HW 3: Energy-Based Systems Modeling in Modelica
Design of an Exterior Shading System for Multistory
Buildings
Yuming Sun
Brent Weigel
ME 6105 Modeling & Simulation Design
Dr. Chris Paredis
October 28, 2010
Task 1: Define your goals and problem domain
The simulation is designed for the following problem domain: designing an outrigger
exterior shading system for minimizing total annual heating and cooling energy
consumption of a perimeter space in a multistory building. Our selected design context is
a south-facing exterior wall of a perimeter space in a multistory building located in
Atlanta, GA. Figure 1 and Figure 2 below show and a rendering and a layout drawing
(respectively) of the perimeter space design context.
Figure 1: Rendering of the perimeter space design context.
1
Figure 2: Elevation view (top) and plan view (bottom) of the perimeter space design context.
The context of the perimeter space is considered to be representative of a modern exterior
office. The exterior wall is comprised primarily of glazing, for which the outrigger
shading system is designed to control the passage of solar irradiance (solar heat gain). An
exterior shading system minimizes heating and cooling energy consumption by
maximizing solar heat gain during heating and minimizing solar heat gain during cooling.
Figure 3 shows a three-dimensional perspective of the perimeter space served by the
shading device (ceiling plenum not shown).
2
Figure 3: Three-dimensional perspective of space served by shading device (dimensions in mm).
The overall design context of our simulation is similar to our original proposal. One
significant change to our design context is a reduction in the number of design variable
for our outrigger shading device. Since our design context contains many inherent (and
uncertain) variables (T-stat setpoints, room occupancy, lighting schedule, window Uvalue, etc.) we have reduced the number of shading device design parameters from 5 to 2
(outrigger depth and outrigger height above window). This revision does not oversimplify
the design context or the simulation effort, since many dimensions of uncertainty remain.
Additionaly, our proposed simulation design has been revised to include not only the
shading and envelope systems, but also the heating and cooling (HVAC) system serving
the perimeter space (or zone). The HVAC system is now included in the simulation
design for the purpose of estimating space cooling and heating energy consumption (as
opposed to estimating merely the solar heat gain entering the perimeter space).
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The goal of our simulation model is to determine an outrigger design (outrigger design
parameter values) that minimizes total annual energy consumption for heating and
cooling a perimeter space in a multistory building. To do so, our simulation must be able
to address several questions:
 What is the total HVAC energy consumption and cost? (inclusion of cost based
on comments on initial proposal)
 To minimize space energy (or cost):
o What should the outrigger depth be?
o What should the outrigger distance above the top of the window be?
 How does uncertainty in space conditions (temperature setpoints and internal heat
gain) and envelope performance (wall thermal resistance, glazing thermal
resistance, glazing area, glazing SHGC, etc.) affect the outrigger design?
To answer these questions, our simulation must provide a reasonably accurate
representation of the thermal energy dynamics of a perimeter space in a multistory
building. Thus, our simulation must represent the relevant components and component
interactions of the thermal energy system in our design context.
Task 2: System and Simulation Specification
Our Modelica model is based on a characterization of the thermal energy system, which
contains many interacting components. In the interest of answering the design questions
listed in the previous section, the simulation model must at least represent the design
parameters of the shading device and the operation of the HVAC system serving the
perimeter space. In order to relate the shading device design parameters to the operation
(energy consumption) of the HVAC system, the simulation model must also represent the
relevant thermal energy components (external energy loads, material properties, and
geometry). Figure 4 below shows a schematic of the heat flows and temperature nodes of
the system components included in the system simulation model.
4
PLAN VIEW
Q_solar_rad
Q_infil/
exfil
Shading Device
Q_solar_rad
_shade_wall
T_room_surface
Q_solar_
glaz_rad
T_amb
Q_glaz_ext
_conv
T_glaz_ext
Q_solar_room
_rad
T_glaz_int
Q_glaz_cond
Q_surface_conv
Q_glaz_int_conv
T_room_air
Q_cooling
Q_wall_int_conv
Q_wall_ext_conv
Q_internal_conv
T_wall_ext
T_wall_int
Q_heating
Q_wall_cond
Figure 4: Simplified schematic of heat flows and temperature nodes of system components in
the simulation model.
In the above figure, the energy consumed by the HVAC system is represented by the heat
flows Q_cooling and Q_heating. Although actual HVAC energy performance is
dependent upon many building mechanical system parameters (fan efficiency, duct heat
loss/gain, air mixing/stratification, vapor-compression efficiency, etc.), the cooling and
heating flows may be modeled as prescribed heat flows to and from the room air,
T_room_air. For our cooling and heating system, we assume a conservative COP and
heating efficiency of 2.8 and 85% respectively. Furthermore, in our simulation of the
thermal dynamics of the perimeter space, we assume a uniform room air temperature.
The room air temperature is affected by heat transfer with several other system
components. One of these components is internal heat gain from equipment, lighting, and
occupants of the space, Q_internal_conv. This total convective heat load is estimated to
be 36.7 W/m2 and follows a building occupancy schedule of 8:00 am – 6:00 pm, Monday
– Friday. The room air temperature is also affected by the flow of heat through the
building envelope, to/from T_amb. This heat flow is modeled through several parallel
pathways:
1) Conduction through the opaque envelope (Q_wall_ext_conv  Q_wall_cond 
Q_wall_int_conv);
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2) Conduction through the envelope glazing (Q_glaz_ext_conv  Q_glaz_cond 
Q_glaz_int_conv);
3) Long wave radiation through the envelope glazing to the room interior, and
convection to the room air (Q_solar_rad  Q_solar_room_rad 
Q_surface_conv);
4) Infiltration/exfiltration through the envelope, Q_infil/exfil;
Heat pathways 1, 2, and 3 are affected by the shading performance of the outrigger
shading device. The shading device influences the area of solar irradiance on the exterior
glazing surface, which in-turn influences the solar irradiance on the interior room
surfaces. The short wave radiation on these surfaces ultimately affects the convective
heat flow to/from the room air, which in turn affects the heating and cooling energy
needed to serve the space.
In addition to those previously stated, the energy system simulation includes many
assumptions and abstractions which are necessary for developing a model that captures
the relevant phenomena/dynamics in a feasible manner (within the limits of available
time and available expertise for the modeling effort). One assumption is that the solar
radiation is absorbed by the interior room surfaces in the following proportions: 30
percent on the floor, 30 percent on the furniture, 20 percent on the east partition, and 20
percent on the west partition. This assumption simplifies the complex estimation of room
surface temperatures, and the assumption is consistent with most commercial building
energy simulation models. The simulation model includes heat flows through the floor,
ceiling, and wall partitions (not shown in Figure 4). The perimeter space context is
assumed to be symmetrical (with respect to materials, geometry, and space conditions)
with all adjoining spaces). Thus, whatever heat flows from a partition surface to an
adjacent space is assumed to flow through the opposite partition. This heat flow is
included to model the energy balance of the room surface temperature nodes. Another
assumption is that all heat flow within the room is either conductive or convective (no
long wave radiation between surfaces). With respect to the weather conditions, it is
assumed that the convection coefficient on the envelope exterior is constant (does not
vary with wind speed or direction). Furthermore, we assume that the solar heat gain
coefficient (SHGC) of the glazing is constant (does not change with solar angle). With
respect to building material components, the estimated thermal resistances and heat
capacitances are assumed to be consistent with typical, code-compliant commercial
building construction (see Appendix A).
6
Task 3: Create your models in Dymola
Our simulation of the energy performance of an outrigger shading device is accomplished
by incorporating the aforementioned components and relationships into Dymola. The
simulation consists of several connected models that represent unique portions of the
energy system. One portion of the system is the opaque exterior wall, represented in
Figure 5.
U_?
k=8?
k=3?
U_?
U_?
k=0?
thermalCond?
Exterior_?
Interior
co?
co?
G=G
heatCapaci?
heatCapacitor
1231?
1231?
Exterior_?
Figure 5: Dymola schematic of exterior wall model.
The exterior wall model represents the thermal conductance (U-value), heat capacitance,
surface convection, and solar radiation on the exterior surface. The building exterior ports
exchange heat with the ambient air temperature and the solar radiation, respectively, and
the interior port exchanges heat with the room air.
7
The window model (see Figure 6) is similar to the exterior wall model, except that it
utilizes a different U-value and heat capacitance. The SHGC of the window is applied
outside of the window model, along the path of solar, short wave radiation heat transfer.
U_?
k=8?
k=3?
U_?
U_?
k=2?
thermalCond?
WinExteri?
co?
WinInte?
co?
G=G
heatCapacitor
heatCapaci?
81648
81648
WinExteri?
Figure 6: Dymola schematic of window model.
The SHGC is applied outside of the window model, along the path of short wave
radiation heat transfer (see solar radiation model, Figure 14).
The building envelope consists of not only the window area and opaque wall area in
contact with the room air, but also the opaque wall in contact with the ceiling plenum. A
ceiling model was developed to account for heat transfer between the exterior and the
ceiling plenum, and between the ceiling plenum and the room air (see Figure 7).
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T_amb
Exterior_?
const2
k=3.6336
k=210
const1
Slab
FixRoom?
G=89.376
heatCapaci?
heatCapacitor
2728?
2728?
convection3
const3
Plenum
V=20
k=210
Ceiling
exterior_?
G=840
heatCapaci?
heatCapaci?
684902
684902
convection4
Figure 7: Dymola schematic of ceiling model.
The ceiling model contains the wall model for heat exchange between the plenum air and
the building exterior (both conduction and solar radiation). The floor model is the same is
identical to the ceiling model.
To properly account for the wall partition surface temperatures and the subsequent
convective heat transfer to the room air temperature, models of heat conduction through
the wall partitions are included (see Figure 8).
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Partition
port_a1
port_b1
G=48.2
heatCapaci?
heatCapacitor
492591
492591
Figure 8: Dymola schematic of partition model.
In the wall partition models, the convection coefficients are external to the model, so that
the short wave radiation heat flow, convection heat flow, and symmetrical heat flow (heat
flowing out of west partition connected to heat flowing into east partition) can be
connected separately.
The furniture model shown below in Figure 9 is included to account for the heat
capacitance of the room interior objects.
Furniture
Exterior
Interior
G=90
heatCapaci?
heatCapaci?
3000?
3000?
Figure 9: Dymola schematic of furniture model.
The room interior, partition, and envelope components are combined into a composite
envelope and interior model (see Figure 10).
10
pr?
Wal?
T_Ambient
CEI?
k=8?
pr?
WA?
K
tem?
K
con?
pr?
Win?
k=1?
k=210
tem?
W?
co?
Total_SR_w in_South
K
K
pre?
K
con?
pr?
ROOM_AIR
k=147
co? k=210
k=420
Total_SR_intoRoom
PAR?
con?
PAR?
co?
co?
co?
tem?
Fur?
K
co?
pre?
k=147
K
con?
Total_SR_Plen_Wall
tem?
R?
k=?
R?
Total_SR_Wall_South
pre?
K
pre?
K
pr?
pr?
k=?
FL?
Figure 10: Dymola schematic of envelope and interior model.
The envelope and interior model represents the flow of conductive, convective, and
radiative heat across the room boundaries and into the room air.
The room air is also connected to heat from the internal heat gain model, shown in Figure
11. The internal heat gain model represents the generation of heat from lighting, people,
and equipment, and the heat is generated according to a common occupancy schedule.
11
Lighting_load
period=86400
Occupancy
add3_1
+1
+1
prescribed?
Internal_?
+
+1
period=86400
Equipment
period=86400
Figure 11: Dymola schematic of internal heat gain model.
Additionally, a model of the air infiltration and ventilation load has been included (see
Figure 12). The model adds heating or cooling directly to the room air based on the
difference between the room air and outdoor air temperature, an ASHRAE 62.1
ventilation rate, and an assumed air infiltration rate.
12
Ventilation
add1
+1
period=86400
Infiltration
+
+1
product
Ambient_Tem?
k=5.72
prescribed?
Ventilatio?
add
+1
+
offset=0
-1
Room_Air
K
tem?
Figure 12: Infiltration and ventilation model
Estimation of the heating and cooling energy is accomplished in an HVAC system model
(see Figure 13).
13
Heating_Energy
division
Heating_cost
I
booleanToReal1
B
prescribed?
con?
k=1
k=6?
R
Cooling_Energy
divisi?
Cooling_Cost
I
booleanToReal
B
prescribed?
con?
k=-1
k=1?
R
port1
2?
K
2?
temperatu?
T_Stat_Cool
u
k=294.15
refe?
T_Stat_Heat
const
Figure 13: Dymola schematic of HVAC system (heating and cooling cost).
The HVAC system model includes temperature control setpoints for both heating and
cooling. The cooling capacity is set at 1,000 W (approximately 0.3 tons of cooling) and
the heating capacity is set at 1,000 W. The division constants account for the assumed
efficiencies, and the energy rates to calculate the heating and cooling cost.
The solar load on the room is estimated by the solar radiation model shown in Figure 14.
14
shadin?
solar_P?
COS_T?
product1
add3_1
+1
product
+1
Absor?
+1
timeTable
Direct_SR
offset=0
offset=0
k=0.05
SHGC_?
Sky_Diffuse_?
offset=0
Horizontal_To?
SR_WIn
+
SR_IntoRoom
k=0.636
VF
Area_w in
k=0.35
k=13.5
Albedo
Area_w in1
Area_plen_w all
Abso? SR_plen_w all
k=0.7
offset=0
k=0.2
VF2
k=13.5
add3_2
+1
+1
k=3.6336
Area_w all
Absor?
SR_w all
+
+1
k=0.7
k=8.0664
k=0.5
Figure 14: Dymola schematic of solar radiation.
The details of the solar radiation model are illustrated in the verification portion of this
report (Task 4). The solar radiation model contains the shading device model and it
connects to the envelop and interior model (see Figure 10).
The complete arrangement of component models is shown below in Figure 15. From this
complete mode we may estimate the total heating and cooling cost associated with the
shading device design.
15
Total_Cost
add
+1
+
+1
Ambient_Tem?
volume
hVAC
offset=0
V=80
Enve?
Solar?
internal?
ventilati?
Figure 15: Dymola schematic of combined energy-based system models.
Task 4: Verification
During the development of the simulation and model components, the model components
were tested individually and in combination, so that the proper function of the
components could be verified.
1. Solar Position
Our simulation is designed to use a large set of input data (hourly weather data for a
typical meteorological year) to calculate the thermal loads on a perimeter office space. In
order to evaluate the performance of the outrigger shading device, it is essential for the
simulation to convert the solar radiation weather data into radiation data that accounts for
the direction of the solar radiation (and the subsequent projected shaded area). The
conversion requires calculation of the solar position, a process that is performed by a
component of the solar radiation model (see upper left-hand corner of Figure 14).
The sun’s position in the sky is expressed in terms of the solar altitude angle, β, above the
horizontal and the solar azimuth angle, , measured from the south pole. The angle of
incidence θ for any surface is defined as the angle between the incoming solar rays and a
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line normal to that surface, and θ changes throughout the day as the sun follows its
ecliptic through the sky (see Figure 16). These angles are used to determine sunrise and
sunset time, and solar radiation intensity on surface.
Figure 16: Solar angles for vertical and horizontal surfaces.
Source: 2005 ASHRAE Handbook—Fundamentals, SI Edition, Chapter 31.
Our solar position model in Dymola is programmed to calculate the solar position (solar
attitude angle β and azimuth ) from city latitude, longitude and time zone parameters,
and a time table of the local time. The expected result for solar position is shown in
Figure 17, which is derived from a previous research project involving solar loading and
shading.
17
Figure 17: Hourly solar altitude and azimuth angle for Atlanta, GA on January 1st.
The solar position model was tested to verify that it produces the expected solar position.
Figure 18 below shows the solar position results produced from the Dymola model.
18
Figure 18: Continuous solar altitude and azimuth angle for Atlanta, GA from January 1st to 6th.
The test results indicate that the solar position model provides an appropriate calculation
of solar position. According to the software documentation, Dymola performs a linear
interpolation of the hourly input data to calculate a more continuous solar position
(horizontal axis of Figure 18 is displayed in seconds)
2. Solar Radiation
Solar radiation is a major part of our shading device simulation, so it is important that the
intensity of solar radiation is modeled correctly. The total short-wavelength irradiance Et
reaching a surface is the sum of the direct solar radiation ED, the diffuse sky radiation
Ed, and the solar radiation Er reflected from surrounding surfaces. The irradiance on the
fenestration aperture (window area) of the direct beam component ED is the product of
the direct normal irradiation EDN and the cosine of the angle of incidence θ between the
incoming solar rays and a line normal (perpendicular) to the surface:
Et= EDN*cos θ + VFsky-surface * Ed + Er
Solar radiation EDN and Ed are read from TMY data. The view factor (VF) between sky
and window is a fixed value (0.35) in the solar radiation model.
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Similar to the verification of the solar position model, the solar radiation model (see
Figure 14) was tested to see if the results match expectations.
Figure 19: Test of solar radiation model component from January 1st to January 6th.
The results show a daily fluctuation in the total solar irradiance, which is the result of the
estimated variation in weather conditions in the TMY data. The relative proportion of
solar radiation allocated to the window, room, and wall is determined by the relative
areas of the surfaces, the absorptance of the window and wall material, and the SHGC of
the window. The proportions shown in the test results appear reasonable since the wall
has a significantly higher absorptance than the window. The solar radiation into the room
is considerable since the window glazing area dominates the envelope area in our
simulation model. The above results were further verified with hand calculations, which
confirmed the values shown.
3. Shading Device
The outrigger shading device, which is the focus of this design simulation, is designed to
be installed above the window exterior, so that it may decrease the solar radiation
entering the room by forming a shaded area on the window. The sunlit area (shaded area)
calculation is the function of this shading device component. The input parameters for
this model are the geometry of the shading device, which will eventually be optimized to
minimize heating and cooling energy consumption. Our shading device model
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component was verified by comparing our model results to hourly calculation results
produced from a shading device design tool developed for KAWNEER. The design tool
results and Dymola model results are shown in Figure 20 and respectively.
Figure 20: KAWNEER design tool sunlit area results.
21
Figure 21: Shading device model sunlit area test results.
The test results for sunlit area are very similar to the results produced by the commercial
design tool. The variation in the vertical portions of the line plots can be explained by
variations in linear interpolation of the solar radiation TMY data.
4. HVAC Component
Estimation of the heating and cooling energy consumption associated with the shading
device design is based on the operation of an HVAC system (see Figure 13). Thus, it is
important that the HVAC system model be tested and verified. Figure 22 shows the
schematic of a simple test of the HVAC system model.
22
Figure 22: Simple test of HVAC system model.
A prescribed heat flux connected to a sinusoidal input was used to mimic the heating and
cooling load. To maintain room air temperature in the comfortable range, the HVAC
system is supposed to provide cooling and heating to the room to offset heat flux into and
out of the space (in response to room air temperature). We can observe this phenomena in
the following test results (see Figure 23).
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Figure 23: HVAC system model test results
The heating and cooling energy consumption alternates (never occur simultaneously),
and there exists some time delay compared to the input signal, which is due to the heat
capacity of the air.
Task 5: Experimentation and Interpretation
Following the verification of the model components, we have run our complete
simulation model under multiple design scenarios (see Table 1). Each of these scenarios
represent a change in various input parameter values. The purpose of these scenario
experiments is to gain a sense for how much each of the input parameters influences the
total energy consumption of the system. Due to the high time cost of running each of the
scenarios for a complete TMY year, the scenarios were evaluated for the first 3 months of
the year (7,883,100 seconds). Our results are therefore based on the heating season, but
due to the high internal heat gain, there still exists significant cooling during this period.
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Table 1: Model Experimentation Scenarios
Scenarios
Parameters
Changes
Exterior Wall Insulation Thickness
90.2 mm --> 20mm
Thermal Conductivity (U Value) (W/m2-K)
0.371 --> 0.932
Glazing Type
Double Low-E --> Double Clear
Thermal Conductivity (U Value) (W/m2-K)
2.47 --> 3.226
SHGC
0.636 --> 0.758
3
Lighting Schedual
8 am - 6pm ---> 8am-10am & 4pm-6pm
4
Ventilation (L/S/Person)
8 ---> 12
5
HVAC Setpoint Temperature (︒C)
1
2
Cooling: 24 --> 26
Heating: 21 --> 19
6
Shading Device Outrigger Depth (M)
1 --> 1.5
7
Shading Device Outrigger Depth (M)
1 --> 2
8
Distance between Shading Device and window top (M)
0 --> 0.5
9
Distance between Shading Device and window top (M)
0 --> 1
The experimentation scenarios are discusses in each of the sections that follow.
Following these sections is a summary of the impact on cooling and heating cost.
1. Exterior Wall Insulation Thickness
In our first experiment scenario, we explore how exterior wall insulation will affect
envelop cooling and heating load and HVAC energy consumption. Figure 24 and Figure
25 below show a comparison of the simulation results for the two wall insulation
thicknesses.
25
Figure 24: Heat flow through exterior wall.
26
90.2 mm Insulation
12
10
8
6
4
2
0
-2
0E0
50
20 mm Insulation
hVAC.Heating_cost
1E6
2E6
hVAC.Cooling_Cost
hVAC.Heating_cost
3E6
4E6
5E6
6E6
7E6
8E6
5E6
6E6
7E6
8E6
hVAC.Cooling_Cost
40
30
20
10
0
-10
0E0
1E6
2E6
3E6
4E6
Figure 25: Heating and cooling cost
Figure 24 shows heat flow through south wall. As is expected, the better insulated wall
will decrease heat flow both for heating and cooling season. HVAC energy cost results
(Figure 25) tell us that using higher insulation will decrease heating energy consumption,
but for cooling energy consumption, it may either decrease or increase which depending
on other cooling load sources. A room will need cooling because of higher internal load
or solar radiation when outside is actually cooler, this sometimes happens in commercial
buildings (known as exceeding the “balance point”). For this room, we can see better
insulation actually results in slightly higher cooling energy consumption.
2. Glazing Type
Next, we compare the simulation results in terms of solar radiation, conduction and
HVAC energy consumption by using double clear glazing instead of double low-e
glazing.
27
solar_radiation.SR_IntoRoom
solar_radiation.SR_IntoRoom
3.0E5
7.0E5
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
2.0E5
4.0E5
5.0E5
6.0E5
8.0E5
9.0E5
1.0E6
1.1E6
1.2E6
Figure 26: Solar radiation into room.
Figure 26 shows that solar radiation into the room increases by using double clear glazing
instead of double low-e glazing, because double low-e glazing has a lower SHGC (Solar
Heat Gain Coefficient) by coating on the surface.
200
envelop_Load.window.thermalConductor.Q_flow [W]
envelop_Load.window.thermalConductor.Q_flow [W]
0
-200
-400
-600
-800
-1000
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 27: Heat flow through window.
Figure 27 indicates that by using low-e glazing, the heat flow is lower than when using
clear glazing both for heating and cooling season, because low-e glazing has a higher
thermal resistance.
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3. Lighting Schedule
Lighting is a major part of not only for building electricity consumption but space cooling
load. We want to explore how much energy will be saved by turning off the light from 10
Am to 3Pm, when natural light is most likely to meet indoor lighting requirement.
internalLoad.prescribedHeatFlow.Q_flow
internalLoad.prescribedHeatFlow.Q_flow
1600
1400
1200
1000
800
600
400
200
0
0E0
1E5
2E5
3E5
4E5
5E5
6E5
7E5
8E5
Figure 28: Internal load.
Figure 28 shows that the internal load decreases during the time when the lights are
turned off, and when peak cooling load is also most likely to occur for a south facing
room.
4. Ventilation
Space ventilation is essential for creating an inhabitable indoor air quality by bringing
fresh air into the room. It can cause either cooling or heating load which depends on air
entropy difference between inside and outside. During our simulation period from
January to April, ventilation will always bring cooling to the room. It will increase
heating energy consumption but also save some cooling energy.
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ventilation_Infiltration.product.y
ventilation_Infiltration.product.y
400
0
-400
-800
-1200
-1600
-2000
-2400
-2800
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 29: Ventilation and infiltration load.
The results in Figure 29 show that an increase in the ventilation load significantly
increases the cooling effect of the ventilation component of the simulation model.
5. HVAC Setpoint Temperature
In this scenario, we evaluate the impact of widening the gap between the heating and
cooling HVAC temperature control (T-stat) setpoints. Figure 30 below shows the
resulting impact on total energy cost.
30
Total_Cost
Total_Cost
50
40
30
20
10
0
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 30: Relative reduction in total HVAC system energy cost due to a widening of T-stat
setpoints.
After reviewing these results and taking conventional building operation practices into
consideration, we have determined that the wider T-stat setpoint range is perhaps more
appropriate for our shading device design simulation.
6. Outrigger Depth Increase
By increasing the outrigger shading device depth, we expect that the solar radiation on
the window will decrease. Figure 31 below shows the results for increasing the outrigger
depth.
31
envelop_Load.window.WinExterior_Rad.Q_flow
700
envelop_Load.window.WinExterior_Rad.Q_flow
600
500
400
300
200
100
0
-100
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 31: Reduction in exterior window radiation due to increased outrigger depth.
The results indicate that the solar radiation load on the window is indeed reduced by
increasing the depth of the shading device.
7. Outrigger Depth Increase (additional)
By increasing the outrigger depth further, we should expect to see a reduction in the
cooling load for the HVAC system (see Figure 32 below).
32
hVAC.Cooling_Cost
45
hVAC.Cooling_Cost
40
35
30
25
20
15
10
5
0
-5
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 32: Comparison of cooling cost for increased outrigger depth.
The results of this experiment confirm our expectation of reduced energy costs. For a full
simulation year, we would expect the cooling cost reduction to be more pronounced
during the summer months.
8. Increased Shading Device Height
The shading device height above the top of the window is expected to have some impact
(positive) on the solar radiation entering the room, but not the same degree of impact as
the outrigger depth. This expectation is based on the fact that for a south-facing wall, the
solar angle is greater than 45 degrees during the most intense periods of direct solar
radiation. In this scenario, the device depth has more influence over the shaded area than
does the device height (assuming a shaded area projected on a vertical surface). Figure 33
shows the results of our simulation experiment for increased device height.
33
envelop_Load.window.thermalConductor.Q_flow
200
envelop_Load.window.thermalConductor.Q_flow
0
-200
-400
-600
-800
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 33: Comparison of window solar radiation for an increase in outrigger height.
The simulation results indicate that the solar radiation on the window is nearly identical
for device heights of 0 and 0.5 m above the top of the window. These results indicate that
an alternative shading device parameter may need to be included in our design study.
9. Increased Shading Device Height (additional)
A further increase in the height of the shading device above the window was tested and
the results are shown below in Figure 34.
34
hVAC.Cooling_Cost
45
hVAC.Cooling_Cost
40
35
30
25
20
15
10
5
0
-5
0E0
1E6
2E6
3E6
4E6
5E6
6E6
7E6
8E6
Figure 34: Comparison of cooling cost for increased outrigger depth (0 to 1m).
The simulation results compare device heights of 0 and 1 m, which is nearly the practical
maximum increase for typical building plenum heights. Since the results are nearly
identical, we conclude that a different device design parameter will need to be included in
our design study. Due to project time constraints, we have not been able to modify our
shading device model component to include an alternate design parameter/variable. Our
intent is to include window U-value and window SHGC as design variables for our study.
These design parameters, in combination with shading device depth, are directly related
to the thermal performance of the fenestration system. Thus, these parameters should be
considered together by the decision maker (architect) when designing the fenestration
system.
Experimentation Summary
The estimated heating, cooling, and total energy costs for each of the simulation
scenarios are shown below in Figure 35.
35
9
8
7
Scenario
6
Total Cost
5
Heating Energy Cost
4
Cooling Energy Cost
3
2
1
0
0
10
20
30
Cost, $
40
50
60
Figure 35: Comparison of heating, cooling, and total costs for the experimentation scenarios.
Scenario “0” represents the original base design.
The scenarios illustrate how the building design and operation context produces tradeoffs
in heating and cooling energy cost. Furthermore, the total energy consumed or saved by
changes in shading device design parameters (Scenarios 6 through 9) may be offset by
changes in the design context (Scenarios 1 through 5). Even though our comparison
scenarios are limited to the first few months of the year, we see that the depth of the
outrigger has a considerable impact on cooling energy cost. For a longer simulation year
that includes the cooling season, the impact is expected to be greater.
Task 6: Lessons learned
The experience of developing and testing a Dymola model of a shading device for a
commercial office building presented many challenges. Heat transfer in building seems is
conceptually simple, but in terms of the number and variety of interactions, it is
considerably complex. The dymola software supported modular development of the
building model components, but the connections between these components quickly
became cumbersome. In one way, our model is perhaps overly complex, in that it
includes heat transfer between opposite partitions and floor/ceiling sections (symmetrical
36
heat transfer to/from the space). After investing the time to develop the model in this
manner, not enough time was available to compare the model results with and without
partitions, floors, and ceilings. If we were to create another simulation model, we would
have started with a simpler model of the room envelope. If we had more time we would
like to include more sophistication and operational complexity in the HVAC system
model, particularly for cooling operation. Our current model measures cooling cost
during the winter months, whereas in reality, the cooling would be accomplished by “free
cooling” in economizer mode (cold ventilation air would be used instead of vapor
compression cooling).
Despite the modular, object-oriented architecture of Dymola and the Modelica language,
we were unfortunately not able to locate/utilize an existing building simulation library.
Consequently, we had to build from scratch many model components that have likely
been created by hundreds of other Modelica users. Even though we both have a solid
background of building design and simulation principles, we also went through a hard
time in training ourselves to think from the perspective of Modelica and simplified, lumpparamter modeling.
Overall, we have improved our knowledge of the behavior of the system we modeled. By
building a network of the main thermal components, we have improved our
understanding of heat transfer in a complex system, and how those components interact
with each other. Furthermore, by using Dymola, we have learned how the Modelica,
object-oriented language actually works in terms of an operating modeling.
37
Appendix A: Envelope and Space Inputs
Exterior Wall
Area
M^2
U
W/m2-K
G
W/K
Solar Absorptance
11.7
0.371
4.3407
0.7
Brick
Density
Thickness
Volume
Capacity
C
Total C/2
kg/m3
mm
M^3
J/kgK
J/K
J/k
Wood
1922.2
101.6
1.18872
836.8
1912052.506
1231150.741
Ho
Hi
Area*Ho
Area*Hi
20 W/m2-K
5 W/m2-K
MinwoolBatt
Gypboard
592.7
25.4
0.29718
2510.4
442178.3063
9.61
90.2
1.05534
836.8
8486.6728
234
58.5
800.9
12.7
0.14859
836.8
99583.9957
Partition Wall
Area
U
G
M^2
W/m2-K
W/K
29.4
1.639
48.1866
Hi
Area*Hi
Gypboard
Density
Thickness
Volume
Capacity
C
Total C/2
kg/m3
mm
M^3
J/kgK
J/K
J/k
800.9
25
0.735
836.8
492591.9432
492591.9432
Gypboard
800.9
25
0.735
836.8
492591.9432
38
5 W/m2-K
147
Ceiling and Floor
Area
U
G
Plywood+Concrete Plasterboard
M^2
42
42 Hi
W/m2-K
2.128
20
W/K
89.376
840 Area*Hi
Plywood
Density
Thickness
Volume
Capacity
C
Total C/2
Total C/2_Plasterboard
kg/m3
mm
M^3
J/kgK
J/K
J/k
J/k
700
10
0.42
1420
417480
2728740
684902.4
5 W/m2-K
210
Concrete-Light Plasterboard
1200
2800
100
13
4.2
0.546
1000
896
5040000
1369804.8
Window: Dbl LoE (e3=.1) Clr 3mm/6mm Air
Area
U
G
SHGC
M^2
W/m2-K
W/K
Density
Thickness
Volume
Capacity
C
Total C/2
kg/m3
mm
M^3
J/kgK
J/K
J/k
13.5
2.47
33.345
0.636
Ho
Hi
Area*Ho
Area*Hi
Generic Clear Pane Generic Low_E Clear Pane
2400
2400
3
3
0.0405
0.0405
840
840
81648
81648
81648
39
20 W/m2-K
5 W/m2-K
270
67.5
Occupancy
Total
6 Person
110W/person
660 W
ASHRAE
Equipment
Total
9 W/m^2
378 W
42 M^2
Lighting
Total
12 W/m^2
504 W
42 M^2
Ventilation
Min
Total
8 L/s/Person
48 L/s
0.048 M^3/s
Air
Density (Kg/m^3)
Cp (J/kg.k)
Volume M^3
1.205
1005
58.13 W/k
Infiltration
Min
Total
0.09 AC/h
17.01 m^3/h
0.004725 M^3/s
Volume
5.72 w/k
SetPoint Tem
Cooling
Heating
23-25
20-22
C
C
189 m^3
Efficiency
2.8
0.85
40