Validation of codes for modeling and simulation of nuclear power plants: A review

Abstract

The validation process is done to ensure the performance of nuclear codes compared to experimental results and to determine the degree of reliability of codes output. The development of nuclear power plants (NPPs) designs and the need for safer power plants has led to the increasing diversity/extension of nuclear codes and, as a result, the validation process has become more complicated. In this study, single physics validation including neutronic, core thermal-hydraulics (CTH), system thermal-hydraulics (STH), fuel performance, and multi-physics validation especially coupled neutronic and thermal-hydraulics (CNTH) are discussed. An international collective effort to provide benchmark data and test facilities including core test facility (CTF), separate effect test facility (SETF), and integral test facility (ITF) accompanied with addressing challenges related to scaling issues has been made. However, validation processes may suffer from some challenges. Non-quantifiable parameters cannot be given by experimental measurements. Moreover, for measurable parameters, faulty sensors can lead to incorrect results. Neural network, especially generative deep learning, with the ability to learn the probability distribution of training data and to generate unknown patterns may be a good candidate for the challenges mentioned above, e.g., detecting faulty sensors values and estimating non-quantifiable parameters based on quantifiable ones. Therefore, the application of artificial intelligence (AI) in the validation process may lead to a reduction of the volume of experimental measurements /construction of test facilities which alone is valuable.