Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

Yanhui Li; Tongtong Xu; Shuzhong Wang; Balazs Fekete; Jie Yang; Jianqiao Yang; Jie Qiu; Aoni Xu; Jiaming Wang; Yi Xu; Digby D Macdonald

doi:10.3390/ma12030409

Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

Materials (Basel). 2019 Jan 28;12(3):409. doi: 10.3390/ma12030409.

Authors

Yanhui Li^{1

2}, Tongtong Xu³, Shuzhong Wang⁴, Balazs Fekete⁵, Jie Yang⁶, Jianqiao Yang⁷, Jie Qiu⁸, Aoni Xu⁹, Jiaming Wang¹⁰, Yi Xu¹¹, Digby D Macdonald¹²

Affiliations

¹ Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China. yhli19@sina.com.
² Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. yhli19@sina.com.
³ Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China. ttxu17@sina.com.
⁴ Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China. szwang@aliyun.com.
⁵ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. balazs.fekete@berkeley.edu.
⁶ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. mengchunyj@gmail.com.
⁷ Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China. yangjianqiao@stu.xjtu.edu.cn.
⁸ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. qiujie@berkeley.edu.
⁹ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. aonixu@berkeley.edu.
¹⁰ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. wangjiaming@fudan.edu.cn.
¹¹ Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. xuyi@berkeley.edu.
¹² Departments of Nuclear Engineering & Materials Science and Engineering, University of California at Berkeley, Berkeley, CA 94720, USA. macdonald@berkeley.edu.

Abstract

The dependencies of weight gain of 9-12 Cr ferritic-martensitic steels in supercritical water on each of seven principal independent variables (temperature, oxygen concentration, flow rate, exposure time, and key chemical composition and surface condition of steels) have been predicted using a supervised artificial neural network (ANN). The relative significance of each independent variable was uncovered by fuzzy curve analysis, which ranks temperature and exposure time as the most important. The optimized ANN, not only satisfactorily represents the experimentally-known non-linear relationships between the corrosion characteristics of F-M steels and the key independent variables (demonstrating the effectiveness of this technique), but also predicts and reveals that the effects of oxygen concentration on the weight gains, to a certain degree, is influenced by the flow rate and temperature. Finally, according to the ANN predicted-results, departure of oxidation kinetics from the parabolic law, and basic cause of chromium content in steel substrate influencing the corrosion rate, and the synergetic effects of dissolved oxygen concentration, flow rate, and temperature, are discussed and analyzed.

Keywords: F-M steels; artificial neural network; fuzzy curve analysis; oxidation; supercritical water; weight gain.