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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 4 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 7 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 8 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 9 FEM Simulation/Output Transient thermal gradients induces variable thermal stresses Transient Stress Distribution Thermal stress depends on: - Material thermal expansion - Material Elasticity - Temperature gradient 10 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 11 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. 12 ACKNOWLEDGMENT The authors would like to acknowledge support for this research provided by GE Energy, Houston, TX. 13 Thank You Questions?