In the realm of nuclear energy, the accuracy of calculations is paramount, and the foundation of these calculations is the nuclear data libraries that provide essential information about atomic nuclei. When new versions of these libraries are developed, they must be rigorously validated to ensure their reliability. A recent study has introduced an innovative automated simulation pipeline that streamlines this validation process, offering significant benefits for the nuclear industry.
The pipeline converts raw nuclear data into predictions for various reactors, allowing for the assessment of potential biases in integral measurement predictions for nuclear power plants before the release of a new nuclear data library. This is achieved through the implementation of history variables in DRAGON, a nuclear fuel cycle simulation code, to determine the macroscopic cross sections for the nodal code PARCS, which is used for reactor core simulations.
The methodology was verified for seven depletion cycles across three pressurized water reactors (PWRs) using publicly available data. The errors in the predictions were found to be within the uncertainty range attributed to variations in nuclear data, indicating the robustness of the method.
The implications of this research are substantial for the nuclear energy sector. By automating the validation process, the pipeline can significantly reduce the time and resources required to assess new nuclear data libraries. This, in turn, can accelerate the deployment of new libraries, enhancing the accuracy of nuclear calculations and improving the safety and efficiency of nuclear power plants.
Moreover, the methodology presented in the study can be used to assess the quality of novel nuclear data libraries against experimental measurements in PWRs. This can provide valuable insights into the performance of new libraries and help identify areas for improvement, ultimately contributing to the advancement of nuclear technology.
In conclusion, this research represents a significant step forward in the validation of nuclear data libraries. By enhancing the accuracy and efficiency of the validation process, it can support the safe and sustainable development of nuclear energy, a critical component of the global energy mix.
This research was published on arXiv and can be read in full [here](http://arxiv.org/abs/2509.19450v1).