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Energy and Buildings xxx (200
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ENB-2319; No of Pages 8Performance Index (ADPI) in a displacement-ventilated office is presented. By adopting the technique of Computational Fluid Dynamics (CFD),
the new ADPI models developed are used to investigate the effect of simultaneous variation of three design variables in a displacement ventilation
case, i.e. location of the displacement diffuser (Ldd), supply temperature (T) and exhaust position (Lex) on the comfort parameter ADPI. The RSM
analyses are carried out with the aid of a statistical software package MINITAB. In the current study, the separate effect of individual design
variable as well as the second-order interactions between these variables, are investigated. Based on the variance analyses of both the first- and
second-order RSM models, the most influential design variable is the supply temperature. In addition, it is found that the interactions of supply
temperature with other design variables are insignificant, as deduced from the second-order RSM model. The optimised ADPI value is
subsequently obtained from the model equations.
# 2007 Elsevier B.V. All rights reserved.
Keywords: Response Surface Methodology (RSM); Computational Fluid Dynamics (CFD); Air Diffusion Performance Index (ADPI); Thermal comfort; Air
ventilation
1. Introduction
The cooling of occupied spaces, which is generally
accomplished by mechanical ventilation, consumes a huge
amount of non-renewable fossil energy in the world that leads
to the pollution of atmospheric environment. Therefore, in
order to minimise the energy usage while enabling good
thermal comfort condition to be achieved, effective distribution
of fresh air within an occupied space is of practical importance.
For a long time, the heating, ventilating and air conditioning
(HVAC) engineers and researchers have been realising that in
order to optimise the comfort condition in an occupied space,
efficient quantitative models that establish the relationship
between a large group of independent parameters (design
variables) and output variable (response) are highly desirable.
This can be accomplished by both the experimental and
numerical approaches.
In order to study the relationship between the response and
independent design variables, a large number of experiments
are undoubtedly required. This has reflected on the increased
total cost of the study, which is particularly true in the case of
employing physical experimentations. Therefore, numerical
experiments such as those accomplished by CFD have been
gaining immense popularity within the HVAC industry since
the past few decades. Despite the fact that it is not totally free
from errors, it serves as a practical design tool for building
engineers nowadays. For example, by using pure numerical
approach, Haghighat et al. [1] have investigated the relationship
between the concentration level in a partitioned room and
various positions of door, supply and exhaust. Lee and Awbi [2]
have studied the effect of partition on ventilation effectiveness
due to its location and gap underneath. Lim et al. [3] have
determined the optimum position of an air-conditioned unit for
achieving good thermal comfort condition (based on the
* Corresponding author. Tel.: +60 389286227; fax: +60 389212116.
E-mail addresses: [email protected] (K.C. Ng),
[email protected] (K. Kadirgama), [email protected] (E.Y.K. Ng).1 Tel.: +60 389287255, fax: +60 389212116.2 Tel.: +65 67904455, fax: +65 67911859.
0378-7788/$ see front matter # 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.enbuild.2007.04.024Response surface models for C
performance index in a dis
K.C. Ng a,*, K. Kadira Department of Research & Applications, O.Y.L. R&D Cente
Sungai Buloh, Selangob Department of Mechanical Engineering, Universiti Tenaga Nasional, Km
c School of Mechanical and Aerospace Engineering, Nanyang Techn
Received 29 January 2007; received in revise
Abstract
Based on the Response Surface Methodology (RSM), the developPlease cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016D predictions of air diffusion
lacement ventilated office
ma b,1, E.Y.K. Ng c,2
t 4739, Jalan BRP 8/2, Taman Bukit Rahman Putra, 47000,
arul Ehsan, Malaysia
Jalan Kajang-Puchong, 43009 Kajang, Selangor Darul Ehsan, Malaysia
ical University, 50 Nanyang Avenue, Singapore 639798, Singapore
rm 22 March 2007; accepted 13 April 2007
nt of first- and second-order models for predicting the Air Diffusion
www.elsevier.com/locate/enbuild
7) xxxxxxodels for CFD predictions of air diffusion performance index in a
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Predicted Mean Vote) in a typical studio-type apartment. Very
recently, by employing the commercial CFD code (FLUENT),
Ooi et al. [4] have studied the temperature and velocity
distributions in an air-conditioned room for various positions of
the air conditioner blower. Based on the results of three blower
positions simulated, the best position is then selected for
maximum comfort of an occupant. In addition, some
recommendations have been given by Bojic et al. [5], based
on the pure CFD analyses (FLOVENT), the optimum
placement of a window-type air conditioner in a residential
bedroom in order to achieve minimum draft subjected to the
calculation of Air Diffusion Performance Index (ADPI). It can
be noted in general, however, most of the numerical studies
focus on the one-factor-at-a-time design, without having any
idea on the behaviour of response variable when two or more
design variables are varied at the same time. The current paper
intends to consider this particular issue that involves making
design decision based on several design variables, which is
practically desirable.
In order to demonstrate the method, the authors have
considered the effect of simultaneous variations of three design
variables in a displacement-ventilated office (refer to Fig. 1),
i.e. location of the displacement diffuser (Ldd), supply
temperature (T) and exhaust position (Lex) on the behaviour
of response variable (ADPI). The case considered here is taken
from He et al. [6], in which detailed numerical and
experimental studies have been performed to investigate the
efficiency of contaminant removal for several ventilation
systems. Here, the CFD model developed is firstly validated
with the experimental data provided by He et al. [6], prior to
ion
be
K.C. Ng et al. / Energy and Buildings xxx (2007) xxxxxx2
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ENB-2319; No of Pages 8Fig. 1. Configuration of the mockup office equipped with a displacement ventilat
4A. Exact dimensions and locations of the obstacles and measurement points canorigin O, (a) isometric view and (b) top view.
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016system investigated by He et al. [6]. The measurement points are 1A, 2A, 3A and
found in He et al. [6]. The design variables (Ldd and Lex) are measured from theodels for CFD predictions of air diffusion performance index in a
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Bui
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ENB-2319; No of Pages 8running the response surface analyses. It has been recently
noted by Abou-El-Hossein et al. [7] that RSM is one of the
statistical techniques that saves cost and time in conducting
experiments by reducing the total number of required tests.
Furthermore, RSM helps to identify, with great accuracy, the
effect of the interactions of different design variables on the
response when they are varied simultaneously. In spite of this,
within the community of building engineering, only a few
research works based on RSM are reported, such as those by
Klemm et al. [8] for multicriteria optimisation and Valencia
et al. [9] for model development to predict asphalt pavement
properties.
In this study, based on RSM, the first- and second-order
models for the comfort parameter ADPI are developed for a
displacement ventilation case illustrated in Fig. 1. Based on the
received RSM models from the CFD simulation, the most
influential design variable is determined and the corresponding
interactions between the design variables are subsequently
identified. Also, based on the model equations obtained, one
can easily identify the optimum design combination to achieve
good thermal comfort condition based on the ADPI value,
which is frequently used as a reference value for indoor airflow
studies [10].
2. Introduction to response surface methodology
Response Surface Methodology (RSM) is a collection of
mathematical and statistical techniques for empirical model
building. By careful design of experiments, the objective is to
optimise a response variable (output variable), which is
influenced by several independent design variables (input
variables). An experiment is a series of tests, called runs, in
which changes are made in the input variables in order to
identify the reasons for changes in the output response.
Originally, RSM has been developed to model experimental
responses and then migrated into the modelling of numerical
experiments. The difference is in the type of error generated by
the response. In physical experiments, inaccuracy can be due to
measurement errors whereas in numerical experiments, errors
may due to incomplete convergence of the iterative process,
round-off errors and the discrete representation of continuous
physical phenomena. In RSM, the errors are assumed to be
random.
RSM is a methodology of constructing approximations of
the system behavior using results of the response analyses
calculated at a series of points in the design variable space.
Optimisation of RSM can be solved in the following three
stages:
Design of experiment. Building the response surface model. Solution of minimization/maximisation problem accordingto the criterion selected.
The concept of a response surface involves a dependent
variable y called the response variable and several independent
K.C. Ng et al. / Energy anddesign variables x1, x2, . . ., xk. If all of these variables are
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016assumed to be measurable, the response surface can be
expressed mathematically as:
y f x1; x2; . . . ; xk (1)For practical design purpose, the goal is to optimise the
response variable y, subjected to certain combination of design
variables. In what next, the ADPI model in the form of Eq. (1)
will be expressed.
3. Model of air diffusion performance index
Draft is a frequent concern when designing indoor
environments [11]. In order to account for the presence of
draft, which is defined as any localised feeling of coolness or
warmth of any position of the body due to both air movement
and air temperature, the ADPI parameter is used in the current
study. ADPI presents the percentage of locations where values
are taken that meet specifications for effective draft temperature
(1.7 K < u< 1.1 K) and air speed (WS < 0.35 m/s). If ADPIreaches its maximum value, i.e. 100%, the most desirable
condition is thereby achieved [5]. The effective draft
temperature is expressed as:
u Tx Tc aWS b (2)where Tx is the local dry bulb temperature for air (8C), Tc theaveraged room dry bulb temperature (8C) and WS is the airspeed (m/s). The constants a and b are taken as 8 K s/m and
0.15 m/s, respectively.
With reference to RSM, where the response variable is ADPI
in the current study, the relationship between the investigated
three design variables and the response variable can be
represented by the linear Eq. (3):
y1 b0x0 b1x1 b2x2 b3x3 (3)Here, y(1) is the first-order prediction model for ADPI and b is
the model parameter. x0 is dummy variable (x0 = 1) and b0 is anarbitrary constant. The design variables such as x1, x2 and x3 are
the location of the displacement diffuser (Ldd), supply tem-
perature (T) and exhaust position (Lex), respectively.
In most of the practical cases, the response surface
demonstrates some curvature effects in most ranges of the
design variables. Therefore, it would be more useful for a
designer to consider the second-order model. The practical
importance of second-order model is to help one to understand
the second-order effect of each design variable separately and
the two-way interaction amongst these design variables. This
second-order model y(2) can be represented by Eq. (4), in
general, for three design variables:
y2 b0x0 b1x1 b2x2 b3x3 b11x21 b22x22 b33x23 b12x1x2 b13x1x3 b23x2x3 (4)
Here the two indices (subscripts) of variable b represent the
interaction between the corresponding variables. For example,
b12 represents the significance of interaction between design
ldings xxx (2007) xxxxxx 3variable 1 and 2.
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4. Research methodology
4.1. CFD simulation
The ADPI models, as discussed in the previous section, are
determined numerically in the current work. The CFD package
used has been constantly verified with the available experi-
mental measurement and reference solution (see [12,13]). Here,
prior to performing the sensitivity study based on RSM, the
flow model is validated with the experimental data given by He
et al. [6]. The ADPI values for various design combinations
(obtained from the BoxBehnken method to be discussed later)
are then determined by the validated CFD model.
The flow solver is based on the finite-volume formulation
on structured meshes using the cell-centered approach. It uses
a non-staggered variable storage technique, which is more
robust as compared to the traditional staggered arrangement
[14]. Therefore, in order to avoid the pressure oscillations
steady when the percentage difference of the successive
change between the variables at current and previous time
steps is less than 0.01%.
The configuration of the displacement ventilation flow case
has been illustrated in Fig. 1. The design variables in this
particular design combination (based on that of [6]) are:
Ldd = 1.5550 m, T = 15.9 8C and Lex = 2.33 m. Figs. 2 and 3compare the predicted speed and temperature profiles with the
available experimental data at four locations (see 1A, 2A, 3A
and 4A in Fig. 1). In general, the predicted speed and
temperature variations match the measurements and, by
considering the coarseness of the mesh system employed
(30 25 16), the agreements can be considered satisfacto-rily. The discrepancies between the predicted and measured
speed profiles may due to, partly, the low speed values
associated in most of the space in which the hot-sphere
anemometers may fail to give accurate results [6]. For the
temperature profiles, all the predictions follow the similar
in a
K.C. Ng et al. / Energy and Buildings xxx (2007) xxxxxx4
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ENB-2319; No of Pages 8arisen due to the non-staggered arrangement, the pressure
interpolation technique similar to the one proposed by Rhie
and Chow [15] is adopted here. The issue of pressurevelocity
decoupling associated with the current incompressible flow
equations is resolved via the SIMPLE algorithm of Patankar
[16]; more recent details of SIMPLE algorithm can be found
in Jasak [17]. The Bi-Conjugate Gradient (Bi-CGSTAB)
method proposed by Van der Vorst [18] has been used to solve
the sparse matrix system arisen from the discretised flow
equations. In the current study, the first-order upwind
differencing scheme for convective discretisation is adopted
for robustness purpose. This is acceptable in the current
context due to the fact that trend analysis deduced from the
simulation results of various designs is more important here.
In order to model the flow turbulence, the RNG ke equationsare adopted. Buoyancy is modelled via the Boussinesq
approximation. In order to promote numerical stability of the
buoyant flow simulation, a transient approach has been used
with a time step size of 0.1 s. The results are assumed to be
Fig. 2. Comparison of speed profiles on mesh 30 25 16 at four locations
prediction.
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016trends of those measured. Here, the predicted and measured
temperature profiles have shown clear stratification associated
with the displacement ventilation system.
4.2. Experimental design for RSM
With the validation of the current CFD model, the ADPI
models, i.e. Eqs. (3) and (4), are now readily to be determined.
The model parameter b is calculated from the least square
method, in which the calculation is performed by adopting the
commercial statistical software, MINITAB. In order to reduce
the total number of numerical tests and allow simultaneous
variation of the three independent design variables, the
numerical procedure has to be well designed.
In the current study, the BoxBehnken design method,
which is based on the combination of the factorial with
incomplete block design, has been adopted. The attractive part
of this method is that it does not require a large number of tests
as it considers only three levels (lowest 1, middle 0 and
displacement ventilated room. H = 2.26 m. ^: Experiment [6], : currentodels for CFD predictions of air diffusion performance index in a
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K.C. Ng et al. / Energy and Buildings xxx (2007) xxxxxx 5
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ENB-2319; No of Pages 8Fig. 3. Comparison of temperature profiles on mesh 30 26 16 at four locatiosupply temperature (15.9 8C), Te is the exhaust temperature (24.8 8C). ^: Exp
Table 1
Levels of design variables
Design variable Coding of levels
1 (lowest) 0 (middle) 1 (highest)Location of the displacement 0.700 1.905 3.110highest 1) of each design variable. The maximum and
minimum levels (constraints) of each design variable are
normally determined based on the recommendations given by
the manufacturer as well as users preferences. The levels of the
three design variables are given in Table 1. The BoxBehnken
design is normally used for non-sequential experimentation,
where a test is conducted only once, which in turn allows
efficient evaluation of the model parameters in the first- and
diffuser, Ldd [m]
Supply temperature, T [8C] 13 16 19Exhaust position, Lex [m] 0.00 2.36 4.72
Table 2
CFD simulation conditions according to BoxBehnken design and the predicted A
Test number Location of the displacement
diffuser, Ldd [m]
Supply temperature,
T [8C]
1 3.110 16
2 3.110 16
3 1.905 13
4 0.700 19
5 1.905 19
6 1.905 13
7 3.110 13
8 0.700 16
9 3.110 19
10 0.700 16
11 0.700 13
12 1.905 16
13 1.905 16
14 1.905 16
15 1.905 19
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016second-order models. By using MINITAB, the simulation
conditions of 15 tests are generated, as shown in Table 2. Based
on these testing conditions, the comfort parameter (response
variable), ADPI is then computed from the in-house CFD
package as described earlier. The CFD-predicted ADPI values
are plotted in Fig. 4 for different test numbers, on top of those
predictions based on RSM, which will be discussed in the next
section.
5. Results and discussions
5.1. Development of first-order ADPI model
After performing the 15 numerical tests using CFD, the
ADPI simulated is used to find the model parameters appearing
in the postulated first-order model (see Eq. (3)). In order to
n a displacement ventilated room. H = 2.26 m. T* = (T Ts)/(Te Ts). Ts is theent [6], : current prediction.perform the calculation of these parameters, the least square
method is used with the aid of MINITAB. The first-order linear
DPI models based on CFD and RSM
Exhaust position,
Lex [m]
ADPI (%)
CFD 1st-order RSM 2nd-order RSM
4.720 36.23 34.27 35.42
0.000 35.70 33.72 35.14
4.720 22.87 24.99 23.33
2.360 40.96 43.13 40.62
4.720 42.21 43.45 42.00
0.000 22.77 24.44 22.99
2.360 21.92 24.76 22.26
4.720 35.02 34.17 35.58
2.360 42.59 43.23 43.62
0.000 33.97 33.62 34.78
2.360 26.08 24.66 25.06
2.360 35.04 33.95 35.71
2.360 35.04 33.95 35.71
2.360 35.04 33.95 35.71
0.000 41.72 42.90 41.25
odels for CFD predictions of air diffusion performance index in a
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locations of the displacement diffuser (Ldd) and the exhaust
(Lex) do not contribute much to the variation of the ADPI. In
general, the increase of all the design variables will cause
the ADPI to become larger, which is desirable from the
design point of view. Chung and Lee [10] have performed a
similar trend analysis on ADPI based on different values of
inlet air temperature. It is worth to mention here that, as
illustrated in Fig. 5, the current predicted model agree
K.C. Ng et al. / Energy and Buildings xxx (2007) xxxxxx6
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ENB-2319; No of Pages 8Fig. 4. Comparison of ADPI models against CFD predictions.model for predicting the ADPI can be expressed as:
ADPI1 15:6377 0:0241Ldd 3:0770T 0:1153Lex(5)
From this linear expression, by examining the values of
the coefficients, one can easily deduce that the response
variable ADPI is significantly affected by the supply
temperature (T). Also, it is interesting to note that the
Fig. 5. Comparison of ADPI model based on supply temperatures. For RSM,
Ldd is 3.11 m and Lex is 4.72 m.
Table 3
Analysis of variance (ANOVA) for first-order equation (from MINITAB)
Source of variation Degree of freedom (d.f.) Sum of sq
Regression 3 682.312
Linear 3 682.312
Residual error 11 45.991
Lack-of-fit 9 43.324
Pure error 2 2.667
Total 14 728.303
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016qualitatively well with that of Chung and Lee [10], in which
the ADPI values increase as the supply temperature
increases.
As seen from Fig. 4, the predicted ADPI values obtained
from the first-order model agree well with the CFD values. The
adequacy of the first-order model is verified by using the
analysis of variance (ANOVA). At a level of confidence of 95%,
the model is checked for its adequacy. As shown in Table 3, the
P-value of 0.236 (> 0.05) is not significant with the lack-of fitand F-ratio is 3.61. This implies that the model can fit and it is
adequate [19].
5.2. Development of second-order ADPI model
Here, the second-order model is formulated to describe the
effect of the three design variables investigated on the ADPI,
given by MINITAB:
ADPI2 82:2641 6:2873Ldd 12:3392T 0:3862Lex 0:0074L2dd 0:3143T2 0:0875L2ex 0:4004LddT 0:0452LddLex 0:0143TLex (6)
Similar to the first-order model, by examining the
coefficients of the first-order terms, the supply temperature
(T) has the most dominant effect on the ADPI. The contribution
of exhaust location (Lex) is the least significant here. Also,
owing to the P-value of interaction is 0.248 (>0.05), one caneasily deduce that the interactions of distinct design variables
are not significant here. In other words, the most dominant
design variable T has minimum interaction with others in the
current context.
As seen from Fig. 4, the predicted ADPI using the second-
order RSM model is able to produce values close to those
computed using CFD, and, as it should be the case, it exhibits
better agreement as compared to those from the first-order RSM
model. The ANOVA shown in Table 4 indicates that the model
is adequate as the P-value of the lack-of-fit is not significant
(>0.05).
uares Mean squares F-ratio P-value
227.440 54.400 0.000
227.440 54.400 0.000
4.181
4.814 3.610 0.236
1.333odels for CFD predictions of air diffusion performance index in a
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Table 4
Analysis of variance (ANOVA) for second-order equation (from MINITAB)
Source of variation Degree of freedom (d.f.) Sum of sq
Regression 9 720.851
Linear 3 682.312
Square 3 30.050
Interaction 3 8.489
Residual error 5 7.452
Lack-of-fit 3 4.785
Pure error 2 2.667
3
K.C. Ng et al. / Energy and Bui
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ENB-2319; No of Pages 85.3. Design optimisation
With the ADPI models obtained, the optimised response
variable (ADPI) can then be determined. Here, the goal is to
maximise the ADPI from the correct combination of the design
variables.
For optimisation purpose, the response variable is trans-
formed using a specific desirability function shown in Fig. 6.
The weight defines the shape of the desirability function for the
response, which can be selected from 0.1 to 10.0 to emphasise
or de-emphasize the target value (set to 100%). Aweight can be
Less than one (minimum is 0.1) places less emphasis on thetarget, or
Equal to one places equal importance on the target and thebounds, or
Greater than one (maximum is 10.0) places more emphasis onthe target, which is the main concern of the current work.
Therefore, the weight is set to 10.0.
From the second-order ADPI model, the optimized ADPI
value is 43.41% (calculated from MINITAB), subjected to the
following combination of the design variables:
Ldd 3:11m; T 19 C; Lex 4:6487m: (7)In order to verify the optimised ADPI value predicted from
RSM, the CFD simulation is performed again, by adopting the
combination of design variables shown in Eq. (7). The ADPI
computed is 42.68% (%difference = 1.71%), and it is worth to
mention here that it is indeed the highest ADPI value as
compared to those from the previous 15 CFD tests (see Table 2).
Apparently, all the design variables are approaching their
Total 14 728.30maximum values (level = 1) in the case of maximum ADPI
value is desired. This condition holds true even for the first-
Fig. 6. Desirability function for maximising the response.
Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016order model, in which the linear model has recommended the
ceiling values of those design variables in order to achieve the
most desirable comfort condition, by maximising the ADPI
value in the current context.
6. Conclusion
CFD studies have been applied extensively to the simulation
of indoor/outdoor airflow. However, most of the numerical tests
are based on a one-factor-at-a-time design, without having any
idea about the behaviour of an output parameter (response)
when two or more design variables are varied simultaneously.
The current study focuses on the effect of simultaneous
variations of three design variables in a displacement-ventilated
office, i.e. location of the displacement diffuser (Ldd), supply
temperature (T) and exhaust position (Lex) on behaviour of the
response variable (air diffusion performance index).
In the current work, the response surface methodology has
been proven to be a successful technique to perform the trend
analysis of air diffusion performance index with respect to
various combinations of three design variables. By using the
least square method, the first- and second-order models have
been developed based on the test conditions in accordance with
the BoxBehnken design method. The models have been found
to accurately representing the ADPI values with respect to those
simulated using CFD. The equations have been checked for
their adequacy with a confidence interval of 95%.
Both RSM models reveal that the supply temperature is the
most significant design variable in determining the ADPI
response as compared to the others. In general, within the
working range of the supply temperatures considered here,
ADPI increases as the supply temperature increases. Based on
uares Mean squares F-ratio P-value
80.095 53.740 0.000
227.44 152.610 0.000
10.017 6.720 0.033
2.830 1.900 0.248
1.490
1.595 1.200 0.485
1.333
ldings xxx (2007) xxxxxx 7the second-order RSM model, the supply temperature does not
interact much with the remaining design variables. Therefore,
one may exclude both locations of displacement diffuser and
exhaust for indoor comfort design purpose (based on ADPI) in
the current design case. With the model equations obtained, a
designer can subsequently select the best combination of design
variables for achieving optimum comfort condition.
Acknowledgements
The first author would like to express his sincere
appreciation to his former colleague, Dr. T.K. Lim (now in
AMD, Singapore) for his recommendation. We also acknowl-
odels for CFD predictions of air diffusion performance index in a
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edge Mr. W.M. Chin (OYL R&D, Malaysia) for showing
consistent interest and support on the current work. The
software facilities provided by Universiti Tenaga Nasional
(UNITEN) are greatly appreciated. Also, special thanks to Mr.
Anuar (UNITEN), for developing the Graphical User Interface
of the current CFD package.
Reference
[1] F. Haghighat, Z. Jiang, J. Wang, A CFD analysis of ventilation effective-
ness in a partitioned room, Indoor Air 4 (1991) 606615.
[2] H. Lee, H.B. Awbi, Effect of partition location on the air and contaminant
movement in a room, in: Proceedings of Indoor Air 99, vol. 1, Edinburgh,
August 813, (1999), pp. 349354.
[3] T.K. Lim, Y.L. Ong, M. Hamdi, Locating the Optimum Position to Place
an Indoor Air Conditioner in Studio-Type Apartment to Achieve Good
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+ Models
ENB-2319; No of Pages 8241247.Please cite this article in press as: K.C. Ng et al., Response surface m
displacement ventilated office, Energy & Buildings (2007), doi:10.1016odels for CFD predictions of air diffusion performance index in a
/j.enbuild.2007.04.024
Response surface models for CFD predictions of air diffusion performance index in a displacement ventilated officeIntroductionIntroduction to response surface methodologyModel of air diffusion performance indexResearch methodologyCFD simulationExperimental design for RSM
Results and discussionsDevelopment of first-order ADPI modelDevelopment of second-order ADPI modelDesign optimisation
ConclusionAcknowledgementsReference