Nuclear Engineering and Technology

  • 제52권12호
  • /
  • Pages.2709-2716
  • /
  • 2020
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Application of deep neural networks for high-dimensional large BWR core neutronics

  • Abu Saleem, Rabie (Department of Nuclear Engineering, Jordan University of Science and Technology) ;
  • Radaideh, Majdi I. (Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana Champaign) ;
  • Kozlowski, Tomasz (Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana Champaign)
  • 투고 : 2020.02.17
  • 심사 : 2020.05.11
  • 발행 : 2020.12.25
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초록

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the 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 high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

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참고문헌

  1. H. Mazrou, M. Hamadouche, Application of artificial neural network for safety core parameters prediction in LWRs, Prog. Nucl. Energy 44 (2004) 263-275.
  2. International Atomic Energy Agence, Research Reactor Core Conversion from the Use of Highly Enriched Uranium to the Use of Low Enriched Uranium Fuels Guidebook, vol. 233, IAEA-TECDOC, 1980.
  3. A. Hedayat, H. Davilu, A.A. Barfrosh, K. Sepanloo, Estimation of research reactor core parameters using cascade feed forward artificial neural networks, Prog. Nucl. Energy 51 (2009) 709-718.
  4. S.M. Mirvakili, F. Faghihi, H. Khalafi, Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network, Ann. Nucl. Energy 50 (2012) 82-93.
  5. A. Garg, P.S. Sastry, M. Pandey, U.S. Dixit, S.K. Gupta, Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor, Nucl. Eng. Des. 237 (2007) 230-239.
  6. X. Wei, J. Wan, F. Zhao, Prediction study on PCI failure of reactor fuel based on a radial basis function neural network, Science and Technology of Nuclear Installations (2016) 1-6. Article ID 4720685.
  7. M.I. Radaideh, T. Kozlowski, Analyzing nuclear reactor simulation data and uncertainty with the Group Method of Data Handling, Nucl. Eng. Technol. 52 (2020) 287-295.
  8. M.I. Radaideh, T. Kozlowski, Surrogate modeling of advanced computer simulations using deep Gaussian processes, Reliab. Eng. Syst. Saf. 195 (2020), 106731.
  9. J. Yang, J. Kim, An accident diagnosis algorithm using long short-term memory, Nucl. Eng. Technol. 50 (2018) 582-588.
  10. M.I. Radaideh, T. Kozlowski, Combining simulations and data with deep learning and uncertainty quantification for advanced energy modelling, Int. J. Energy Res. 43 (2019) 7866-7890.
  11. Y. Do Koo, Y.J. An, C.H. Kim, M.G. Na, Nuclear reactor vessel water level prediction during severe accidents using deep neural networks, Nucl. Eng. Technol. 51 (2019) 723-730.
  12. J.J. Ortiz, I. Requena, Using a multi-state recurrent neural network to optimize loading patterns in BWRs, Ann. Nucl. Energy 31 (2004) 789-803.
  13. J.L. Montes, J.L. Francois, J.J. Ortiz, C. Martin-Del-Campo, R. Perusquia, Local power peaking factor estimation in nuclear fuel by artificial neural networks, Ann. Nucl. Energy 36 (2009) 121-130.
  14. V.A. Phung, D. Grishchenko, S. Galushin, P. Kudinov, Prediction of in-vessel debris bed properties in BWR severe accident scenarios using MELCOR and neural networks, Ann. Nucl. Energy 120 (2018) 461-476.
  15. T. Lefvert, Ringhals-1 Stability Benchmark: Final Report, NEA/NSC/DOC(96) 22, OECD Nuclear Energy Agency, 1996.
  16. T. Downar, Y. Xu, V. Seker, A. Ward, PARCS: Purdue Advance Reactor Core Simulator, Presented at Physics of Reactors (PHYSOR-2002), Seoul, Korea, October 7-10, 2002.
  17. A. Zameer, S.M. Mirza, N.M. Mirza, Core loading pattern optimization of a typical two-loop 300 MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA (SA) schemes, Ann. Nucl. Energy 65 (2014) 122-131.
  18. E.F. Faria, C. Pereira, Nuclear fuel loading pattern optimisation using a neural network, Ann. Nucl. Energy 30 (2003) 603-613.
  19. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 436-444.
  20. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F.E. Alsaadi, A survey of deep neural network architectures and their applications, Neurocomputing 234 (2017) 11-26.
  21. G. Hinton, L. Deng, D. Yu, G. Dahl, A.R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, B. Kingsbury, T. Sainath, Deep neural networks for acoustic modeling in speech recognition, IEEE Signal Process. Mag. 29 (2012) 82-97.
  22. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, The MIT press, London, England, 2016.
  23. L. Bottou, Large-scale machine learning with stochastic gradient descent, in: Proc. of 19th Int. Computational Statistics (COMPSTAT'2010), Paris, France, August 22-27, 2010 vol. 1, Physica-Verlag HD, 2010, pp. 177-186.
  24. D.P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, 2014 arXiv preprint arXiv:1412.6980.
  25. F. Chollet, Keras. https://keras.io, 2015.