With NETL support, through the Lab’s University Training and Research program, researchers at the University of California, Riverside used advanced computing models that harness machine learning to efficiently reduce impingement in boilers — an innovation that can ensure longer and more efficient service life for power plants and even potentially extend the lives of helicopter rotor blades or aircraft engine components.
Erosion from particle impingement results in irreversible material degradation due to repeated impact of high-velocity particles on surfaces. This process causes perpetual wear of critical components in various energy and technological industries, like those used in petroleum refining and pipelines and in power plants. Mitigating these effects is particularly crucial because the financial loss from erosion is estimated to cost 1 – 4% of the gross national product in industrialized nations.