Long-Term Prediction of Atmospheric Corrosion Loss in Various Field Environments

被引:12
|
作者
Cai, Yi-kun [1 ]
Zhao, Yu [1 ]
Ma, Xiao-bing [1 ]
Zhou, Kun [2 ]
Wang, Hao [3 ]
机构
[1] Beihang Univ, Sch Reliabil & Syst Engn, 37 Xueyuan Rd, Beijing 100191, Peoples R China
[2] Southwest Inst Technol & Engn, 33 Yuzhou Rd, Chongqing 400041, Peoples R China
[3] Syst Engn Res Inst, 1 Fengxian East Rd, Beijing 100094, Peoples R China
基金
中国国家自然科学基金;
关键词
artificial neural network; atmospheric corrosion prediction; power-linear model; response surface method; SALT AEROSOL PROXIES; RELATIVE-HUMIDITY; NEURAL-NETWORK; RESPONSE-SURFACE; CARBON-STEEL; ZINC; TEMPERATURE; POLLUTION; CLIMATE; ALLOYS;
D O I
10.5006/2706
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper deals with the prediction of long-term atmospheric corrosion in different field environments using the power-linear function. A method for the calculation of exponent n and stationary corrosion rate a in the power-linear function is proposed based on the 1- and 8-y corrosion loss results (C-1 and C-8 ) of the ISO CORRAG program. The response surface method and the artificial neural network methodology are used to obtain the accurate estimation of C-1 and C-8 in different locations using environmental variables. Considering the uncertainty of the model and the experimental data, the confidence intervals of n and a are also calculated. It is shown that the long-term predictions obtained by the proposed method coincide with the actual corrosion loss within +/- 30% relative error. The estimations for the range of the long-term corrosion loss are also reliable. The proposed method is helpful in extrapolating the knowledge of corrosion management to different field environments where corrosion data are not available.
引用
收藏
页码:669 / 682
页数:14
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