Monte Carlo Simulation of Stress Corrosion Cracking in Structural Metal Materials Taking Account of Surface Crack Effects

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According to laboratory accelerated test data, stress corrosion cracking (SCC) in structural metal materials occurs by initiation and coalescence of micro cracks, subcritical crack propagation and multiple large crack formation or final failure under the combination of materials, stress and corrosive environment. In this paper, a Monte Carlo simulation for the process of SCC has been proposed based on the stochastic properties of micro crack initiation and fracture mechanics concept for crack coalescence and propagation. The emphasis in the model is put on the influence of the semi-elliptical surface cracks. The numerical examples for a sensitized stainless steel type 304 indicate the applicability of the present model to the prediction of the SCC behavior in real structures.

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Periodical:

Key Engineering Materials (Volumes 353-358)

Edited by:

Yu Zhou, Shan-Tung Tu and Xishan Xie

Pages:

1068-1071

Citation:

K. Tohgo et al., "Monte Carlo Simulation of Stress Corrosion Cracking in Structural Metal Materials Taking Account of Surface Crack Effects ", Key Engineering Materials, Vols. 353-358, pp. 1068-1071, 2007

Online since:

September 2007

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$38.00

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[7] H. Kitagawa, T. Fujita and K. Miyazawa: ASTM STP 642, (1978), p.98 Fig. 8 Exponential distribution plots for SCC lifetime Time t (Ms).

[3] 2. 5.

[2] 1. 5.

[1] [0] 0. 5 0. 5 1 1. 5 2 2. 5 3 H (t) 0. 9 0. 7 0. 5 0. 3 0. 1 0. 0 Cumulative probability F (t) SCC lifetime amax = 5. 0 mm af = 1. 13 θ f = 0. 667 Time t (Ms).

[3] 2. 5.

[2] 1. 5.

[1] [0] 0. 5 0. 5 1 1. 5 2 2. 5 3 H (t) 0. 9 0. 7 0. 5 0. 3 0. 1 0. 0 Cumulative probability F (t) SCC lifetime amax = 5. 0 mm af = 1. 13 θ f = 0. 667.

[3] 2. 5.

[2] 1. 5.

[1] [0] 0. 5 0. 5 1 1. 5 2 2. 5 3 H (t) 0. 9 0. 7 0. 5 0. 3 0. 1 0. 0 Cumulative probability F (t) SCC lifetime amax = 5. 0 mm af = 1. 13 θ f = 0. 667 Fig. 5 The variation of crack distribution with time. (a) 24 hr (b) 48 hr (c) 96 hr (d) 520 hr (a) 24 hr (b) 48 hr (c) 96 hr (d) 520 hr Fig. 6 Number of cracks as a function of time.

DOI: https://doi.org/10.7554/elife.01699.009

500 1000 1500 2000 2500 3000 3500 0 0. 5 1 1. 5 2 2. 5 3.

0. 5 1 1. 5 2 2. 5.

500 1000 1500 2000 2500.

[24] 4896 480 240 720 Time t (Ms) Number of cracks per 100 mm2 t (hr) CBB test result.

[3] 3000 3500 from exponential distribution The number of cracks.

500 1000 1500 2000 2500 3000 3500 0 0. 5 1 1. 5 2 2. 5 3.

0. 5 1 1. 5 2 2. 5.

500 1000 1500 2000 2500.

[24] 4896 480 240 720 Time t (Ms) Number of cracks per 100 mm2 t (hr) CBB test result.

[3] 3000 3500 from exponential distribution The number of cracks from exponential distribution The number of cracks Fig. 7 Maximum crack length as a function of time Maximum crack length 2a (mm).

0. 5 1 1. 5 2 2. 5.

[2] [4] [6] 824 4896 480 240 720 Time t (Ms) t (hr).

[3] [10] [12] [0] [2] [4] [6] [8] [10] [12] 0 0. 5 1 1. 5 2 2. 5 3 Maximum crack length 2a (mm).

0. 5 1 1. 5 2 2. 5.

[2] [4] [6] 824 4896 480 240 720 Time t (Ms) t (hr).

[3] [10] [12] [0] [2] [4] [6] [8] [10] [12] 0 0. 5 1 1. 5 2 2. 5 3.

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