DOE is awarding $47 million through its Nuclear Energy University Program (NEUP) to support 63 university-led nuclear energy research and development projects in 29 states. NEUP seeks to maintain U.S. leadership in nuclear research across the country by providing top science and engineering faculty and their students opportunities to develop innovative technologies and solutions for civil nuclear capabilities.
Title: Development of Thermal Inelastic Scattering Covariance Data Capabilities with Demonstration of Light Water Evaluation
Project description: The goal of this project is to produce a format for covariance data for inelastic thermal neutron scattering data for moderators in the ENDF format. To demonstrate the viability of this new format, an evaluation of the covariance data for thermal scattering in light water in this format will be produced, along with the capabilities to generate the files and test their efficacy. A capability for calculating sensitivity coefficients using multigroup methods to the fundamental physics parameters governing light-water scattering will be developed to facilitate identifying nuclear data needs related to thermal scattering.
Title: Mechanistic Understanding of Radioytically Assisted Hydrothermal Corrosion of SiC in LWR Coolant Environments
Project description: The objective of this project is to develop a mechanistic understanding of the hydrothermal corrosion behavior of monolithic SiC and SiC/SiC composites in LWR environment under the influence of water radiolysis products and radiation damage. Complementary atomistic simulations will be carried out to determine the rate controlling mechanisms for dissolution under different water chemistries and in the presence of radiation. Activation energies and kinetic rates will be calculated directly from these simulations and compared to experimentally fitted values. The dissolution rate constants determined and validated in this integrated experimental and modeling approach will allow predictions of long-term SiC corrosion behavior.
Title: Model-Based Diagnostics and Mitigation of Cyber Threats
Faculty: (UM PI) John Lee, (BNL Co-PI) Athi Varuttamaseni, (INL Co-PI) Robert Youngblood
Project description: This project intends to develop a toolkit for modeling digital instrumentation and control (I&C) systems for nuclear power plants so that the consequences of cyber-attacks on I&C systems may conveniently be modeled using nuclear plant simulation software. The results of the toolkit-based models, the corresponding responses, and the performance of the diagnostic schemes will be tested on a virtual control room driven by a plant simulator.
Title: High-resolution Experiments for Extended LOFC and Steam Ingress Accidents in HTGRs
Project description: The objective of this project is to better understand key phenomena in high-temperature gas-cooled reactors relevant to steam ingress and loss of forced circulation (LOFC) accidents. Specifically, the research will: 1) experimentally investigate, using an existing integral-effect test facility with some improvements, the steam-ingress accident caused by a postulated steam generator tube rupture initiating event; 2) carry out integral-effect tests for the extended LOFC accident to study the establishment of global natural circulation flow in the primary loop; 3) design, based on a scaling analysis, and construct a separate-effect test facility to study the complex helium flows in the core and hot plenum during the extended LOFC accident; and 4) perform detailed, high-resolution, separate-effects experiments using the results obtained as boundary/initial conditions.
Title: Process-Constrained Data Analytics for Sensor Assignment and Calibration
Faculty: (ANL PI) Richard Vilim, (UM PI) Brendan Kochunas, (Xcel Energy Co-PI) Marc Anderson
Amount: NEET-Argonne National Lab to UMICH as a subK, Brendan Kochunas $202,650
Project description: This project will develop and demonstrate data-analytic methods to address the problem of how to assign a sensor set in a nuclear facility such that 1) a requisite level of process monitoring capability is realized, and in turn, 2) the sensor set is sufficiently rich to allow analytics to determine the status of the individual sensors with respect to their need for calibration. This approach will allow for automated calibration status, avoiding unneeded calibration activities in the facility.