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 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

MCS2 - Fatigue Life Distribution & Reliability FEM - Thermal Stress & Fatigue Life MCS1 - HTC's Distributions CFD - HTC's Reliability Prediction Method 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 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 Parameter Air TempAir FlowAir Pressure (°C)(kg/s)(kPa) Weibull Exponent234 Weibull Characteristic Value Mean Standard Deviation 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 Simulation Stochastic CFD simulation determines the Probability Density Function of the Air Heat Transfer Coefficient Parameter Air HTC (W/m 2 °C) Mean1274 Standard Deviation149 Minimum690 Maximum1831 OUTPUT PARAMETERS 8

FEM Simulation FEM Hexagonal Mesh of Return Joint FEM INPUT PARAMETERS CHARACTERISTICS Parameter Air Temp. (°C) Air HTC (W/m 2 °C) Minimum Maximum Mean Standard Deviation Film Coefficient Distribution is imposed as Boundary Condition onto the FEM Model Operational & Environmental Variables distributions 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 induces variable thermal stresses

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 Stochastic FEM Results 11 Max Transient Thermal Stress Fatigue Life PDF Max thermal stress is calculated based 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

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

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