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Research Papers

Assessment of Thermal Fatigue Predictions of Pipes With Spectral Methods

[+] Author and Article Information
Oriol Costa Garrido

Mem. ASME
Reactor Engineering Division,
Jožef Stefan Institute,
Jamova cesta 39,
Ljubljana 1000, Slovenia
e-mail: Oriol.Costa@ijs.si

Samir El Shawish

Reactor Engineering Division,
Jožef Stefan Institute,
Jamova cesta 39,
Ljubljana 1000, Slovenia
e-mail: Samir.ElShawish@ijs.si

Leon Cizelj

Mem. ASME
Reactor Engineering Division,
Jožef Stefan Institute,
Jamova cesta 39,
Ljubljana 1000, Slovenia
e-mail: Leon.Cizelj@ijs.si

1Corresponding author.

Manuscript received October 17, 2016; final manuscript received May 9, 2017; published online July 31, 2017. Assoc. Editor: Asif Arastu.

ASME J of Nuclear Rad Sci 3(4), 041001 (Jul 31, 2017) (8 pages) Paper No: NERS-16-1142; doi: 10.1115/1.4036736 History: Received October 17, 2016; Revised May 09, 2017

Large sets of fluid temperature histories and a recently proposed thermal fatigue assessment procedure are employed in this paper to deliver more accurate statistics of predicted lives of pipes and their uncertainties under turbulent fluid mixing circumstances. The wide variety of synthetic fluid temperatures, generated with an improved spectral method, results in a set of estimated distributions of fatigue lives through linear one-dimensional (1D) heat diffusion, thermal stress estimates, and fatigue assessment codified rules. The results of the fatigue analysis indicate that, in order to avoid the inherent uncertainties due to comparatively short fluid temperature histories to the estimated fatigue lives, a conservative safe design against thermal fatigue could be attempted with the lower bounds of the predicted life distributions, such as the 5% probability life (5% of samples fail). The impact of the convection heat transfer coefficient on the predictions is also studied in a sensitivity analysis. This represents a detailed attempt to correlate the uncertainties in the physical fluid mixing conditions and heat transfer to the estimated fatigue life using spectral methods.

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References

Figures

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Fig. 1

Sketch of the error function evaluation in the time domain (top) and in the distribution of fluid temperatures (bottom). Tf using random (full line) and optimal (dashed line) set of phases φj.

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Fig. 2

Temperature variation space limited by the upper bound (Max_01) and PSD constrained (Max_PSD) curves. Experimental data points in circles [19,20] and fatigue assessment points in labeled squares.

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Fig. 3

Fatigue design curve for austenitic stainless steel in air proposed in NUREG/CR-6909 [25] and adopted by ASME

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Fig. 4

Examples of fluid and pipe surface temperatures and stress distributions for selected assessment points in Fig. 2, such as: (a) point A, (b) point E, (c) point C, and (d) point SIN. For a ΔT  = 160 °C, σ̃  = 0.5 corresponds to σ  ≈ 330 MPa (Eq. (10) and Table 1).

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Fig. 5

Distributions of fatigue lives (boxes and bars), average fatigue lives (black squares), and 5–95% probabilities predicted for fluid temperature histories characteristic of the assessment points in Fig. 2. The dashed lines mark the Civaux leakage initiation time.

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Fig. 6

Influence of signal time-length on the scatter of fatigue life predictions. The dashed line in (a) marks the Civaux leakage initiation time.

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Fig. 7

Comparison of the presented fatigue assessment results with the SIN method

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Fig. 8

Heat transfer coefficient and fluid temperature difference effects on fatigue life predictions

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Fig. 9

Average fatigue life predictions for different levels of temperature fluctuations and heat transfer coefficients assuming ΔT  = 160 °C

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