The Half Monte Carlo Method: Combining Total Monte Carlo with Nuclear Data Sensitivity Profiles

Journal Article · Sat Jul 01 04:00:00 EDT 2017 · Transactions of the American Nuclear Society

OSTI ID:23047519

Hill, I.; Dyrda, J. ^[1]

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1. OECD-Nuclear Energy Agency - NEA, 46, quai Alphonse Le Gallo, Boulogne-Billancourt 92100, France (Nuclear Energy Agency of the OECD (NEA))

Both Total Monte Carlo (TMC) and Generalized Linear Least Squares (GLLS) algorithms have been used extensively to perform nuclear uncertainty propagation. Both methods have strengths and weaknesses, for example, the time required to perform TMC limits its attractiveness, while GLLS cannot generate higher order moments of output distributions and also has trouble dealing with nonlinear distributions. This work uses a new method that overcomes many of the aforementioned limitations of TMC and GLLS, while at the same time reasonably reproduces the TMC result. The concept is a simple combination of the two methods i.e., generate the random nuclear data files using the TMC approach, and then propagate the effect on the integral parameters like k{sub eff} using sensitivity coefficients, as opposed to cumbersome neutron transport calculations. Agreement is demonstrated for the Jezebel sphere ICSBEP benchmark. Since the method is part TMC part GLLS, the term Half Monte Method (HMM) will be used.

• OSTI ID: 23047519
• Journal Information: Transactions of the American Nuclear Society, Journal Name: Transactions of the American Nuclear Society Vol. 116; ISSN 0003-018X
• Country of Publication: United States
• Language: English

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
97 MATHEMATICS AND COMPUTING
ALGORITHMS
BENCHMARKS
COMPUTERIZED SIMULATION
LEAST SQUARE FIT
MONTE CARLO METHOD
NEUTRON TRANSPORT
NONLINEAR PROBLEMS
SENSITIVITY