SYMPOSIA PAPER Published: 14 December 2018

Spectrum Adjustment Results for Three Environments in the ACRR Central Cavity Using a Genetic Algorithm


Presented in this report is the description of a new method for neutron energy spectrum adjustment that uses a genetic algorithm to minimize the difference between calculated and measured reaction probabilities. The reaction probability is the integral over all energies of the product of the microscopic reaction cross section with the neutron fluence. The measured reaction probabilities are found using neutron activation analysis. The method adjusts a trial spectrum provided by the user that typically is calculated using a neutron transport code such as Monte Carlo N-Particle. Observed benefits of this method over currently existing methods include: (a) the reduction in unrealistic artifacts in the spectral shape when compared to iterative unfold approaches such as are used in the SAND-II code or to least squares approaches when an accurate prior spectrum covariance is not available; and (b) a reduced sensitivity to increases in the energy resolution of the derived spectrum. This report presents the adjustment results for various spectrum-altering bucket environments in the central cavity of the Annular Core Research Reactor. In each case, the results are compared to those generated using LSL-M2, which is a code commonly used for spectrum adjustment. The genetic algorithm produces spectrum-averaged reaction probabilities comparable to those resulting from LSL-M2. The splicing of local segments of the a priori spectrum, which is part of the genetic algorithm, permits the resulting spectrum adjustment to avoid introducing severe narrow energy-width shape artifacts without the requirement of a covariance matrix for the prior spectrum.

Author Information

Vega, Richard, M.
Applied Nuclear Technologies, Sandia National Laboratories, Albuquerque, NM, US
Parma, Edward, J.
Applied Nuclear Technologies, Sandia National Laboratories, Albuquerque, NM, US
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Developed by Committee: E10
Pages: 335–352
DOI: 10.1520/STP160820170099
ISBN-EB: 978-0-8031-7662-1
ISBN-13: 978-0-8031-7661-4