Experimental validation of analytic formulas for the statistical uncertainty in the Feynman-α method. (August 2017)

• Record Type: Journal Article
• Authors: Dubi, C.; Kolin, A.; Blaise, P.; Geslot, B.; Gilad, E.
• Is Part Of: Annals of nuclear energy. Volume 106(2017:Aug.)
• Page Start: 84
• Page End: 90
• Publication Date: 2017-08
• Subjects: Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2017.03.031
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The paper introduces the implementation and verification of analytic results introduced in an earlier publication at ANE, "Analytic derivation of the statistical error in the Feynman-alpha method", Vol. 88, 2016. Implementation of the formulas shows a very good correspondence between the analytic estimation and the measured uncertainty. Results are applicable in determining the needed measurement time (depending on the reactivity) when preforming noise experiments. Abstract: Noise experiments, and the Feynman- α method in particular, are considered a standard in-pile experiment, often used in zero power reactors in a sub critical configuration to estimate the reactivity level. In the Feynman- α method, the variance to mean ratio of event triggered neutron count is fitted on the so called Feynman-Y function, resulting with an experimental estimation of the α decay mode. In a recent study, analytic formulas for the expected statistical uncertainty in the Feynman- α method were derived, using the backward stochastic transport equation. The outline of the present study is to experimentally validate these analytic formulas. The formulas were implemented on 4 different signals, obtained from the MINERVE reactor at CEA Cadarache. For each of the four measurements, the signal was truncated in increasing measurement lengths (ranging from 1 to 60 min), in order to observe the functional behavior of the statistical error through time. Then, the measured statistical error was compared with the analytic estimation. Results indicate a very high correspondence between the expected statistical error and the measured one.