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Interdisciplinary Project with South Texas Project Funded by The Nuclear Power Institute
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Team Members:Matt Langston – CHEN: SeniorKyle Bowzer – MEEN: JuniorRyan Bigelow – MEEN: JuniorMatthew King – MEEN: JuniorRichard Colunga – ELEN: Sophomore Jennifer Banegas – CVEN: FreshmanGeorge Campa – CHEN: FreshmanBrent Mayorga – AERO: Freshman
MentorsGraduate Mentor: Andron CrearyTAMU Mentor: Mr. Cable KurwitzSTP Mentor: Mr Rick Grantom
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Agenda
Motivation & Project Components Project Objectives CFD Analysis Lumped Parameter Simulations Experimental Results Summary Future Work
Motivation & Project Components MotivationMotivation
Provide STP’s Probabilistic Risk Assessment (PRA) Provide STP’s Probabilistic Risk Assessment (PRA) Team with air temperature profile data after a Team with air temperature profile data after a hypothetical loss of HVAC. hypothetical loss of HVAC.
Project ComponentsProject ComponentsCFD AnalysisCFD Analysis
○ Using a SolidWorks created model of room and Using a SolidWorks created model of room and electrical cabinetselectrical cabinets
Analytical CalculationsAnalytical Calculations○ Perform calculations in Matlab using a Lumped Perform calculations in Matlab using a Lumped
Parameter MethodParameter MethodLaboratory ExperimentsLaboratory Experiments
○ Run experiments investigating the heat transfer and Run experiments investigating the heat transfer and energy storage within a solid materialenergy storage within a solid material
Simulation Objectives
Determine air and metal heat up rates during various HVAC failure scenarios
Gain information on when and where the air temperature reaches manufacturer’s critical temperature (104°F)
Investigate the effect of energy storage within metal in the transformers using a cabinet CFD model
CFD - SolidWorks Model
InletsInlets
InletsInlets
OutletsOutlets
Heater RodsHeater Rods
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Distribution of Heat Loss
29935 watts
3131 watts
1966 watts
12596 watts
200 watts
2234 watts
1759 watts
2519 watts
1523 watts
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29935 watts
CFD Simulations Computer Simulations
Case 1: Steady State○ Simulates the EAB
room’s “Normal Operating Conditions” (50°F inlet air temp and 19870 cfm)
Case 2: Transient○ Simulates the loss of one
of the HVAC trains (50% air flow)
Case 3: Transient○ Simulates the HVAC
chiller failure (73°F inlet air temp instead of 50°F)
Case 4: Transient○ Simulates the total loss of
HVAC
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Case 1 - Temperature Profile for “Normal Operating Conditions”
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64°F average
Case 1 - Maximum Temperature “Normal Operating Conditions”
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Max air temp ~78 °F (above main cabinet)
Max air temp between cabinets ~64°F (at 5ft)
5 ft
Case 2 - Temperature Profile “Half-flow Simulation”
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68°F average
SS after ~ 21min
Case 3 - Temperature Profile “HVAC Chiller Failure”
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83°F average
SS after ~ 19 min
Temperature Profile “Total HVAC Failure”
When Critical Temperature (104°F) is Reached
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Critical temperature (104F) location
After ~19 minutes
Final Results Plot
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Case 1Case 1
Case 3Case 3
Case 2Case 2
Case 4Case 4
104°F
Energy Storage in Transformers The HVAC failure problem is more complicated because it
is a transient problem Stored thermal energy flow is important in the temperature history In particular, the heat up of the transformer’s copper windings
and iron cores due to the high specific heat capacity.
Bounding the Specific Heat Based on manufacturer’s specifications of transformer
cabinets in the EAB room, metal mass composition values were assumed: Stainless steel: 15-20% Aluminum: 5-20% Iron: 20-60% Copper: 40-60%
Bounded Values Using Matlab, all
possible mass combinations were computedUsed to determine
max, min, mean of lumped specific heat
Min Cp Avg Cp Max Cp
453.3 J/kg*K 504.1 J/kg*K 554.9 J/kg*K
Transformer Cabinet Model
Cabinet Simulations
Steady state conditions with a uniform air flow across the cabinet’s outer surface
Transient simulation with no forced flow using:Maximum specific heatMinimum specific heat
Cabinet Temperature Profile: Steady State
Front View of Air Velocity Profile:
Steady State
Side View of Air Velocity Profile:
Steady State
Cabinet Simulation Results
Lumped Parameter Simulations (1/5)
Objective:Create a theoretical model of the EAB
room’s thermal activityProvide an alternative solution method that
will predict air heat up rate.○ Provide confidence in computational model.
Allow an additional means of connecting the simulation results with the experimental results.
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In our current analytical approach, the room is reduced to two heat-storing masses, the cabinets and the air. From the basic equation for heat storage,
two differential equations can be derived for the air
temperature and cabinet surface temperature:
Lumped Parameter Simulations (2/5)
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d
dtTair
hA(Tm Tair )M air Cpa
andd
dtTm
Pg hA(Tm Tair )Mm Cpm
Lumped Parameter Simulations (3/5)
The two equations on the previous slide can be arranged in a heterogeneous linear system of equations, which can be solved simultaneously through matrix methods to yield:
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Tair C1 C2e2 t
Pg t
M airCpa MmCpm1
2
PgM airCpa MmCpm
and
Tm C1 C2M airCpaMmCpm
e2 t Pg t
M airCpa MmCpmPg2
M airCpaMmCpm
M airCpa MmCpm
Where: 2air a m m
hA hA
M Cp M Cp
is the second eigenvalue. (λ1 = 0)
Lumped Parameter Simulations (4/5)
To confirm simulation validity, geometric parameters were taken from SolidWorks model
Pg = heat generation = 85700 W = 292400 Btu/hr
Mair = mass of air in room = 5657 lbs
Mm = mass of cabinets = 1638000 lbs
Cpa = air heat capacity = 0.241 Btu/lb °F
Cpm = metal heat capacity = .117 Btu/lb °F
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Lumped Parameter Simulations (5/5)
Once all parameters are known, the constants C1 and C2 can be determined from initial conditions (t = 0). Initial conditions used:
Tair(0) = 63.4 °F and
Tm(0) = 181.3 °F
Once constants are known, equation for Tair = 104°F can be solved for t, which may be used to determine Tm at that time
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From SS simulation under normal operating conditions
Analytical Solution Assumptions Uniform heat generation. The convection coefficient does not
vary spatially. The convection coefficient is fairly
constant over the temperature range.
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Overall Approaches
Three approaches:Perform calculations by hand/in Excel
spreadsheet.Model simplified version in FloWorks with
cabinets lumped together.Use differential equation solver ODE45 in
MATLAB
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Analytical Solution Results Hand Calculations/Excel file (with h = 6
W/m2 °C = 122.4 Btu/hr ft2 °F:
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Analytical Solution Results
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•Simplified FloWorks Simulation (h calculated automatically by FloWorks a CFD package):
Experiment Overview
1. Goal
2. Approach
3. Experimental setup
4. Tests
5. Results
Experiment Goals
Determine thermal conductivity (k) Benchmark the FloWorks CFD package
using experimental results
dx
dTKAQ xx
3.7in
2.5in
2.5in
Experimental Setup ( 1 /2 )
Fourier’s Law
Experiment Setup (2/2 ) Aluminum & steel blocks
2.5x2.5x3.7 in 200 W cartridge heater
Approximately 95% Efficiency
Block system: Cartridge heater and thermocouples are
covered with silicone grease to remove insulating effects of air
4. Tests
Test 1:Insulated aluminum blockPower remains constantDetermine the thermal heat generation and
conductivity (k)
5. Results Test 1:
Temp Deviation at 373.15 (deg C) k avg (W/m*K) k_standard (W/m*K) % Error in k
0.28 190 200 5
Convection experiment using same setup
Conduct testing with different materials Create FloWorks model with the same
material and conditions to benchmark simulation results
Experiment Future Work
Project AccomplishmentsUsed computer simulation results to predict the
heat-up rate of the EAB room.Normal Operating Conditions : ~63°FHalf flow single train failure: ~79°FHVAC chiller failure: ~ 82.6°FTotal HVAC failure: ~19 minutes after total failure
(104°F)Derived equations to analytically calculate the
heat-up rate using lumped parameter model.Heat-up rate: ~7 minutes after total failure (104°F)
Designed an experimental setup that can be easily compared with a Cosmos FloWorks CFD package.
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Andron Creary, Kyle Bowzer, Brent Mayorga, Matthew King, Ryan Bigelow,
George Campa, Jennifer Banegas, Matt Langston, Richard Colunga
QUESTIONS?