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.

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Reliability PredictionMethod

CFD Model

Stochastic CFD Simulation FEM Simulation

Fatigue Life PDF

Stochastic FEM Results

Outline

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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

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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

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CFD ModelReturn Expansion Joint CFD Mesh

1.3 Million Finite Volume Elements: Tetrahedrons, Pyramids, & Prisms

Internal Air flow while outside surface is insulated

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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

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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

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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

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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

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ACKNOWLEDGMENTThe authors would like to acknowledge support for this

research provided by GE Energy, Houston, TX.

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Thank You

Questions?