Upload
nelson-wilfrid-hutchinson
View
214
Download
0
Tags:
Embed Size (px)
Citation preview
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
Reliability PredictionMethod
CFD Model
Stochastic CFD Simulation FEM Simulation
Fatigue Life PDF
Stochastic FEM Results
Outline
3
Power Generation System
Reliability vs. Life
Reliability Prediction Method
Physics-based reliability prediction method Several tools are
linked to predict reliability
CFD, FEM, Fatigue, & MCS are integrated
4
Power Generation System
The reliability Prediction procedure is applied to the Return Expansion Joint Model
Supply Expansion Joint
Heat Exchanger
Moisture Separator
Return Expansion Joint
Gas Turbine
5
CFD ModelReturn 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
ParameterAir Temp Air Flow Air Pressure
(°C) (kg/s) (kPa)
Weibull Exponent 2 3 4
Weibull Characteristic Value 130 140 310
Mean 122 134 300
Standard Deviation 11.7 15.2 35.1
INPUT PARAMETERS
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
7
Stochastic CFD SimulationStochastic CFD simulation determines the Probability Density Function of the Air Heat Transfer Coefficient
ParameterAir HTC
(W/m2 °C)
Mean 1274
Standard Deviation 149
Minimum 690
Maximum 1831
OUTPUT PARAMETERS
8
FEM Simulation
FEM Hexagonal Mesh of Return Joint
FEM INPUT PARAMETERSCHARACTERISTICS
ParameterAir Temp.
(°C)Air HTC
(W/m2 °C)
Minimum 19.2 690
Maximum 457 1831
Mean 216 1274
Standard Deviation
37.3 149
Film Coefficient Distribution is imposed as Boundary Condition onto the FEM Model
Operational & Environmental Variablesdistributions are used for FEM Iterations 9
FEM Simulation/Output
Thermal stress depends on:
- Material thermal
expansion
- Material Elasticity
- Temperature gradient 10
Transient Stress Distribution
Transient thermal gradients inducesvariable thermal stresses
Fatigue life is calculated based on Max Stress
As a result of input uncertainty,Life is in the form of a ProbabilityDensity Function (PDF)
Reliability is calculated using Life PDF
Stochastic FEM Results
11
Max Transient Thermal Stress
Fatigue Life PDF
Max thermal stress is calculatedbased on transient thermal analysis
Stress reaches a peak point then stabilizes to the steady-state value
The implemented reliability prediction method can easily be used to predict the reliability of return expansion joints by means of numerical physics-based 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.
Conclusions
12
ACKNOWLEDGMENTThe authors would like to acknowledge support for this
research provided by GE Energy, Houston, TX.
13
Thank You
Questions?