Reliability Prediction of a Return Thermal Expansion Joint

1. 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

2. 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.

3. Outline Reliability Prediction Method Reliability vs. Life Power Generation System Fatigue Life PDF Stochastic FEM Results CFD Model Stochastic CFD Simulation FEM Simulation 3

4. Reliability Prediction Method CFD, FEM, Fatigue, & MCS are integrated Physics-based reliability prediction method Several tools are linked to predict reliability

5. Power Generation System Gas Turbine Supply Expansion Joint Heat Exchanger Return Expansion Joint Moisture Separator The reliability Prediction procedure is applied to the Return Expansion Joint Model 5

6. 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

7. 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 7

8. Stochastic CFD Simulation Stochastic CFD simulation determines the Probability Density Function of the Air Heat Transfer Coefficient OUTPUT PARAMETERS 8

9. 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

10. FEM Simulation/Output Transient thermal gradients induces variable thermal stresses Transient Stress Distribution • Thermal stress depends on: • - Material thermal expansion • Material Elasticity • Temperature gradient

11. Stochastic FEM Results Max thermal stress is calculated based on transient thermal analysis Stress reaches a peak point then stabilizes to the steady-state value Max Transient Thermal Stress 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) Reliability is calculated using Life PDF Fatigue Life PDF 11

12. Conclusions 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. 12

13. ACKNOWLEDGMENT The authors would like to acknowledge support for this research provided by GE Energy, Houston, TX.

14. Thank You Questions?