Transcript Presentation at GE

Reliability Prediction of a Return Thermal
Expansion Joint
O. Habahbeh*, D. Aidun**, P. Marzocca**
* Mechatronics Engineering Dept.,
University of Jordan, Amman, Jordan
** Mechanical & Aeronautical Engineering
Dept., Clarkson University, New York, USA
Jordan International Energy Conference (JIEC) 2011 – Amman, Jordan
20-22 September, 2011
Motivation
• It is required to predict the reliability of a critical thermal
component (return expansion joint).
• Assessment process should be conducted during the design
phase of the component.
• The state-of-the-art does not provide a full answer to the
problem, as it deals with transient startup and contains fluid as
well as structure elements.
2
Outline
Reliability
Prediction
Method
MCS2 Fatigue Life
Distribution &
Reliability
Reliability vs. Life
FEM - Thermal
Stress &
Fatigue Life
Power
Generation
System
CFD Model
Stochastic CFD Simulation
MCS1 - HTC's
Distributions
CFD - HTC's
Fatigue Life
PDF
Stochastic
FEM Results
FEM Simulation
3
Reliability Prediction Method
CFD, FEM, Fatigue, &
MCS are integrated
Physics-based reliability
prediction method
Several tools are linked
to predict reliability
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Power Generation System
The reliability Prediction procedure is applied to the Return Expansion
Joint Model
Supply Expansion Joint
Heat
Exchanger
Gas
Turbine
Return Expansion Joint
Moisture
Separator
5
CFD Model
Return Expansion Joint CFD Mesh
1.3 Million Finite
Volume Elements:
Tetrahedrons,
Pyramids,
& Prisms
Internal Air flow
while outside
surface is insulated
6
Stochastic CFD Simulation
CFD simulation is conducted for the return expansion joint to find the Heat
Transfer Coefficient
Air Heat Transfer Coefficient is affected by:
- Operational variables such as Flow Velocity, Temperature, & Pressure
- Environmental variables such as outside air temperature and pressure
Monte Carlo Simulation is used to generate PDF of Heat transfer coefficient
INPUT PARAMETERS
Air Temp
Air Flow
Air Pressure
(°C)
(kg/s)
(kPa)
2
3
4
Weibull Characteristic Value
130
140
310
Mean
122
134
300
Standard Deviation
11.7
15.2
35.1
Parameter
Weibull Exponent
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Stochastic CFD Simulation
Stochastic CFD simulation determines the Probability Density Function
of the Air Heat Transfer Coefficient
OUTPUT PARAMETERS
Parameter
Air HTC
(W/m2 °C)
Mean
1274
Standard Deviation
149
Minimum
690
Maximum
1831
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FEM Simulation
Film Coefficient Distribution is imposed as Boundary
Condition onto the FEM Model
FEM Hexagonal Mesh of Return Joint
FEM INPUT PARAMETERS
CHARACTERISTICS
Operational & Environmental Variables
distributions are used for FEM Iterations
Parameter
Air Temp.
(°C)
Air HTC
(W/m2 °C)
Minimum
19.2
690
Maximum
457
1831
Mean
216
1274
Standard
Deviation
37.3
149
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FEM Simulation/Output
Transient thermal gradients induces
variable thermal stresses
Transient Stress Distribution
Thermal stress depends on:
- Material thermal expansion
- Material Elasticity
- Temperature gradient
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Stochastic FEM Results
Max Transient Thermal Stress
Max thermal stress is calculated
based on transient thermal
analysis
Stress reaches a peak point then
stabilizes to the steady-state
value
Fatigue life is calculated based
on Max Stress
As a result of input uncertainty,
Life is in the form of a Probability
Density Function (PDF)
Fatigue Life PDF
Reliability is calculated
using Life PDF
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Conclusions
The implemented reliability prediction method can easily be used to predict
the reliability of return expansion joints by means of numerical physicsbased modeling.
By implementing stochastic CFD and FEM analyses, uncertainties of
operational and environmental conditions such as flow velocity and
temperature can be reflected into the reliability prediction process.
Transient thermal analysis produces variable thermal stress. Therefore,
critical stress is determined by investigating the whole transient phase.
This integrated reliability prediction method is a powerful method for
designing return expansion joints with optimum performance and reliability.
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ACKNOWLEDGMENT
The authors would like to acknowledge support for this
research provided by GE Energy, Houston, TX.
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Thank You
Questions?