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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.11
- 수록면
- 3,563 - 3,579 (17page)
- DOI
- https://doi.org/10.1016/j.net.2021.05.037
이용수
초록· 키워드
오류제보하기
Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adoptedto train machine learning (ML) models. The measured data is available for fuel assemblies irradiated incommercial reactors operated in the United States and Sweden. The data comes from calorimetricmeasurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuelassemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively.
Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii)to generate and use synthetic data, as large dataset which has similar statistical characteristics as theoriginal dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averagedenrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. Theoutcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heatfrom the four inputs (ii) generation and application of synthetic data which improves the performance ofthe ML models (iii) uncertainty analysis of the ML models and their predictions
목차
등록된 정보가 없습니다.
참고문헌 (54)
참고문헌 신청
I. Bose / 2001 / Business data mining-a machine learning perspective / Inf. Manag 39:211~225
Y. LeCun / 1998 / Gradient-based learning applied to document recognition / Proc. IEEE 86:2278~2324
W. Huang / 2005 / Forecasting stock market movement direction with support vector machine / Comput. Oper. Res 32:2513~2522
J. R. Dorronsoro / 1997 / Neural fraud detection in credit card operations / IEEE Trans. Neural Network 8:827~834
A. Hsu / 2020 / Analysis of NIF scaling using physics informed machine learning / Phys. Plasmas 27:012703
Y. LeCun / 1998 / Gradient-based learning applied to document recognition / Proc. IEEE 86:2278~2324
W. Huang / 2005 / Forecasting stock market movement direction with support vector machine / Comput. Oper. Res 32:2513~2522
J. R. Dorronsoro / 1997 / Neural fraud detection in credit card operations / IEEE Trans. Neural Network 8:827~834
A. Hsu / 2020 / Analysis of NIF scaling using physics informed machine learning / Phys. Plasmas 27:012703
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