EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||18|
|Section||Advanced Modelling and Simulation|
|Published online||22 February 2021|
- J. Rhodes, K. Smith, and D. Lee, “CASMO-5 Development and Applications,” PHYSOR-2006, Vancouver, Canada, 2006. [Google Scholar]
- O. Fabbris et al, “Surrogates Based Multi-Criteria Predesign Methodology of Sodium-Cooled Fast Reactor Cores - Application to CFV-like cores,” Nuclear Engineering and Design, vol. 305, pp. 314–333, 2016. [Google Scholar]
- G. Reynoso-Meza, et al, “Physical programming for preference driven evolutionary multiobjective optimization,” Applied Soft Computing, 24, pp. 341–362, 2014. [Google Scholar]
- J. Andersson, “A survey of multiobjective optimization in engineering design,” Tech. Rep. LiTH-IKP-R-1097, Department of Mechanical Engineering, Linkoping University, 2000. [Google Scholar]
- D.E. Goldberg. 1989. “Genetic Algorithms in Search, Optimization and Machine Learning,” 1st. ed. Addison-Wesley Longman Publishing Co., Inc., USA. [Google Scholar]
- “Nuclear Metal Fuel: Characteristics, Design, Manufacturing, Testing, and Operating History,” White Paper 18-01, United States Nuclear Regulatory Commission, June 2018. [Google Scholar]
- B.M. Adams, et al, “Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.10 User’s Manual,” Sandia Technical Report SAND2014-4253, May 2019. [Google Scholar]
- A. Messac, “Physical programming-effective optimization for computational design.” AIAA journal 34.1 (1996): 149-158. [Google Scholar]
- C.J. Werner, et al., “MCNP6.2 Release Notes”, Los Alamos National Laboratory, report LA-UR-18-20808 (2018). [Google Scholar]
- R. Stewart, “FRIDGe: Fast Reactor Input Deck Generator,” Journal of Open Source Software, 4(40), 1486, https://doi.org/10.21105/joss.01486. [Google Scholar]
- M. B. Chadwick, M. Herman, P. Oblozinsky, et al, “ENDF/B-VII.1 nuclear data for science and technology: Cross sections, covariances, fission product yields and decay data”, Nuclear Data Sheets, 112(12):2887-2996 (2011). [Google Scholar]
- The HDF Group. “Hierarchical Data Format, version 5,” http://www.hdfgroup.org/HDF5/(1997). [Google Scholar]
- R. Stewart, (2019). Sodium Fast Reactor Database (Version v0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3464101 [Google Scholar]
- W. McKinney. “Data Structures for Statistical Computing in Python”, Proceedings of the 9th Python in Science Conference, 51-56 (2010). [Google Scholar]
- E. Jones, E. Oliphant, P. Peterson, et al, “SciPy: Open Source Scientific Tools for Python”, http://www.scipy.org/ (2001). [Google Scholar]
- N. Touran, “physprog”, https://github.com/partofthething/physprog (2017). [Google Scholar]
- R. Stewart and T.S. Palmer, “Geometric Design Space for Sodium Fast Reactors,” Transactions of the American Nuclear Society, Washington, D.C., November 17-21, Vol. 121, (2019). [Google Scholar]
- R. Stewart and T.S. Palmer, “Metallic Fuel Design Space for Sodium Fast Reactors,” Transactions of the American Nuclear Society, Orlando, Florida, November 11-15, Vol. 119, (2018). [Google Scholar]
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