Artificial Intelligence Design of a Fast Neutron Source at The University of Tennessee
Scott Lab
Scott Lab
E 525
201 W 19th Ave.
United States
Abstract
The nuclear reaction cross sections of many materials proposed for use in several advanced nuclear reactor applications are poorly understood and computational modeling and simulation of future nuclear systems is unreliable without accurate nuclear reaction data. To meet the national need of providing more accurate nuclear modeling data, the subcritical Fast Neutron Source is being designed to facilitate cross section measurements and benchmarking models. The proposed facility has an innovative flexible design that will be able to adjust the neutron spectra to match that of multiple fast reactor designs. The facility will be located in a heavily shielded laboratory in the University of Tennessee’s new Engineering building to open in 2021. This talk will focus on the AI-based design and optimization, in addition to some of the licensing requirements.
About the Speaker
Dr. Hines received the BS degree in Electrical Engineering from Ohio University in 1985, and then served as a nuclear qualified submarine officer in the US Navy. He later received both an MBA and an MS in Nuclear Engineering from The Ohio State University in 1992, and a Ph.D. in Nuclear Engineering from The Ohio State University in 1994
Dr. Hines has authored over 350 technical papers in the areas of artificial intelligence and advanced statistical techniques applied to process diagnostics, condition-based maintenance, and prognostics.
He was recognized by the American Society of Engineering Education Nuclear Engineering Division, through their Glenn Murphy Distinguished Nuclear Engineering Educator Award in 2014, was selected as an American Nuclear Society Fellow in 2015, and was the recipient of the American Nuclear Society, Arthur Holly Compton Award in Education in 2019.