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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.12
- 수록면
- 2,709 - 2,716 (8page)
- DOI
- https://doi.org/10.1016/j.net.2020.05.010
이용수
초록· 키워드
오류제보하기
Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuelcomposition, control rod insertions and flow regimes. For this reason, they usually lack high order ofsymmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces ofpossible loading patterns. A detailed hyperparameter optimization technique (a combination of manualand Gaussian process search) is used to train and optimize deep neural networks for the prediction ofthree neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod banklevel, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulatorby shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deepnetworks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and forthe radially and axially averaged PPF (~0.05). The mean difference between targets and predictions forthe control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the highdimensional space, with minor improvements found for larger number of samples. The promisingfindings of this work prove the ability of deep neural networks to resolve high dimensionality issues oflarge cores in the nuclear area
목차
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참고문헌 (22)
참고문헌 신청
S. M. Mirvakili / 2012 / Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network / Ann. Nucl. Energy 50:82~93
A. Garg / 2007 / Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor / Nucl. Eng. Des 237:230~239
X. Wei / 2016 / Prediction study on PCI failure of reactor fuel based on a radial basis function neural network / Science and Technology of Nuclear Installations :1~6
Majdi I. Radaideh / 2020 / Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling / Nuclear Engineering and Technology 52(2):287~295
M. I. Radaideh / 2020 / Surrogate modeling of advanced computer simulations using deep Gaussian processes / Reliab. Eng. Syst. Saf 195:106731
A. Garg / 2007 / Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor / Nucl. Eng. Des 237:230~239
X. Wei / 2016 / Prediction study on PCI failure of reactor fuel based on a radial basis function neural network / Science and Technology of Nuclear Installations :1~6
Majdi I. Radaideh / 2020 / Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling / Nuclear Engineering and Technology 52(2):287~295
M. I. Radaideh / 2020 / Surrogate modeling of advanced computer simulations using deep Gaussian processes / Reliab. Eng. Syst. Saf 195:106731
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