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Acta Metall Sin  2011, Vol. 47 Issue (9): 1159-1166    DOI: 10.3724/SP.J.1037.2011.00039
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STOCHASTIC PROCESS-BASED MODELING METHOD FOR PITTING CORRSION OF Ni-BASED ALLOY 690
ZHOU Binghai, ZHAI Ziqing
1) School of Mechanical Engineering, Tongji University, Shanghai 201804
2) School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240
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ZHOU Binghai ZHAI Ziqing. STOCHASTIC PROCESS-BASED MODELING METHOD FOR PITTING CORRSION OF Ni-BASED ALLOY 690. Acta Metall Sin, 2011, 47(9): 1159-1166.

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Abstract  Pitting corrosion is one of the most serious modes of localized corrosions that can cause deteriorations of Ni-based alloy 690 steam generator tubes. Because the initiation and propagation of pitting are subject to interactions of different factors, it is a stochastic process. It is almost impossible to draw any deterministic models for formations of the processes. To efficiently model the formation process of pitting, firstly, experimental schemes of pitting formations were designed. Based on stochastic theory, combined experimental data and analyzing results, a modeling method was presented with stochastic processes. Namely, non-homogeneous Poisson process (NHPP) was used to model the pitting initiation process and non-stationary Gamma model was used to set up pitting propagation model. A systemic method was built for pitting corrosion failure of the Ni-based alloy 690 tubes, including experimental method, modeling method and simulation method. Finally, the stochastic degradation failure of the Ni-based alloy 690 tubes was simulated. The probability distribution of the lifespan of the Ni-based alloy 690 tubes was obtained under different working environments. The results showed that the proposed modeling method is valid to analyze the initiation and propagation of pitting of the tubes according to different combinations of modeling parameters. It is important for the lifespan prediction and development of relevant maintenance plans of the tubes.
Key words:  Ni-based alloy 690      pitting corrosion      stochastic process      process modeling      Monte Carlo simulation     
Received:  17 January 2011     
Fund: 

Supported by National Natural Science Foundation of China (No.71071115) and High Technology Research and Development Program of China (Nos.2009AA043403 and 2009AA043000)

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https://www.ams.org.cn/EN/10.3724/SP.J.1037.2011.00039     OR     https://www.ams.org.cn/EN/Y2011/V47/I9/1159

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