A PWR reactor coolant system is a highly complex physical process: heterogeneous power, flow and temperature distributions are difficult to be accurately measured, because instrumentations are limited in number, thus leading to the relevant safety and protection margins. This situation is in many ways similar to climate and weather models: a complex process that is not possible to sample and measure as finely as wanted. Meteorology and climate sciences have adapted and improved the Data Assimilation techniques in order to improve the accuracy of description and prediction in their fields.

EDF R&D is seeking to assess the potential benefits of applying Data Assimilation to a PWR’s RCS (Reactor Coolant System) measurements: is it possible to improve the estimates for parameters of a reactor’s operating set-point, i.e. improving accuracy and reducing uncertainties of measured RCS parameters?

In this paper we study the feasibility of enhanced estimation of PWR primary parameters, by using twin experiments for assessing Data Assimilation benefits. We simulated test samples with a 0D-Model, and used these samples in a Monte-Carlo approach to get background terms for Data Assimilation.

This successful preliminary study will lead to further assessments with real plant data.

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