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An improved algorithm to predict the mechanical properties of nuclear grade 316 stainless steel under elevated-temperature liquid sodium
Journal of Nuclear Science and Technology ( IF 1.2 ) Pub Date : 2021-04-27 , DOI: 10.1080/00223131.2021.1918591
Yaonan Dai 1 , Xiaotao Zheng 1 , Jiuyang Yu 1
Affiliation  

ABSTRACT

Nuclear grade 316 stainless steel (SS) is the main material of core internals for liquid sodium-cooled fast reactor (SFR). However, very limited mechanical properties of nuclear grade 316SS under elevated-temperature liquid sodium, including yield strength (YS), ultimate tensile strength (UTS) and total elongation (TE), can be obtained due to long-time consumption and extreme testing condition. Therefore, it is necessary to use limited experimental data to predict mechanical properties of nuclear grade 316SS reliably and efficiently for very long life design up to 60 years. The standardized Euclidean distance was introduced to the radial basis function neural network (RBF-NN) model to develop an improved RBF neural network (IRBF-NN) model, which was trained to solve the problems of back propagation neural network (BP-NN) model. Additionally, the validity of (YS, UTS, TE) about the IRBF-NN model and BP-NN model is evaluated and compared by the absolute relative error (ARE), T-test, F-test, correlation coefficients (R), average absolute error (MAE) and standard deviation (σ). Results clearly illustrate that the artificial neural network (ANN) model is suitable for predicting the mechanical properties of nuclear grade 316SS under elevated-temperature liquid sodium, and the prediction effect of the IRBF-NN model is better than that of the BP-NN model.



中文翻译:

核级316不锈钢高温液态钠力学性能预测的改进算法

摘要

核级316不锈钢(SS)是液态钠冷快堆(SFR)堆芯内件的主要材料。然而,核级316SS在高温液态钠下的力学性能非常有限,包括屈服强度(YS)、极限拉伸强度(UTS)和总伸长率(TE),由于长时间消耗和极端测试条件. 因此,有必要使用有限的实验数据来可靠有效地预测核级 316SS 的机械性能,以实现长达 60 年的超长寿命设计。将标准化欧几里德距离引入径向基函数神经网络(RBF-NN)模型,开发改进的RBF神经网络(IRBF-NN)模型,训练该模型解决反向传播神经网络(BP-NN)的问题模型。此外,R )、平均绝对误差 (MAE) 和标准偏差 ( σ )。结果清楚地表明人工神经网络(ANN)模型适用于预测高温液态钠下核级316SS的力学性能,IRBF-NN模型的预测效果优于BP-NN模型.

更新日期:2021-04-27
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