Speaker
Description
The VENUS-F zero power reactor was first operated in 2011 at SCK CEN in the framework of the MYRRHA project. Over time, many experimental campaigns were carried out at VENUS-F in support of several - mostly heavy metal cooled - fast reactor designs.
Knowledge about the processing procedures of such experiments is retained by expert users knowing how to interact with the many codes developed in the years for this purpose.
The NEREA (Neutron Energy-integrated Reactor Experiment Analysis) open source Python package is now available to enable expedite reactor experiment analysis.
The object oriented design of NEREA grants three main advantages:
- easy user interaction with each class independently from others;
- modularity enabling fast new feature implementation;
- possibility of automated A-to-Z processing with minimal effort.
To date, NEREA enables processing of reaction rate traverses, reaction rate ratios as spectral indices and control rod worth measurements.
The code is structured in four major components. A first set of classes is designed to gather and pre-process the experimental data (reaction rates, fission fragment spectra and fission chamber effective masses). The pre-processed raw experimental data are fed to classes enabling reaction rate traverse, spectral index and control rod worth processing. Parallel to that, classes are defined to read model results from Serpent 2 Monte Carlo code outputs. Finally, an independent suite is designed to compare the processed experimental results to the calculated ones. The uncertainty is propagated through the whole processing chain, with automatic calculation of variance fractions. Moreover, all uncertainty components can be computed enabling for explicit uncertainty management.
Git version control is used in the development of NEREA. Unit tests with a 95% coverage ensure code stability during its development. NEREA is currently hosted on GitHub (https://github.com/GrimFe/NEREA) and on test Pypi (https://test.pypi.org/project/nerea/).
As compared to the codes previously used at SCK CEN, NEREA stands as faster processing tool that enables for automatized processing in a Python environment. This results in a reduced user mistake and ensures consistent processing methodologies among different experiments.
NEREA features preferential interfaces with the data acquisition systems used by SCK CEN at VENUS-F, yet the modularity inherent to its object oriented design enables easy implementation of new features. Future plans foresee implementation of modules for the processing of solid state detector and activation foil measurements in NEREA.
