郑如炎, 彭飞, 牟金磊. 基于GM(1,N)模型的碳钢腐蚀速率预测[J]. 中国舰船研究, 2018, 13(1): 60-64, 113. DOI: 10.3969/j.issn.1673-3185.2018.01.009
引用本文: 郑如炎, 彭飞, 牟金磊. 基于GM(1,N)模型的碳钢腐蚀速率预测[J]. 中国舰船研究, 2018, 13(1): 60-64, 113. DOI: 10.3969/j.issn.1673-3185.2018.01.009
ZHENG Ruyan, PENG Fei, MU Jinlei. GM(1, N)model-based prediction of carbon steel corrosion rate[J]. Chinese Journal of Ship Research, 2018, 13(1): 60-64, 113. DOI: 10.3969/j.issn.1673-3185.2018.01.009
Citation: ZHENG Ruyan, PENG Fei, MU Jinlei. GM(1, N)model-based prediction of carbon steel corrosion rate[J]. Chinese Journal of Ship Research, 2018, 13(1): 60-64, 113. DOI: 10.3969/j.issn.1673-3185.2018.01.009

基于GM(1,N)模型的碳钢腐蚀速率预测

GM(1, N)model-based prediction of carbon steel corrosion rate

  • 摘要:
      目的  碳钢在海洋环境下的腐蚀速率预测非常复杂和模糊。针对碳钢腐蚀速率的灰色预测模型精度普遍不高的现状,为提高腐蚀速率预测模型的精度,
      方法  通过船用碳钢腐蚀速率和海洋环境因素的灰关联分析,得到影响船用碳钢腐蚀速率的关键因素,进而建立具有较高精度的GM(1,N)腐蚀速率预测模型。
      结果  实例分析表明,在青岛、厦门、舟山和榆林海域影响船用碳钢腐蚀速率的主要环境因素是海水温度、海生物附着量、pH值和海水盐度,在此基础上建立的GM(1,5)模型具有较高的精度且计算量较小。
      结论  GM(1,N)模型可以有效预测碳钢等船用金属的腐蚀速率,可为船用金属腐蚀寿命的研究提供理论支撑,具有一定的参考价值。

     

    Abstract:
      Objectives  The corrosion rate prediction of carbon steel in marine environment is very complicated and uncertain.
      Methods  To improve the accuracy of prediction model in view of the low precision of grey prediction model for corrosion rate of carbon steel at present stage, the key factors which affect the corrosion rate can be concluded from the grey theory analysis of marine environment and corrosion rate of carbon steel, and then the GM(1, N) model which can predict the corrosion rate of carbon steel is established.
      Results  According to the case analysis, the main factors that affect the corrosion rate in seaareas of Qindao, Xiamen, Zhousan, Yulin coastal region are seawater temperature, biofouling, pH value and salinity, and based on the above, the establishment of GM(1, 5) model possesses higher precision and less computational costs.
      Conclusions  The research shows that the GM(1, N) model can predict the corrosion rate of carbon steel effectively, and also provide a theoretical basis for the prediction of residual life of carbon steel.

     

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