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Lecture Objectives:
• Define the final project Deliverables and Grading policy
• Analyze factors that influence accuracy of our modeling study
• Learn about automatic control
Final project topics:Deliverables:1) Preliminary report : April 28 - 5% of your final report- Should includes
- model (matchcad, excel, … , or software file) and- 1 page that contains assumptions
2) Final report : May 7Report with complete analysis
3) Project defenseOral presentation:
- May 5 and May 7 in class, or - Sometime afternoon
Final project grading:• 20% Oral presentation
Written Report (80% of total project grade)• 20% Model and justification of assumptions • 20% Depth of analysis • 20% Completeness and accuracy• 20% Quality of writing and result presentation
Final report length: 5 pages, at lest 50% text.
Accuracy of Building Energy Simulation Tool
Large number of:
– Analytical – Numerical – Empirical
models
for energy and mass transfer calculation in building envelope and building systems
Modeling steps• Define the domain• Analyze the most important phenomena and
define the most important elements• Discretize the elements and define the
connection • Write the energy and mass balance equations• Solve the equations (use numeric methods or
solver)• Present the result
Accuracy of energy simulation
• There are many different factors for inaccuracy of energy simulation results
….....…….…….
Accuracy of energy simulation Depends on
2 Assumptions - simplification which you introduced to solve the problem- level of details in your analytical and numerical models
1 Input data– geometry– material properties – weather data – operation schedule
3 Numerical methods - used to solve equations from analytical and numerical models
Find the balance !Always think what you want to achieve (what kind of analysis you want to provide)
How to check the accuracyof numerical model?
Comparison wit existing analytical solutions
Ts
0
T
-L / 2 L /2
h
h
h
T o
T
h omogenous wa ll
L = 0.2 mk = 0 . 5 W/ m Kc = 9 20 J/kgK
= 120 0 k g/mp
2
0 1 2 3 4 5 6 7 8 9 100.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Analytical solution Numerical -3 nodes, =60 min Numerical -7 nodes, =60 min Numerical -7 nodes, =12 min
(T-T
s)/(T
o-Ts
)
hour
How to check the validity of the larger simulation models?
0 24 48 72 96 120 144 168 1920
50
100
150
200
250
300
350
400
Same Area
Q internal sources Q at control surface
Q [W
]
hour
Building room:large number of analytical and Numerical equations (sub-models)
Energy balance
How to evaluate the whole building simulation tools
Two options:
1) Comparison with the experimental data - monitoring
- very expensive- feasible only for smaller buildings
2) Comparison with other energy simulation programs- for the same input data
- system of numerical experiments - BESTEST
BESTEST Building Energy Simulation TEST
• System of tests (~ 40 cases) - Each test emphasizes certain phenomena like external (internal) convection, radiation,
ground contact - Simple geometry- Mountain climate
6 m
2.7 m
3 m
8 m
0.2 m
0.2 m
1 m
2 m
SN
E
W
COMPARE THE RESULTS
Example of best test comparison
BESTEST test cases
0
2000
4000
6000
8000
10000
12000
195 200 220 230 240 270
Annual heating load [kWH]
new ES prog
ESP
BLAST
DOE2
SRES/SUN
SRES-BRE
S3PAS
TRYNSYS
TASE
What are the reasons for the energy simulation
• Design (sizing of different systems)
• Economic benefits
• Impact on environed
• Budget planning
Basic purpose of HVAC controlDaily, weekly, and seasonal swings make HVAC control challengingHighly unsteady-state environmentProvide balance of reasonable comfort at minimum cost and energy
Two distinct actions:1) Switching/Enabling: Manage availability
of plant according to schedule using timers.
2) Regulation: Match plant capacity to demand
Basic Control loopExample: Heat exchanger control
– Modulating (Analog) control
air
water
Cooling coil
(set point temperature)
x
Cooling coil control valve
Position (x)
fluid
Electric (pneumatic) motor
Vfluid = f(x) - linear or exponential function
Volume flow rate
The PID control algorithm
For our example of heating coil:
Proportional Integral Differential
time
Position (x)
constants
e(t) – difference between set point and measured value
dTTd
TKdTTTKTTKx di
)()()( measuredpointset
measuredpointset measuredpointset
Proportional(how much)
Integral(for how long)
Differential(how fast)
Position of the valve
The control in HVAC system – only PI
dTTTKTTKxi
)()( measuredpointset measuredpointset
Proportional Integral
Proportionalaffect the slope
Integralaffect the shape after the first “bump”
Set point
Set point
value
Detail control system simulationMatLAB - Simulink
Control system simulation - take into account HVAC component behavior but focus more on control devices and stability of control scheme
Models integrated in HVAC System simulation Example:
Economizer (fresh air volume flow rate control)
mixing
damperfresh air
T & RH sensors
recirc. air
Controlled device is damper
- Damper for the air - Valve for the liquids
HVAC Control
Economizer (fresh air volume flow rate control)
mixing
damperfresh air
T & RH sensors
recirc. air
Controlled device is damper
- Damper for the air - Valve for the liquids
% fresh air
Minimum for ventilation
100%
Economizer – cooling regime
How to control the fresh air volume flow rate?
% fresh air
Minimum for ventilation
100%
If TOA < Tset-point → Supply more fresh air than the minimum required
The question is how much?
Open the damper for the fresh air
and compare the Troom with the Tset-point .
Open till you get the Troom = Tset-point
If you have 100% fresh air and your still need cooling use cooling coil.
What are the priorities: - Control the dampers and then the cooling coils or - Control the valves of cooling coil and then the dampers ?
Defend by SEQUENCE OF OERATION the set of operation which HVAC designer provides to the automatic control engineer
Economizer – cooling regime
Example of SEQUENCE OF OERATIONS:
If TOA < Tset-point open the fresh air damper the maximum position
Then, if Tindoor air < Tset-point start closing the cooling coil valve
If cooling coil valve is closed and T indoor air < Tset-point start closing the damper till you get T indoor air = T set-point Other variations are possible
Sequence of calculation in energy simulation modeling is different than sequence of operation !
We often assume perfect aromatic control
Example of Sequence of calculation in energy simulation models
HVAC solver calculates Q using
plant_real Matrix solver results
Matrix solver solves newT and T
for Qrad_surf air_real
plant_real
T < Trad_sur f max
Matrix solver solves Q for
T or Tplant_corec
max min
for heating:
or for cooling:T > Trad_surf_corec min
yes
yes
no
no
controlled Trad_surf controlled Tsupply
Matrix S.
for
air_setpoint
solves required T and Q
Trad_surf plant
HVAC solver calculates Q using
plant_realMatrix solver results
Matrix solver solves new T and T
for Q supply air_real
plant_real
T < T < Tmin supply max
Matrix solver solves Q for
T or Tplant_corect
max min
yes
yes
no
no
controlled msupply
Matrix S.
for
air_setpoint
solves required T and Q
Tsupply plant
Q < Qplant_real plant Q < Qplant_real plant
HVAC solver calculates Q using
plant_realMatrix solver results
Matrix solver solves new m and T
for Qsupply air_real
plant_real
Matrix solver solves Q for
m or m and Tplant_corec t
max min supply
yes
yes
no
no
Matrix S.
for
air_setpoint
solves required m and Q
Tsupply plant
Q < Qplant_real plant
Matrix Solver
for
air_setpoint
solves Q
Tplant_recqured
Matrix solver
for Q
solves T
= 0air
plant_recqured
Matrix solver
for air_setpoint
solves Qplant_recqured
T
HVAC solver calculates Q using
plant_realMatrix solver results
Matrix solver so lves T
for Qair_real
plant_real
yes
no Q < Qplant_real plant_required
contro lled pure convective source
m < m < mmin supply max
If
corect
zone reheater and m > m
T and checkT < T < T
min supply
supply
min supply max
load calculation HVAC is OFF
HVAC control is ON
II III IV V
VII