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Research Papers: Fluid-Structure Interaction

Large Eddy Simulation Analysis of Fluid Temperature Fluctuations at a T-junction for Prediction of Thermal Loading

[+] Author and Article Information
Shaoxiang Qian

Mem. ASME
EN Technology Center,
Engineering Division,
JGC Corporation,
2-3-1 Minato Mirai, Nishi-ku,
Yokohama 220-6001, Japan
e-mail: qian.shaoxiang@jgc.com

Naoto Kasahara

Nuclear Engineering and Management,
School of Engineering,
The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-8656, Japan
e-mail: kasahara@n.t.u-tokyo.ac.jp

1Corresponding author

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received August 27, 2013; final manuscript received July 19, 2014; published online September 15, 2014. Assoc. Editor: Samir Ziada.

J. Pressure Vessel Technol 137(1), 011303 (Sep 15, 2014) (11 pages) Paper No: PVT-13-1148; doi: 10.1115/1.4028067 History: Received August 27, 2013; Revised July 19, 2014

T-junctions are widely used for fluid mixing in power and process plants. Temperature fluctuations generated by the mixing of hot and cold fluids at a T-junction can cause high cycle thermal fatigue (HCTF) failure. The existing Japanese guideline for evaluating HCTF provides margin that varies greatly depending on the case for the evaluation result. Computational fluid dynamics (CFD)/finite element analysis (FEA) coupling analysis is expected to be a useful tool for the more accurate evaluation of HCTF. Precise temperature fluctuation histories are necessary to determine the thermal loads because fatigue damage prediction requires temperature fluctuation amplitudes and their cycle numbers. The present investigation was intended to discover the accurate prediction methods of fluid temperature fluctuations, prior to performing CFD/FEA coupling analysis. Large eddy simulation (LES) turbulence models suitable for the simulation of unsteady phenomena were investigated. The LES subgrid scale (SGS) models used included the standard Smagorinsky model (SSM) and the dynamic Smagorinsky model (DSM). The effects of numerical schemes for calculating the convective term in the energy equation on the simulation results were also investigated. LES analyses of the flow and temperature fields at a T-junction were carried out using these numerical methods. For comparison, the simulation conditions were the same as the experiment in literature. All of the simulation results show the flow pattern of a wall jet with strong flow and temperature fluctuations, as observed in the experiment. The simulation results indicate the numerical schemes have a great effect on the temperature distribution and the temperature fluctuation intensity (TFI). The first-order upwind difference scheme (1UD) significantly underestimates the TFI for each LES SGS model, although it exhibits good numerical stability. However, the hybrid scheme (HS), which is mainly the second-order central difference scheme (2CD) blended with a small fraction of 1UD, can better predict the TFI for each LES SGS model. Furthermore, the DSM model gives a prediction closer to the experimental results than the SSM model, while using the same numerical scheme. As a result, it was found through the systematic investigations of various turbulence models and numerical schemes that the approach using the DSM model and the HS with a large blending factor could provide accurate predictions of the fluid temperature fluctuations. Furthermore, it is considered that this approach is also applicable to the accurate prediction of any other scalar (e.g., concentration), based on the analogy of scalar transport phenomena.

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References

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Figures

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Fig. 1

Geometry of computational model and boundary conditions

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Fig. 2

Meshes for computational model

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Fig. 3

Locations and direction of the lines (along arrows) on the plot in Figs. 4 and 7

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Fig. 4

Distribution of the normalized time-averaged axial velocity along the radial direction

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Fig. 5

Distribution of instantaneous fluid temperature on the vertical cross section along the flow direction at t = 11.0 s

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Fig. 6

Distribution of fluid TFI on the cross section along the flow direction at t = 11.0 s

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Fig. 7

Distribution of fluid TFI along the radial direction

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Fig. 8

Locations and direction of the lines (along arrows) on the plot in Fig. 9

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Fig. 9

Distribution of fluid TFI along the circumferential direction

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Fig. 10

Distribution of the parameter Cs evaluated in the DSM model

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Fig. 11

Location of the temperature sampling point

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Fig. 12

Temporal variation of fluid temperature at the sampling point at 1 mm from the pipe wall, x = 1.0Dm, θ = 30 deg (case 6)

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Fig. 13

PSD of fluid temperature at the sampling point at 1 mm from the pipe wall, x = 1.0Dm, θ = 30 deg (case 6)

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Fig. 14

Distribution of fluid TFI along the radial direction

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Fig. 15

Distribution of fluid TFI along the circumferential direction

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