- Status
- Closed
Objectives
The project's objective is the reliable quantification of void-induced reactivity effects in current RBMK-reactor core designs and thus to assist regulatory authorities in the Commenwealth of Independent States (CIS) in evaluating the related safety features of RBMK reactors (At the end of the project there were still 15 RBMK nuclear power reactors in operation).
This research project was a cooperation of the Russian Research Center "Kurchatov Institute" (RRC), Moscow and the University of Bremen, which was structured in 4 technical tasks.
Task 1: Determination of the required calculational accuracy and efforts
Task 2 Implementation of RBMK data
Task 3 Verification of the calculational procedures
Task 4 Execution of calculations and evaluation of results
The advanced three-dimensional Monte Carlo computer code MCNP, was applied to obtain a base for an independent analysis of the RBMK void reactivity effects.
Detailed data for the MCNP simulations of RBMK reactor cores have been compiled. The experimental verification of the MCNP code included the utilized basic nuclear data (cross-section libraries) for RBMK related problems. Test experiments performed at the RBMK Critical Facility of the RRC "Kurchatov Institute" and whole reactor experiments performed at the Smolensk Nuclear Power Plant Unit 3 (SmNPP-3) during start-up have been used for the verification.
Results
The results of the subcritical core states show large deviations from the mean experimental values with a total error of 10-20% of the measured subcriticality, which the researchers found unacceptable relative to the necessary accuracy of important core safety parameters such as the shutdown capacity or control rod worth which is based on these experimental data. The MCNP results of the local flux distributions were not yet statistically reliable.
The experts concluded that MCNP using pointwise ENDF/B (Evaluated Nuclear Data File) cross-section data is well qualified for criticality calculations of these experiments, if the relevant core characteristics are adequately modelled and suitable sets of cross-section data are determined.