Abstract
Neutron transport theory is the research basis for neutronics and focuses on the description of neutron motion in media and the corresponding laws. There are two types of methods for neutron transport calculation: the Monte Carlo method (also called the probabilistic method or the stochastic method) and the deterministic method. The Monte Carlo method is a numerical method based on probability and statistical theories, and can explicitly describe the characteristics of randomly moving particles and the process of physical experiments. In contrast, in the deterministic method, a group of mathematical–physical equations are first built up to explain the physical characteristics of the target system. Then, by discretizing the variables in these equations, including direction, energy, space, and time, an approximate solution can be obtained with numerical calculation. There are complicated features for advanced nuclear systems, such as the complex neutron spectrum and angular distribution, complicated material composition, large spatial span, complex geometry, etc. The Monte Carlo method uses the continuous-energy cross section, and can be used to address any complex geometry, with prominent advantages for neutron transport simulations for advanced nuclear systems. However, some challenges, such as the slow convergence rate and difficulty in addressing problems of deep penetration, still exist. The deterministic method is faster, but falls short in addressing advanced nuclear systems with complex geometries, strong anisotropy of neutron scattering, and complicated energy spectrums. In recent years, the method of characteristics (MOC) and the discrete ordinates method with unstructured meshes have been developed with improved geometry processing abilities. However, problems, such as the ray effects and the high cost of large-scale systems, still need to be solved. The Monte Carlo–deterministic coupling method, which combines the advantages of both the Monte Carlo and deterministic methods, is one of the most efficient and accurate methods for solving transport problems in advanced nuclear systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lamarsh JR (1966) Introduction to nuclear reactor theory. Addison-Wesley Publication Company, Massachusetts
Wu YC (2017) Fusion neutronics. Springer Nature Singapore Pte, Ltd
Lewis EE, Miller WF Jr (1993) Computational methods of neutron transport. American Nuclear Society, La Grange Park, Illinois
Wu YC, Song J, Zheng HQ et al (2015) CAD-based Monte Carlo Program for integrated simulation of nuclear system SuperMC. Ann Nucl Energy 82:161–168
Song J, Sun GY, Chen ZP et al (2015) Study on Monte Carlo K-effective calculation method. Nucl Sci Eng 35(2):241–245 (in Chinese)
Ott K, Neuhold R (1985) Introductory nuclear reactor dynamics. American Nuclear Society, La Grange Park III, USA
Haghighat A, Wagner JC (2003) Monte Carlo variance reduction with deterministic importance functions. Prog Nucl Energy 42(1):25–53
Zhao JB, Li XM, Wu B et al (2016) An automatic adaptive mesh generation method for weight window in Monte Carlo particle transport. Ann Nucl Energy 91:105–110
Wagner JC, Blakeman ED, Peplow DE (2007) Forward-weighted CADIS method for global variance reduction. Trans Am Nucl Soc 97:630–633
Davis A, Turner A (2011) Comparison of global variance reduction techniques for Monte Carlo radiation transport simulations of ITER. Fusion Eng Des 86(9–11):2698–2700
Zhang S, Yu SP, He P (2016) Verification of SuperMC with ITER C-Lite neutronic model. Fusion Eng Des 113:126–130
Wu YC (2018) Multi-functional Neutronics calculation methodology and program for nuclear design and radiation safety evaluation. Fusion Sci Technol 74(4):321–329
Chen ZP, Song J, Wu B et al (2015) Optimal spatial subdivision method for improving geometry navigation performance in Monte Carlo particle transport simulation. Ann Nucl Energy 76:479–484
Askew JR (1972) A characteristics formulation of the neutron transport equation in complicated geometries. Report AEEW-M 1108. United Kingdom Atomic Energy Establishment, Winfrith, England
Bell GI, Glasstone S (1970) Nuclear reactor theory. Van Nostrand Reihold Company, New York
Wu YC, Xie ZS, Fischer U (1999) A discrete ordinates nodal method for one-dimensional neutron transport calculation in curvilinear geometries. Nucl Sci Eng 133:350–357
Azmy Y, Sartori E (2010) Nuclear computational science: a century in review. Springer, Berlin
Lee H, Choi S, Lee D (2015) A hybrid Monte Carlo/method-of-characteristics method for efficient neutron transport analysis. Nucl Sci Eng 180:69–85
Wu YC, Team FDS (2009) CAD-based interface programs for fusion neutron transport simulation. Fusion Eng Des 84(7–11):1987–1992
X-5 Monte Carlo Team (2003) MCNP-A general Monte Carlo N-particle transport code, Version 5. LA-UR-03-1987, Los Alamos National Library
Leppanen J, Pusa M, Vitanen T (2015) The serpent Monte Carlo code: status, development and applications in 2013. Ann Nucl Energy 82:142–150
Lindley BA, Hosking JG, Smith PJ et al (2017) Current status of the reactor physics code WIMS and recent developments. Ann Nucl Energy 102:148–157
Oak Ridge National Laboratory (1998) DOORS3.2 one, two- and three dimensional discrete ordinates neutron/photon transport code system. CCC-650, Oak Ridge, Tennessee
Chen J, Liu ZY, Zhao C et al (2018) A new high-fidelity neutronics code NECP-X. Ann Nucl Energy 116:417–428
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wu, Y. (2019). Steady-State Neutron Transport Theory and Simulation. In: Neutronics of Advanced Nuclear Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-6520-1_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-6520-1_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6519-5
Online ISBN: 978-981-13-6520-1
eBook Packages: EnergyEnergy (R0)