Treatment of Epistemic and Aleatory Uncertainties in the Statistical Analysis of the Neutronic Protection System in CANDU Reactors Conference Paper uri icon

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abstract

  • In existing and new nuclear power plants a variety of special safety systems are employed which will trigger fast reactor shutdown in the event of an accident or undesirable plant condition. A key consideration in determining the optimal trip setpoints for these safety systems is the treatment of both epistemic and aleatory uncertainties. A significant issue also arises in attempting to construct probabilistic methodologies that accurately account for these uncertainties while still maintaining consistency with instrument uncertainty calculation methodologies like ISA 67.04. Furthermore, for probabilistic based analyses such as Loss of Regulation events in a CANDU reactor, the calculation of the neutron overpower trip setpoint involves extremal functions and as such extreme value statistics are applied. Since there are variations in the actual reactor physics parameters (fuel channel power, flux detector drift and total reactor power) and thermalhydraulic conditions, determination of accident behavior is more complicated than if the reactor conditions were completely fixed in time. This paper presents the methodology used to establish the epistemic and aleatory uncertainties in reactor trip setpoints such that margins to safety can be established and so that quantitative statistical statements can be made on the probability of safety system action and the confidence level which is self-consistent with the instrument calculation methodology outlined in ISA 67.04. This paper examines the trip setpoints required to mitigate Loss of Power Regulation accidents in a CANDU reactor using a unique treatment of the epistemic and aleatory uncertainties. In addition to the statistical treatment, a key finding in this work relates to the specification of the initial reactor state based on synchronous plant measurements as opposed to Monte Carlo generated states based on sampling of fitted probability distributions.

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publication date

  • January 1, 2008