[1]洪亮,金鑫,刘虓瀚,等.机器学习算法在燃料棒温度性能预测中的应用[J].深圳大学学报理工版,2022,39(5):515-520.[doi:10.3724/SP.J.1249.2022.05515]
 HONG Liang,JIN Xin,LIU Xiaohan,et al.Application of machine learning algorithm in the prediction of fuel rod temperature performance[J].Journal of Shenzhen University Science and Engineering,2022,39(5):515-520.[doi:10.3724/SP.J.1249.2022.05515]
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机器学习算法在燃料棒温度性能预测中的应用()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第5期
页码:
515-520
栏目:
电子与信息科学
出版日期:
2022-09-16

文章信息/Info

Title:
Application of machine learning algorithm in the prediction of fuel rod temperature performance
文章编号:
202205005
作者:
洪亮 金鑫 刘虓瀚 卫小艳
中广核研究院有限公司,广东深圳 518026
Author(s):
HONG Liang JIN Xin LIU Xiaohan and WEI Xiaoyan
Nuclear Power Technology Research Institute, Shenzhen 518026, Guangdong Province, P. R. China
关键词:
核能机器学习燃料棒温度JASMINE软件k近邻决策树AdaBoost算法
Keywords:
nuclear energy machine learning fuel rod temperature JASMINE software k-nearest neighbor decision tree AdaBoost algorithm
分类号:
TL99;TP181
DOI:
10.3724/SP.J.1249.2022.05515
文献标志码:
A
摘要:
为对燃料棒温度性能进行有效预测,分别基于k近邻、决策树和AdaBoost机器学习算法建立预测模型.利用燃料棒性能分析软件JASMINE的输入参数和计算结果以及数据的特征工程构建模型的训练和测试数据集.采用包含芯块与包壳类型、轴向高度、局部功率、包壳腐蚀厚度和堆芯入口温度6个特征参数的训练数据集对模型进行训练.采用测试数据集对包壳外表面温度和芯块中心温度进行预测.结果表明,基于AdaBoost算法建立的模型对包壳外表面温度和芯块中心温度的预测结果的均方误差分别为0.605 ℃和8.347 ℃,平均绝对误差分别为0.273 ℃和3.814 ℃.对比预测值与目标值,AdaBoost算法对包壳外表面温度预测的最大偏差为3 ℃,芯块中心温度的预测偏差大部分小于10 ℃.基于AdaBoost算法建立的模型对燃料棒温度性能具有较高的预测精度.
Abstract:
In order to predict the fuel rod temperature performance effectively, we establish a series of machine learning models based on k-nearest neighbor, decision tree and AdaBoost algorithms. The input parameters and calculation results of fuel rod performance analysis software JASMINE and data feature engineering are used as the training and test data for the machine learning models. The models are trained by the training data set which includes the characteristic parameters such as pellet and cladding type, axial height, local power, cladding corrosion thickness and core inlet temperature. After training, the models use the test data to predict the cladding outside surface temperature and pellet center temperature. The prediction results show that the model based on AdaBoost algorithm has the best prediction performances, and the mean square errors of cladding outside surface temperature and pellet center temperature are 0.605 ℃ and 8.347 ℃, respectively, and the average absolute errors are 0.273 ℃ and 3.814 ℃, respectively. Comparing the predicted values with the target values, the maximum deviation of Adaboost algorithm for the cladding outside surface temperature is 3 ℃, and the most of the prediction deviation of the pellet center temperature is less than 10 ℃, indicating that the model based on AdaBoost algorithm has the high prediction accuracy for the temperature performance of fuel rods.

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备注/Memo

备注/Memo:
Received: 2021- 07-12; Accepted: 2021-10-07; Online (CNKI): 2022-07-15
Corresponding author: Engineer JIN Xin.E-mail: jin-xin2@cgnpc.com.cn
Citation: HONG Liang, JIN Xin, LIU Xiaohan, et al. Application of machine learning algorithm in the prediction of fuel rod temperature performance [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(5): 515-520.(in Chinese)
作者简介:洪亮(1988—),中广核研究院有限公司工程师.研究方向:燃料棒性能分析软件研发、压水堆一回路污垢行为分析软件研发. E-mail: hongl27@163.com
引文:洪亮,金鑫,刘虓瀚,等.机器学习算法在燃料棒温度性能预测中的应用[J].深圳大学学报理工版,2022,39(5):515-520.
更新日期/Last Update: 2022-09-30