核电结构材料应力腐蚀开裂裂纹扩展速率预测

发布时间：2018-01-14 17:36

  本文关键词：核电结构材料应力腐蚀开裂裂纹扩展速率预测　出处：《天津大学》2014年博士论文　论文类型：学位论文

  更多相关文章： 环境耦合致裂模型 人工神经网络模型 应力腐蚀开裂 Alloy 600 304不锈钢 裂纹扩展速率

【摘要】：轻水堆核电站冷却回路材料的应力腐蚀开裂是威胁核电站安全运行的重要因素之一。由于该现象的严重性,普遍性以及由此而引发的各种安全,经济问题,所以有必要建立一种预测应力腐蚀开裂裂纹扩展速率的模型,这种模型的建立将有益于核电站经济效益的提升,同时增加核电站的安全性。因为这种模型能够指导核电站以合适的方式运行,在降低风险的同时节约不必要的开支。建立了沸水堆环境下304不锈钢沿晶应力腐蚀开裂裂纹扩展速率数据库。建立了适用于该环境下裂纹扩展速率预测的人工神经网络模型,并使用实验数据对其训练。结果表明,人工神经网络能够有效地预测多种不同变量对裂纹扩展速率的影响。基于神经网络的训练结果,对环境耦合致裂模型进行改进,使其能够预测敏化度对裂纹扩展速率的影响。环境耦合致裂模型和人工神经网络模型的预测结果吻合良好。通过对影响变量进行敏感性分析,得出沸水堆环境中304不锈钢的应力腐蚀开裂主要是一种电化学现象,同时机械参数和材料微观结构对其也有重要影响。建立了压水堆环境中alloy 600沿晶应力腐蚀开裂裂纹扩展速率的数据库。建立相应的人工神经网络模型并使用实验数据对其训练。结果表明,人工神经网络模型较好的预测了各个影响变量对裂纹扩展速率的影响作用。对环境耦合致裂模型进行扩展,并将其应用于压水堆环境中alloy 600的应力腐蚀开裂预测。预测结果很好的解释了应力强度因子、温度和屈服强度等对裂纹扩展速率的影响。使用fuzzy curve方法对alloy 600的数据分析得出,压水堆环境下alloy 600的沿晶应力腐蚀开裂机械参数作用明显,而电化学参数和微观结构的影响次之。使用环境耦合致裂模型分析了沸水堆环境中304不锈钢沿晶应力腐蚀过程中影响变量的随机误差对裂纹扩展速率分布的影响。结果表明,只有腐蚀电位的随机误差能够改变裂纹扩展速率分布的形式,而当所有的影响变量服从正态分布时,相应的裂纹扩展速率的分布也服从正态分布。
[Abstract]:Stress corrosion cracking (SCC) of LWR cooling loop materials is one of the important factors threatening the safe operation of nuclear power plants. Due to the seriousness of the phenomenon, universality and various safety and economic problems. Therefore, it is necessary to establish a model to predict the stress corrosion cracking crack growth rate, which will benefit the economic efficiency of nuclear power plant. At the same time increase the safety of nuclear power plants, because this model can guide the operation of nuclear power plants in the right way. The database of crack growth rate of 304 stainless steel intergranular stress corrosion cracking under boiling water reactor environment was established, and the person suitable for prediction of crack growth rate in this environment was established. Neural network model. The results show that the artificial neural network can effectively predict the influence of different variables on the crack growth rate. The environmental coupling cracking model is improved. It can predict the effect of sensitivity on crack growth rate. The prediction results of environmental coupled fracture model and artificial neural network model are in good agreement. Sensitivity analysis of the influence variables is carried out. It is concluded that the stress corrosion cracking of 304 stainless steel in boiling water reactor environment is mainly an electrochemical phenomenon. At the same time, the mechanical parameters and the microstructure of materials also have an important effect on it. The alloy in PWR environment has been established. The corresponding artificial neural network model is established and the experimental data are used to train the 600 intergranular stress corrosion cracking crack growth rate database. The artificial neural network model can well predict the effect of each influence variable on the crack growth rate. It is applied to predict the stress corrosion cracking of alloy 600 in PWR environment. The prediction results explain the stress intensity factor well. The effect of temperature and yield strength on the crack growth rate is analyzed by using fuzzy curve method to analyze the data of alloy 600. The mechanical parameters of intergranular stress corrosion cracking (SCC) of alloy 600 under pressurized water reactor (PWR) environment are obvious. The influence of electrochemical parameters and microstructure on the crack growth rate distribution of 304 stainless steel during intergranular stress corrosion (SCC) was analyzed by using the environmental coupled fracture model (ECCM) in the process of intergranular stress corrosion (SCC) of 304 stainless steel in boiling water reactor (BWR). Impact. Results show. Only the random error of corrosion potential can change the form of crack growth rate distribution, and when all the influence variables are applied to normal distribution, the distribution of corresponding crack growth rate is also normal distribution.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM623.7
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本文编号：1424620