropriate discharge coefficients can be
calculated from Equations 5.1– 5.7, even when wind angles and opening locations are
different (Ohba et al, 2006).
OUTLINE OF THE COUPLING MODEL: COMIS-LDSM AND TRNSYS MULTI-ZONE
MODEL
Figure 5.12 shows the block diagram of the COMIS-LDSM and TRNSYS model. This type of
coupling model is widely used today as a multi-zone ventilation model for ventilation
TABLE 5.3 Fundamental equations of Local Dynamic Similarity Model
P
R¼Pr
Pr½5:1Cd¼ffiffiffiffiffiffiffiffi
Pn
jPrj
s½5:2
b
¼tan1ffiffiffiffiffiffi
Pt
Pn
s½5:3
Pr¼PRPW½5:4
Q¼CdAffiffiffiffiffiffiffiffiffiffiffi
2
r
jPrj
s½5:5
Cd¼Cds
P
R
P
RS
njP
RjjP
RSj
½5:6
Cd¼Cds jP
RjjP
RSj
½5:7
FIGURE 5.10 Dynamic similarities in the vicinity of an inflow opening
Overview of natural cross-ventilation studies 153
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
simulation. P
W
(wind pressure) and P
t
for the building envelope are provided as input data.
The ventilation performance of inflow and outflow openings is also provided as input data.
Based on the LDSM model, the COMIS code was revised to calculate the discharge
coefficients and airflow rates at inflow/outflow openings. Arbitrary room pressure (P
R
)is
given as an initial condition and a discharge coefficient corresponding to P*
R
is selected
from the ventilation performance curve. The calculation was performed by the
relaxation-Newton method until ventilation flow rates of outflow and inflow in each room
were balanced. The coupled model can estimate ventilation flow rates more accurately
FIGURE 5.11 Ventilation performance expression for basic inflow opening
FIGURE 5.12 Block diagram of COMIS-LDSM and TRNSYS model
154 M. OHBA AND I. LUN
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
than the conventional orifice model because it can select discharge coefficients suitable for
arbitrary wind directions when the wind direction is not normal to the openings. It can also
determine the inflow/outflow angles at the openings (see Equation [5.3]), which gives
important information on the internal flow patterns.
SIMULATION RESULTS OF THE COMIS-LDSM MODEL AND THE
CONVENTIONAL ORIFICE MODEL
Figure 5.13 shows the inflow/outflow incident angles and ventilation flow rates in rooms for
a wind direction of 1358. The building coverage ratio is 0 per cent. The Qvalue for the
conventional orifice model was calculated in the conventional way (a fixed C
d
of 0.63).
The incident angles at the corner openings of the living/dining/kitchen (LDK) area and
bedrooms on the second floor were larger than those of other upwind openings of LDK
and bedrooms due to the airflow passage along the external wall surface. Figure 5.14
shows the calculated ventilation flow rate for various rooms with a wind direction of
1358. There was a 26 per cent difference between the airflow rates of the conventional
orifice model and COMIS-LDSM in the LDK. In the other rooms it was 5–9 per cent.
Figure 5.15 shows the total ventilation flow rate in the house for various wind angles.
The conventional orifice model overestimated the ventilation flow rates significantly at
wind directions of 458, 1358, 2258and 3158, compared with the COMIS-LDSM model,
FIGURE 5.13 Calculated ventilation flow rates of COMIS-LDSM model and Conventional Orifice model (wind
direction 1358and building coverage ratio of 0 per cent)
Overview of natural cross-ventilation studies 155
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
especially where the approaching flow was not normal to the upwind openings. This may
cause a poor prediction of cooling load reduction when utilizing cross ventilation to reduce
the energy consumption of the air-conditioning system.
SIMULATION RESULTS OF CUMULATIVE COOLING LOADS IN LIVING –DINING–
KITCHEN IN JUNE
Table 5.4 shows the cumulative cooling loads in June and the effects of reducing energy
through cross ventilation in Cases 1 (window closed), 2 (basic opened/closed) and
FIGURE 5.14 Calculated ventilation flow rates for different rooms for 1358wind direction
FIGURE 5.15 Calculated total ventilation flow rates for each wind direction
156 M. OHBA AND I. LUN
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
3 (active opened/closed) for 20 per cent building coverage ratio. Figure 5.16 explains the
logic of operation of the window openings. It can be seen that utilization of cross
ventilation succeeded in reducing cooling loads by 14kWh (5 per cent) compared with
those required when the windows were closed. When the windows remained open
during the unoccupied time zone or while residents were sleeping, the cooling load was
127kWh less than that required when the windows remained closed.
CONCLUDING REMARKS
This chapter gives an overview of the natural cross-ventilation studies and the latest
simulation design tools used in building ventilation-related research. The first part of the
chapter presented a brief introduction to natural ventilation, outlined some historical
events in ventilation evolution, and classified and commented on various study
approaches for building ventilation research. In addition, a discussion of the state of the
art of building energy simulation design tools, including multi-zone airflow network
models, for natural ventilation was elaborated.
The airflow study taking the coupling approach of multi-zone and CFD models is
considered to be the most sophisticated method (Musser, 2001). This may be true as
these stand-alone models usually cannot yield an accurate prediction. CFD needs
assistance from building energy simulation outcomes as inputs: heating and cooling
loads, wall surface temperatures, for instance. Thus, coupling is necessary to provide an
accurate solution, and it is also important to examine carefully whether the coupled
simulation can achieve a unique solution. The idea of the coupling of a multi-zone model
and a CFD model in building airflow simulations was proposed more than a decade ago
(Schaelin et al, 1993). In the literature, it is seen that not many coupling studies have
been conducted and theoretical analyses are still lacking. On the other hand, in the
coupled building energy and CFD simulations, Clarke et al (1995) proved coupling
solution existence by analysing a super-matrix of the coupling. Zhai and Chen (2003)
investigated the solution uniqueness by performing parametric analysis for both energy
simulation and CFD models, and concluded that a converged and stable simulation can
be achieved with different data coupling methods.
The conventional zonal models can estimate airflows and heat and contaminant
transport rapidly, with low requirements regarding input data and even when computer
powers are low. However, some investigations have shown that zonal models do not
provide satisfactory predictions of airflows under isothermal conditions (e.g. Wurtz et al,
1999; Lepers, 2000). On the other hand, the central processing unit time required for
CFD calculation today does not represent a large computational burden. Thus, coupling
TABLE 5.4 Cumulative cooling load and reduction of cooling load in June for gross coverage ratio of 20 per
cent
CASE WINDOW OPERATION CUMULATIVE COOLING LOAD (KWH) REDUCTION OF COOLING LOAD (%)
1 Closed 261 –
2 Basic opened/closed 247 5.1
3 Active opened/closed 120 51.4
Overview of natural cross-ventilation studies 157
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 – 166
FIGURE 5.16 Logic of operation of cooling and windows
158 M. OHBA AND I. LUN
ADVANCES IN BUILDING ENERGY RESEARCH B2010 BVOLUME 4 BPAGES 127 –166
a zonal model and a CFD model provides another option regarding speed, accurate
predictions, etc. It should be noted that the micro-scale simulation for predicting
airflows into and out of individual rooms in buildings may also be done using CFD;
hence CFD can be a satisfactory alternative to zonal methods where more accurate
details are required. However, one of the drawbacks is due to resource considerations
(manpower, time and computing facilities); thus CFD may sometimes not be the best
option for modelling airflow outside and inside a building because this task would
involve flow fields of largely different scales.
In the second part of this chapter, a newly developed multi-zone airflow network model,
COMIS-LDSM, was described. In addition, a developed ventilation model, based on LDSM
theory, coupled with COMIS-LDSM (airflow network model) and TRNSYS (thermal
simulation program) is highlighted. As an example, simulation results obtained from this
developed ventilation model for cooling the load of a typical Japanese detached house
are given. LDSM is capable of predicting ventilation flow rates with better accuracy. It has
superiority over the conventional orifice model, particularly when the wind discharge
coefficient varies due to variant wind direction. The accuracy of the ventilation flow rate
prediction was increased by 5 to 20 per cent compared with the orifice model with fixed
discharge coefficients. In comparison with the cooling load when all the windows of the
house were closed, the predicted results showed that 5 and 51 per cent reductions were
achieved, respectively, when the rooms were occupied with windows opened and
whenever possible. The results shown in the example are encouraging. Future studies
using combined building and airflow modelling should be promoted and enhanced.
Today, environmental concerns have increased and sustainable design has become
more desirable; hence natural ventilation has become the preferred system that fulfils
occupants’ requirements. The energy used by natural ventilation is minimal. It gives
considerable cost savings through reduced construction costs as well as maintenance
and running costs. It can also help alleviate SBS and concentration rates. Decrease the
cooling demand, improve the comfort conditions and reduce indoor pollution levels,
these are the problems that have direct impacts on human beings. One needs to take
the necessary and immediate action of seeking a solution before it is too late. Thus, it is
really a challenge for building designers, architects, engineers and researchers to
present the possibilities of alternative cooling and ventilation strategies.
AUTHOR CONTACT DETAILS
Masaaki Ohba (corresponding author): Department of Architecture, Tokyo Polytechnic University, Atsugi,
Japan; ohba@arch.t-kougei.ac.jp
Isaac Lun: Wind Engineering Research Center, Tokyo Polytechnic University, Atsugi, Japan
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