Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP:Ministry of Science, ICT and Future Planning) (No. NRF-2021M2D2A1A02044210).
References
- US Nuclear Regulatory Commission, Standard review plans for environmental reviews for nuclear power plants, Supplement 1: Operating License Renewal (2013). NUREG-1555, Supplement 1, Revision 1.
- IAEA, Preparedness and Response for a Nuclear or Radiological Emergency, 7, IAEA Safety Standards Series No. GSR Part, Vienna, 2015. IAEA.
- A. De Visscher, Air Dispersion Modeling: Foundations and Applications, John Wiley & Sons, 2013.
- N.S. Holmes, L. Morawska, A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available, Atmos. Environ. 40 (30) (2006) 5902-5928. https://doi.org/10.1016/j.atmosenv.2006.06.003
- A. Leelossy, F. Molnar, F. Izsak, A. Havasi, I. Lagzi, R. Meszaros, Dispersion modeling of air pollutants in the atmosphere: a review, Cent. Eur. J. Geosci. 6 (3) (2014) 257-278.
- A. Baklanov, Application of CFD methods for modelling in air pollution problems: possibilities and gaps, in: Urban Air Quality: Measurement, Modelling and Management, Springer, Dordrecht, 2000, pp. 181-189.
- A.F. Stein, R.R. Draxler, G.D. Rolph, B.J. Stunder, M.D. Cohen, F. Ngan, NOAA's HYSPLIT atmospheric transport and dispersion modeling system, Bull. Am. Meteorol. Soc. 96 (12) (2015) 2059-2077. https://doi.org/10.1175/BAMS-D-14-00110.1
- N. Sanin, G. Montero, A finite difference model for air pollution simulation, Adv. Eng. Software 38 (6) (2007) 358-365. https://doi.org/10.1016/j.advengsoft.2006.09.013
- R. Cervantes-Muratalla, L.P. Ramirez-Rodriguez, T. Mendivil-Reynoso, C.R.A. Murguia-Romero, D. Garcia-Bedoya, Application of fourier transform for distribution of pollutants in air, World J. Environ.l Biosci. 9 (2) (2020) 1-7.
- M. Raissi, P. Perdikaris, G.E. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378 (2019) 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
- J. Wang, X. Peng, Z. Chen, B. Zhou, Y. Zhou, N. Zhou, Surrogate modeling for neutron diffusion problems based on conservative physics-informed neural networks with boundary conditions enforcement, Ann. Nucl. Energy 176 (2022), 109234.
- L. Dong, L. Qi, T. Lei, A. Ping, Y. Fan, Solving multi-dimensional neutron diffusion equation using deep machine learning technology based on pinn model, 核动力工程 43 (2) (2022) 1-8.
- Y. Yang, H. Gong, S. Zhang, Q. Yang, Z. Chen, Q. He, Q. Li, A Data-Enabled Physics-Informed Neural Network with Comprehensive Numerical Study on Solving Neutron Diffusion Eigenvalue Problems, 2022 arXiv preprint arXiv: 2208.13483.
- E. Schiassi, M. De Florio, B.D. Ganapol, P. Picca, R. Furfaro, Physics-informed neural networks for the point kinetics equations for nuclear reactor dynamics, Ann. Nucl. Energy 167 (2022), 108833.
- X.C. Zhang, J.G. Gong, F.Z. Xuan, A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures, Eng. Fract. Mech. 258 (2021), 108130.
- X. Zhao, K. Shirvan, R.K. Salko, F. Guo, On the prediction of critical heat flux using a physics-informed machine learning-aided framework, Appl. Therm. Eng. 164 (2020), 114540.
- J.M. Stockie, The mathematics of atmospheric dispersion modeling, SIAM Rev. 53 (2) (2011) 349-372. https://doi.org/10.1137/10080991X
- A.J. Cimorelli, S.G. Perry, A. Venkatram, J.C. Weil, R.J. Paine, R.B. Wilson, et al., AERMOD: a dispersion model for industrial source applications. Part I: general model formulation and boundary layer characterization, J. Appl. Meteorol. 44 (5) (2005) 682-693. https://doi.org/10.1175/JAM2227.1
- CERC, ADMS 5 Atmospheric Dispersion Modelling System User Guide, 2016.
- U.S.NRC, Code Manual for MACCS2: Volume 1, User's Guide, 1998 (NUREG/CR-6613).
- U.S.NRC, RASCAL 4: Description of Models and Methods, 2012. NUREC-1940).
- R.H. Pletcher, J.C. Tannehill, D. Anderson, Computational Fluid Mechanics and Heat Transfer, CRC press, 2012.
- T.M.A.K. Azad, L.S. Andallah, Stability analysis of finite difference schemes for an advection diffusion equation, Bangladesh J. Sci. Res. 29 (2) (2016) 143-151. https://doi.org/10.3329/bjsr.v29i2.32331
- S.L. Brunton, J.N. Kutz, Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, 2019.
- TensorFlow Core, TensorFlow core tutorials. https://www.tensorflow.org/tutorials, 2020. (Accessed 27 October 2022).
- G. Canbolat, H. Kose, A. Yildizeli, S. Cadirci, Analytical and numerical solutions of the 1D advection-diffusion equation, in: 5th International Conference on Advances in Mechanical Engineering Istanbul, 2019.