Reliability Prediction of a Return Thermal Expansion Joint

Transcription

1 Reliability Prediction of a Return Thermal Expansion Joint O.M. Al-Habahbeh 1,*, D.K. Aidun 2, P. Marzocca 2 1 Mechatronic Engineering Dept., The University of Jordan, Amman, Jordan 2 Mechanical & Aeronautical Eng. Dept., Clarkson University, PO Box 5725, Potsdam, NY USA * Corresponding author. Tel: , Fax: , o.habahbeh@ju.edu.jo, Abstract: An efficient reliability assessment approach is used to estimate the reliability of a return expansion joint. This component is part of a large power generation system. In order to perform the reliability assessment task, Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Fatigue analysis, and Monte Carlo Simulation (MCS) tools are integrated. The process starts with CFD simulation to determine the convective terms necessary for the transient FEM thermal analysis. The thermal analysis provides maximum thermal stress whereby the fatigue life of the component is estimated. As a result of input parameters uncertainty, the calculated life is in the form of a Probability Density Function (PDF), which enables the calculation of the reliability of the component. The application of this reliability prediction procedure to the return expansion joint can be used to enhance the design and operation of the component by uncovering under-design or over-design. Under-design warrants further studies using the same method to determine how to enhance the reliability. On the other hand, over-design can be eliminated to reduce the manufacturing cost of the component. Furthermore, various alternative designs and operational scenarios can be studies using this model. Keywords: Integrated Reliability Prediction, CFD Simulation, FEM Simulation, Stress-Life Method, Return Expansion Joint. 1. Introduction Reliability is defined as the ability of a system to operate under normal and abnormal conditions subject to a defined failure rate and for a specific life time [1]. While reliability can be determined by accelerated life testing, it is more cost-effective to predict the reliability of the system early during the design phase. Many researchers have dealt with reliability of engineering systems. However, most of this work is related to structural systems not involving fluid interaction. Basaran and Chandaroy [2] determined the reliability of a solder joint subjected to thermal cycling loading by FEM instead of laboratory tests. Vandevelde et al. [3] compared two solder joints reliabilities using non-linear FEM. Asghari [4] obtained heat transfer coefficient (h) for surfaces in contact with air flow by running a steady-state CFD model. Bedford et al. [5] used a CFD-based time-averaged heat transfer coefficient (h) for thermal stress analysis. Stress in thermal structures can be determined by FEM simulation

2 [6]. However, thermal stress affects service life more than mechanical stress; this effect lowers life by a factor up to 2.5 [7]. Therefore, this factor is integrated into the simulations. The alternating stress method is used to relate the thermal stress to the number of cycles. The S-N curve of the material is used for this process. A thermo-mechanical study considering only the steady-state operation and not the pulsed heating effects is not enough. Additional study is necessary to consider the pulsed heating effects in the form of additional stress [8]. Thermal shock shares many characteristics with thermally-induced stress, except that its behavior is time dependent as well as spatially dependent. During the operation of a thermal system, the rapid start-up and shut-down leads to a large temperature difference between the surface of a material and the mean body temperature [9]. Ichiro et al. [10] introduced the development of thermal transient stress charts for screening evaluation of thermal loads in structural design works of fast reactor components. Satyamurthy et al. [11] used FEM to calculate the transient thermal stresses in a long cylinder with a square cross section resulting from convective heat transfer. Constantinescu et al. [12]. presented a computational approach for the lifetime assessment of structures under thermomechanical loading. The proposed method is composed of a fluid flow, a thermal and a mechanical finite element computation, as well as fatigue analysis. However, transient analysis was not considered in their work. Liu et al. [13] investigated the Thermal-Mechanical Fatigue (TMF) behavior of cast nickel-based super-alloy under In-Phase (IP) and Out-of- Phase (OP) loading in the temperature range from 400 to 850 C. At corresponding strain amplitude, the thermal-mechanical fatigue life was lower than that of isothermal fatigue. Gue de et al. [14] set up a probabilistic approach of the thermal fatigue design of nuclear components. It aimed at incorporating all kinds of uncertainties that affect the thermal fatigue behavior. The approach was based on the theory of structural reliability. Beside the probability of failure calculation, the sensitivity of the reliability index to each random

3 variable is estimated. The proposed method is applied to a pipe subjected to thermal loading due to water flow. The results show that it is possible to perform a complete reliability analysis to compute the failure probability. It was observed that the scatter of fatigue data and the heat transfer coefficient are the most important variables in thermal fatigue reliability analysis. Lee and Kim [15] discussed failure mechanisms of electronic packaging subjected to thermal cyclic loads. It was found that mechanical load has longer fatigue life than thermal load. Fatigue Strength Reduction Factor (FSRF) must be assigned during the fatigue analysis. It serves to adjust the stress-life or strain-life curve(s) used in the fatigue analysis. This setting is used to account for a "real world" environment that may be harsher than a rigidly-controlled laboratory environment in which fatigue data was collected [6]. The FSRF can be defined as a reduction of the capacity to bear a certain stress level. Life predictions for fatigue failure generally consist of a good determination of this factor [16]. Article NB-3200 in Section III of the ASME Code provides the following definition for FSRF: Fatigue strength reduction factor is a stress intensification factor which accounts for the effect of a local structural discontinuity (stress concentration) on the fatigue strength [17]. This factor is applied to the alternating stress only and does not affect the mean stress [18]. A FSRF of 2.0 for integral parts and 4.0 for non-integral attachments has been assumed for calculating the amplitude of peak stresses [19]. In this work, an efficient reliability assessment approach is employed to estimate the reliability of a return expansion joint. This component is a critical part of a cooling system for a large gas turbine. The reliability prediction method employed in this work was introduced by the authors in a previous journal paper [20]. A model of the return expansion joint is built, then a CFD analysis of the air flow is conducted, followed by a transient FEM analysis of the

4 structure based on the results of CFD analysis. Finally, fatigue analysis is conducted in conjunction with MCS in order to estimate the reliability of the component. 2. Reliability Prediction Method The Reliability Prediction flow chart is shown in Fig. 1. It consists of two loops connected by the Probability Density Functions (PDF) of heat convection coefficients. The left hand side loop represents the stochastic CFD simulation stage, and the right hand side loop represents the stochastic FEM stage. Fig. 1: Reliability Prediction Method 3. Reliability Prediction of the Return Expansion Joint The Reliability Prediction method is applied to a return expansion joint which is part of an energy subsystem shown in Fig. 2. The system comprises four components; Heat exchanger, moisture separator, and pressure-balanced expansion joints (supply and return). The heat exchanger circuit is installed between the Low Pressure Compressor (LPC) and the High Pressure Compressor (HPC) of the gas turbine. The heat exchanger increases the turbine efficiency by cooling air before it enters the HPC [21]. Therefore, the reliability of these components is critical to the reliability of the gas turbine.

5 Supply Expansion Joint Heat Exchanger Gas Turbine Return Expansion Joint Moisture Fig. 2: Power Generation System [21] The application of the Reliability Prediction method to the heat exchanger circuit is a complex task. Here, the focus will be on the return expansion joint. The employed reliability method depends on the Physics-based Modeling which consists of two phases; CFD for the fluid side and FEM/Fatigue for the solid side. Physics-based modeling is used in conjunction with reliability methods. The main interest in this work is to analyze the transient start-up of the component, and to achieve this goal, the system is modeled using transient analysis. As a result of heat flux, temperature gradients develop and cause thermal stresses in the structure. The calculated heat transfer coefficients are for convections of air, while the mode of heat transfer within the solid is conduction. The accuracy of the solution is verified by refining the meshes in both the CFD and the FEM solutions. The Reliability Prediction method is performed by integrating several software packages. isight and ANSYS/DesignXplorer software are used to simulate the reliability of the component, while ANSYS/CFX /Simulation is used for the physical modeling phase. The physical modeling consists of Computational Fluid Dynamics (CFD) and Finite Element Method (FEM). The stochastic CFD simulation is run by ANSYS/DesignXplorer. On the other hand, isight is interfaced with ANSYS/Simulation using an ANSYS/Workbench Component in order to perform Monte Carlo simulation (MCS), and consequently evaluate the reliability of the component, which is based on thermal stress fatigue as failure criterion.

6 Stochastic CFD Simulation A CAD model of the return expansion joint is prepared for analysis. The model is meshed as shown in Fig. 3. The mesh contains 1,341,000 elements. All input and output parameters of this model are listed in Table 1. Selected CFD random input parameters are shown in Table 1 (marked with asterisks). These parameters were selected based on a sensitivity analysis. The statistical distributions of these parameters are defined so as to perform stochastic CFD analysis. After conducting the CFD analysis, velocity, temperature, and heat transfer coefficient results are obtained. For example, air velocity and temperature distributions are shown in Fig. 4 and Fig. 5 respectively. Some output parameters listed in Table 1 are selected as random variables, and marked with double asterisks. Their statistical distributions are defined in order to perform stochastic FEM simulations. Fig. 3: A Section of the Return Joint CFD Mesh

7 Table 1: CFD Parameters of Return Expansion Joint (REJ) Parameter Explanation Unit REJ Air Flow* Return Expansion Joint Air Flow Rate kg/s REJ Air Pressure Return Expansion Joint Air Pressure kpa REJ Density Return Expansion Joint Density kg/m 3 REJ ID Return Expansion Joint Inner Diameter m REJ OD Return Expansion Joint Outer Diameter m REJ Return Temperature* Return Expansion Joint Convective Ambient Temperature C REJ Specific Heat Return Expansion Joint Specific Heat J/kg C REJ Thermal Conductivity Return Expansion Joint Thermal Conductivity W/m C REJ Thermal Expansion Return Expansion Joint Thermal Expansion 1/ C REJ Outlet Temperature** Return Expansion Joint Temperature at Air Out (Output) C REJ RHTC** Return Expansion Joint Convective Film Coefficient (Output) W/m 2 C Fig. 4: Air Velocity Distribution

8 Fig. 5: Air Temperature Distribution 3.2. Stochastic FEM Simulation The stochastic CFD results are used as input to the FEM analysis. All input and output parameters used in the FEM analysis are listed in Table 2. The mesh used for the FEM analysis is shown in Fig. 6. A transient thermal analysis is conducted and thermal stress is computed at regular intervals to determine the maximum stress. The transient temperature distribution is shown in Fig. 7, and the corresponding thermal stress distribution is shown in Fig. 9. The variation of transient temperature with time is plotted in Fig 8, while the variation of transient stress with time is plotted in Fig. 10. The latter serves to determine when the maximum stress occurs. By performing stochastic iterations at this point of time, the stress results are obtained and used to determine fatigue life for each sample point using Fig. 11. The resulting life distribution is used to obtain the reliability of the model as shown in Fig. 12 and Fig. 13.

9 Fig. 6: FEM Mesh of Return Joint Table 2: FEM Parameters of Return Expansion Joint (REJ) Parameter Explanation Unit REJ Compressive Yield Strength Return Expansion Joint Compressive Yield Strength MPa REJ Convective Ambient Temperature Return Expansion Joint Convective Ambient Temperature C REJ Convective Film Coefficient Return Expansion Joint Convective Film Coefficient W/m 2 C REJ Density Return Expansion Joint Density kg/m 3 REJ ID Return Expansion Joint Inner Diameter m REJ OD Return Expansion Joint Outer Diameter m REJ Poisson's Ratio Return Expansion Joint Poisson's Ratio --- REJ Pressure Magnitude Return Expansion Joint Air Pressure kpa REJ Specific Heat Return Expansion Joint Specific Heat J/kg C REJ TensileUltimateStrength Return Expansion Joint Tensile Ultimate Strength MPa REJ TensileYieldStrength Return Expansion Joint Tensile Yield Strength MPa REJ Thermal Conductivity Return Expansion Joint Thermal Conductivity W/m C REJ Thermal Expansion Return Expansion Joint Thermal Expansion 1/ C REJ Young's Modulus Return Expansion Joint Young's Modulus Pa REJ Maximum Shear Stress Return Expansion Joint Maximum Shear Stress (Output) MPa REJ Life Minimum Return Expansion Joint Fatigue Life (Output) Cycles

10 Fig. 7: Transient Temperature Distribution Fig. 8: Temperatures History

11 Fig. 9: Transient Stress Distribution Fig. 10: Max Transient Thermal Stress 3.3. Stress Life Estimation The maximum transient stress result is used to determine the corresponding stress life of the component. The S-N curve shown in Fig. 11 is used for this purpose. Latin Hypercube technique is used to generate 25 maximum stress points. These points represent the variation in operational and environmental conditions as shown in the CFD and FEM analyses sections.

12 Approximation surface regression is used to generate 10,000 points based on the original 25 points. Those points are used to plot the Life Probability Density Function (PDF) shown in Fig. 12. This PDF is used to calculate the reliability of the component. Fig. 11: Semi-Log S-N Curve of the Model Material [22] Fig. 12: Life PDF

13 Fig. 13: Reliability Calculation using Life PDF 4. 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. Acknowledgment The Authors would like to thank GE Energy, Texas, for their support of this research. References [1] Yang, G., Life Cycle Reliability Engineering, John Wiley & Sons, Inc. pp 1, 232, (2007).

14 [2] Basaran, C., Chandaroy, R., Using Finite Element Analysis for Simulation of Reliability Tests on Solder Joints in Microelectronic Packaging, J. Computers and Structures, V. 74, pp , (2000). [3] Vandevelde, B., Gonzalez, M., Limaye, P., Ratchev, P., Beyne, E., Thermal cycling reliability of SnAgCu and SnPb solder joints: a comparison for several IC-packages, IMEC, Kapeldreef 75, B-3001 Leuven, Belgium, (2004). [4] Asghari, T. A. Transient thermal analysis takes one-tenth the time, Motorola Inc, EDN, (2002). [5] Bedford, F., Hu, X., Schmidt, U., In-cylinder combustion modeling and validation using Fluent, (2004). [6] ANSYS Reference manuals, (2008). [7] Oberg, E., McCauley, C.J., Machinery's Handbook: A Reference Book for the Mechanical Engineer, Industrial Press Inc., ISBN , pp 207, (2004). [8] Boyce, R., Dowell, D. H., Hodgson, J., Schmerge, J. F., Yu, N., Design Considerations for the LCLS RF Gun, Stanford Linear Accelerator Center, LCLS TN 04-4, pp 21, Retrieved from: technotes/lcls-tn-04-4.pdf, (2004). [9] LeMasters, J., Thermal Stress Analysis of LCA-Based Solid Oxide Fuel Cells, Master s Thesis, Georgia Institute of Technology, pp 25, 104, (2004). [10] Ichiro, F., Naoto, K. A., Hiroshi, S., Development of Thermal Transient Stress Charts for Screening Evaluation of Thermal Loads, Retrieved from: (2006). [11] Satyamurthy, K., Singh, J. P., Hasselman, D. P. H., Kamat, M. P., Transient Thermal Stresses in Cylinders with a Square Cross Section Under Conditions of Convective Heat Transfer, Journal of the American Ceramic Society 63 (11-12), , (1980). [12] Constantinescu, A., Charkaluk, E., Lederer, G., Verger, L., A computational approach to thermomechanical fatigue, International Journal of Fatigue V. 26 pp , (2004). [13] Liu, F., Ai, S. H., Wang, Y. C., Zhang, H., Wang, Z. G., Thermal-mechanical fatigue behavior of a cast K417 nickel-based superalloy, International Journal of Fatigue, V. 24, pp , (2002). [14] Gue de, Z., Sudret, B., Lemaire, M., Life-time reliability based assessment of structures submitted to thermal fatigue, International Journal of Fatigue, (2007). [15] Lee, S., Kim, I., Fatigue and Fracture Assessment for Reliability of Electronics Packaging, Korea Advanced Institute of Science & Technology, (2007). [16] Qylafku, G., Azari, Z., Kadi, N., Gjonaj, M., Pluvinage, G., Application of a new model proposal for fatigue life prediction on notches and key-seats, International Journal of Fatigue 21, pp , (1999) [17] Jaske, C. E., Fatigue-Strength-Reduction Factors for Welds in Pressure Vessels and Piping, CC Technologies Laboratories, Inc., Dublin, OH, (2000).

15 [18] Hancq, D. A., Fatigue Analysis Using ANSYS, pp 9. CAE Associates, (2004). [19] Rao, K. R., Editor, Companion Guide to the ASME boiler & Pressure Vessel Code, Volume 2, pp.139, (2002) [20] Al-Habahbeh, O.M., Aidun, D.K., Marzocca, P., Lee, H., Integrated Physics-Based Approach for the Reliability Prediction of Thermal Systems, International Journal of Reliability and Safety, Vol. 5, No. 2, pp [21] Gas turbine data [22] Structural steel fatigue data at zero mean stress, ASME BPV Code, Section 8, Div 2, Table , (1998).