Developing the nuclear power systems of the future requires innovative thinking and new approaches to solving complex challenges. For the first time, a team of Idaho National Laboratory (INL) and University of Idaho researchers has successfully applied machine learning to characterizing the microstructure of metallic nuclear fuel, the fine details only visible under powerful magnification. The data collected through this technique will be used by engineers to predict fuel performance more accurately as they develop fuel for the next generation of nuclear power reactors.
The research team, based out of INL’s Irradiated Materials Characterization Laboratory, developed machine learning approaches to extract and analyze a wide range of data points, such as the size and connectivity of fission gas bubbles, from irradiated uranium-zirconium fuel.
Gas bubbles are a natural byproduct of nuclear fission. As uranium atoms split apart, they produce heat along with smaller atoms including xenon and krypton. These and other byproducts are stored as bubbles within the fuel elements, resulting in microstructural changes that can limit the fuel’s ability to transfer heat to the reactor coolant, reducing efficiency.