Performance Assessment of Energy Recovery Ventilator Integrated CO2 Demand Controlled System- Part 2 Optimized Ventilation for Energy Saving in Office Building Simulated by BES-CFD with Integrated CO2 Demand-Controlled System

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CO2ディマンド制御を組み込んだ熱交換型換気システムの性能評価
（第 2報）CO2ディマンド制御を組み込んだ BES-CFD の連成解析
Performance Assessment of Energy Recovery Ventilator Integrated CO2 Demand
Controlled System – Part 2
Optimized Ventilation for Energy Saving in Office Building Simulated by BES-CFD with Integrated CO2
Demand-Controlled System
学生会員 ○ 范 芸青 (九州大学) 正会員 伊藤一秀 (九州大学)
Yunqing FAN*1 Kazuhide ITO*1
*1 Interdisciplinary Graduate School of Engineering Sciences, Kyushu University
Indoor environmental quality (IEQ) has a strong impact on environmental health factors, such as sick building syndrome
(SBS) symptoms, thermal comfort, performance, and pleasantness. This paper demonstrates a technique for integrating
building energy simulation (BES) and computational fluid dynamics (CFD) with a control algorithm for CO2 demand
controlled ventilation (DCV) to promote optimized IEQ.
1. Introduction
The qualitative and quantitative impact of indoor
environmental quality (IEQ) on indoor residents has raised a
controversial awareness about public health factors, such as
sick building syndrome (SBS) symptoms, thermal comfort,
performance, and pleasantness, because people are spending
more time in buildings such as public institutions, commercial
offices, and residential houses than before [1].
Previous papers have indicated that an outdoor airflow can
dilute the indoor contaminant concentration and enhance local
air quality level, by sacrificing a cost of 30%–50% energy
consumptions. Hence, adjusting and optimizing the ventilation
rate from the perspectives of energy saving and maintaining
indoor air quality will be a fundamental and critical issue in the
design of sustainable and environmentally friendly buildings.
CO2 demand controlled ventilation (CO2-DCV) technology
has been widely used for minimizing the energy consumption
associated with building ventilation while maintaining an
acceptable indoor air quality in commercial and industrial
enclosures [2,3]. With a view to employing an auxiliary control
technique for optimization of the ventilation system, this paper
presents a numerical simulation that integrates building energy
simulation (BES) and computational fluid dynamics (CFD)
with a control algorithm for CO2-DCV, applied in a typical
office space. In this study, an energy recovery ventilator (ERV)
is also introduced in the target space, and the potential energy
savings by optimizing the ventilation rate through the ERV
with CO2-DCV is also evaluated.
This coupled numerical approach makes it possible to
eliminate the primary assumptions, i.e., mass system and
perfect mixing condition, employed in BES and provide
various outputs, depending on the needs of the designer, in
order to optimize the ventilation system configuration based on
nonuniform distributions of airflow, temperature, and CO2 in
an indoor environment.
2. Methodology
BES, which is used to calculate the heating and cooling
energy consumption of buildings, has been widely adopted for
investigation in the design stage. At the same time, CFD has
been incorporated to provide more detailed information, such
as temperature, pressure, carbon dioxide concentration, and
velocity vector distribution of airflow, heat, and contaminant
transfer in indoor and outdoor built environments [4].
The framework of the BES-CFD integrated simulation in
this study is shown in Figure 1. The automatic fully dynamic
integrated simulations, with TRNSYS as the BES software and
FLUENT as the CFD software, are carried out on the basis of
the exchange of mutual complementary boundary conditions at
each BES time step. That is, at each BES time interval, the
CFD case runs with a constant boundary condition and exports
non-uniform information on temperature, contaminant
concentration, and velocity distribution to the BES when it
achieves a converged solution, i.e., specified residuals and a
minimum number of iterations.
Based on this coupled algorithm for analyzing non-uniform
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distributions of indoor environmental factors, an algorithm for
CO2 demand controlled ventilation through an energy recovery
ventilator (ERV) is incorporated with the aim of providing an
acceptable indoor environment without incurring a high energy
consumption.
The Reynolds averaged Navier–Stokes equation with the
standard k-ε turbulence model was adopted in this CFD
analysis. For predictions in an enclosed space, such as an office
building, the standard k-ε model can predict flow and turbulent
features with low cost and reasonable accuracy. Equation (1)
indicates the transport equation of the relevant scalar, e.g.,
temperature, humidity, or CO2.
=C
t
t
i
C
US
S






       




(1)
where

is the air density,
i
U
is the ensemble averaged air
velocity vector,

is the target scalar,

is the diffusion
coefficient of the scalar in air,
t

is the turbulent viscosity,
t
C
S
denotes the Schmidt number, and
C
S
refers to a source
term.
In the case of the mass system for indoor CO2
concentration analysis, the transient transport equation of CO2
is expressed by Equation (2). Equation (2) indicates a simple
mass balance equation assuming a perfect mixing condition.
2
ven
e( )
ven P CO
dC Q
QC C G
dt

  
(2)
The representative CO2 concentration indoors is
determined by the ventilation rate Qven and the CO2 generation
rate GCO2 due to occupants. Against the perfect mixing
assumption, in order to adjust the ventilation rate in Equation
(2), various kinds of index for ventilation effectiveness have
been proposed and here, the air change effectiveness E defined
by ASHRAE is denoted as follows;
eo
po
CC
ECC


(3)
Equation (3) indicates the ratio of perfect mixing CO2
concentration and CO2 concentration in the target local domain.
Here, Ce is the reference concentration at the exhaust opening,
which corresponds to the perfect mixing concentration of
contaminants. Cp indicates the point or local domain
concentration representing the control target space. Co is the
supply air concentration through the ERV and is equal to the
outdoor air concentration.
When a nonuniform CO2 concentration distribution is
formed indoors (in the case that the target local domain
concentration is not equal to the perfect mixing concentration),
the required ventilation rate to control the local domain
concentration at the target level can be estimated by Equations
(2) and (3).
The ventilation effectiveness E defined in Equation (3) is
assumed to be constant during each BES time step, and the
ventilation rate of the next time step is adjusted by an updated
E.
3. Numerical Simulation
The entire thermal and environmental assessment was
conducted by an integrated BES-CFD approach in conjunction
35
Script file
Results file
•Building enclosure
construction data
•Climatic data and solar
position
•HVAV equipment
application data
•Building thermal
performance
•CO2Demand controlled
strategy
TRNSYS
•Building geometry and
mesh design
•Turbulence model
•CO2 source generation
•heat transfer and
contaminant
transportation
•3D Non-uniform
airflow information
FLUENT
①Time-dependent heat flux
②Supply inlet temperature
velocity
③Containment generation
rate
①Return air temperature
②Occupied zone average co2concentration
③Return air co2concentration
Figure 1 Framework of BES-CFD integration simulation
AC:Air cond itioning system
SA: Supply opening of ERV
RA: Return opening of ERV 2.4 m
1.8 m
Female fitting room
Male fitting room
Main office
AC2
SA
SA SA
SA
SA
SA
SA
SA
SA
SA
RA
RA
SA-F
SA-F
SA-F
SA-F
SA-F
SA-F
SA-F
SA-F
RA
RA
RA
RA
RA
RA
RA
RA
SA-F
SA-F
RA-F
RA-F
RA-F
RA-F
RA-F
RA-F
RA-F
RA-F
RA-F
RA-F
AC3
Window
Window
AC5
AC6
AC7
AC1
AC8
AC9
AC4
Figure 2 Layout of the target office model
B B B B B B B B
B
B
B B
B
B B B
B
B
B B B B B B
0
5
10
15
20
25
30
35
40
45
50
0:00 24:00
12:00
8:00 16:004:00 20:00
Figure 3 Time series of varying occupant density
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with CO2-DCV. The thermal characteristics of the building
performance and ERV system were described by the BES
components, which were recognized as different functional
types in TRNSYS. On the other hand, the transport
phenomenon of indoor physical parameters, including air flow,
temperature, relative humidity, CO2 concentration, and
ventilation effectiveness, were described via ANSYS/FLUENT.
Concerning the prediction accuracy of the coupled BES-CFD
approach, we have already reported results that were validated
with experimental data measured for an open-plan office in a
typical two-story building in Japan [4]. A diagrammatic
perspective view of the office model in this study is given in
Figure 2.
Thermal performance and indoor air quality, corresponding
separately to energy and concentration evaluations,
respectively, were taken into account for this integrated
prediction. With a view to controlling energy consumption, an
energy recovery ventilation (ERV) and package type air
conditioning system (PAC) were installed; on the other hand,
CO2 concentration level was controlled by CO2-DCV. A
proportional-integral-derivative (PID) controller (Type 23) was
applied to minimize the temperature fluctuation around 28°C
in the occupied zone in the office by adjusting the supply air
temperature of the PAC system. A heat exchanger (Type 91)
and ducts (Type 31) were employed to form an adiabatic ERV
system with 60% temperature exchanging co-efficiency.
A simulation was conducted to quantify the energy savings
associated with the CO2 demand controlled strategies. The set
point CO2 concentration of the DCV target was 900 ppmv. In
this study, we disregarded humidity as the latent heat.
The numerical and boundary conditions of the CFD and
BES are summarized in Tables 1 and 2, respectively. In this
study, a time varying occupant density was considered, as
shown in Figure 3.
4. Results and Discussion
The air flow pattern, temperature, and CO2 concentration
distributions at a representative time point are shown in Figure
4. The numerical and boundary conditions of this integrated
simulation were generated in accordance with real office
conditions [4]; please also refer to this paper for a detailed
description of the constant air volume case. The specific
temperature distribution pattern indicates that the average room
temperature basically complies with the controlled target of
28°C for the occupied zone. An accumulation of relatively high
temperature air around the ceiling is due to direct radiant heat
transfer between the desk surface, which is identified as a
sensitive heat source uniformly generated from the human
body and office devices, and the other heat source, the ceiling,
which uniformly represents luminous appliances. The average
air velocity at the center of the office space was approximately
0.2 m/s, and a draught flow field with a relatively high speed
was formed in local domains near the air packages in the office
space. For evaluating the air quality inside the office space,
CO2, which represents the varying occupancy density inside
the space, is used as a passive indicator. Figure 4 (3) shows that
CO2 levels are unevenly scattered in this cutaway plane. The
analytical plane is horizontally set at a height of 1.5 m, and the
CO2 concentration distribution at the peak time is presented.
The occupied zone average CO2 level is used as the set point
(900 ppmv) in a proportional control strategy by means of an
anchor point at each BES time interval. As shown in this figure,
the average CO2 concentration is approximately close to the
target set point; however, unobvious stratification is effected by
hybrid mixing air flow inside the occupied zone.
Based on the indoor CO2 level distribution, we average the
airflow information, referred to as the ventilation effectiveness
of the local domain, to adjust the ventilation rate with the BES
components.
Figure 5 shows the comparison of time-series CO2
concentration between constant air volume ventilation and CO2
demand controlled ventilation. In both cases, the CO2
Table 1 Numerical and boundary conditions for CFD
Turbulence model
Standard k-ε model
Total mesh number
703,180 (unstructured, hexahedral mesh)
Algorithm
SIMPLE, steady
AC
SA
Uin = 1.5 m/s
kin = 3/2 × (0.1 Uin)2,
εin = (Cμ3/4kin3/2)/lin,lin = 0.1 L0
RA
Uout = kout = εout = Free slip
HRV
SA
Uin = 0.81 m/s
kin = 3/2 × (0.1 Uin)2,
εin = (Cμ3/4kin3/2)/lin , lin = 0.1 L0
RA
Uout = kout = εout = Free slip
Wall surface
Velocity: generalized log law
Radiation Model
View factor: hemicube method
mutual radiation: radiosity method
Table 2 Numerical and boundary conditions for BES
Meteorological data
Meteonorm, matsumoto
Simulation time
Sep 7th, 1 h time step
Occupants
See Fig.3
Personal heat
60 W/ person (sensitive heat)
PC
140 W/ 1 PC, total 88 PCs
Illumination
10 W/m2 (on the ceiling)
Exchange rate of HRV
60% (field measurement data)
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concentrations of the occupied zone remain relatively constant
around the demand target of 900ppmv. The overshooting
concentration is caused by a trial-and-error process when
initially implementing the neutral complemented boundary
conditions to integrate the two types of software. In order to
remove indoor pollutants or some hazardous volatile organic
compound, a minimum ventilation rate is presented as
aforementioned.
By using the indoor CO2 level from the previous time step
and the current varying occupant density, ventilation rates can
be predicted from the BES components as shown in Figure 6.
Figure 6 indirectly represents the energy consumption caused
by outdoor intake through the ventilation system. The CO2
DCV case minimizes the energy penalty by 53.8% over an
entire day and by 6.7% during the working period, compared
with the CAV case. As we know, ventilation rate has a strong
impact on the energy cost of a ventilation system. Providing
more ventilation increases energy consumption, increases the
related emissions of carbon dioxide, and contributes to climate
change.
5. Conclusions
In this study, the advantage of CO2 demand controlled
ventilation through an energy recovery ventilator was
demonstrated by adopting an integrated BES-CFD simulation
with a CO2-DCV feedback algorithm. The nonuniform
distribution of airflow and temperature indoors was analyzed
by the CFD part, and in particular, CO2 concentration
distributions were predicted based on time dependent flow and
occupant density data. Finally, the energy cost saving was
evaluated by the BES part.
In future work, this integrated simulation procedure will be
validated by using field measurement data.
Acknowledgement:
Part of this project was joint research with Mitsubishi Elec
Co., and this project was partially supported by a Grant-in-Aid
for Scientific Research (JSPS 21676005).
References
[1] Omer AM. Energy, environment and sustainable development.
Renewable Sustainable Energy Review. 2008, 12(19):265-300.
[2] Persily AK. Evaluating building IAQ and ventilation with indoor
carbon dioxide. ASHRAE Transactions, 1997, 103:4072-4084.
[3] Emmerich SJ, Persily AK. State-of-the-art review of CO2
demand-controlled ventilation technology and application.
National Institute of Standards Technical Report NISTIR 6729,
July 2001.
[4] Fan YQ, Ito K. Energy consumption analysis intended for real
office space with energy recovery ventilator by integrating BES
and CFD approaches. Building and Environment. 2012, 52:57-67.
[5] Kiyoshiro S, et al. (2012) Performance assessment of energy
recovery ventilator integrated CO2 demand controlled system –
Part 1, SHASE domestic conference, submitted.
0
200
400
600
800
1000
1200
1400
CO2-DCV
CAV
0:00 24:0012:00
8:00 16:004:00 20:00
[ppm]
Figure 5 Comparison between CAV and CO2-DCV for time series of
CO2 concentration prediction at occupied zone
0
500
1000
1500
2000
2500
0:00 24:0012:008:00 16:004:00 20:00
[m3/h]
CAV
CO2-DCV
Figure 6 Comparison between CAV and CO2-DCV
for ventilation rate prediction
0.75 0.3
0.15
0.20.55
28
26
23
26
24
37
900
1020 860
500 960
0.45
0.2
0.1
0.3
0.2
28
26
23
26
1020
900 860
500
1000
(1) Air flow pattern (2) Temperature distribution (3) CO2 concentration distribution
Figure 4 Distribution of airflow, temperature, and CO2 concentration in the office model at a specific time step
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