Dr. Jamie B. Coble
UT Department of Nuclear Engineering
February 6th, 2015, 2:30 – 3:30 PM
410 John D. Tickle Engineering Building
Dr. Jamie Baalis Coble is an Assistant Professor in the Nuclear Engineering department at the University of Tennessee, Knoxville. Dr. Coble’s research focuses on statistical data analysis, empirical modeling, and advanced pattern recognition for equipment condition assessment, process and system monitoring, anomaly detection and diagnosis, and failure prognosis. Prior to joining the UT faculty, she worked in the Applied Physics group at Pacific Northwest National Laboratory (PNNL). Her work there focused primarily on data analysis and feature extraction for detecting anomalies and degradation in large passive components (e.g., concrete structures, pipes, welds), advanced active components (e.g., pumps, motors, valves), and other nuclear systems. Dr. Coble is currently pursuing research in prognostics and health management for active components and systems. Her research interests expand on past work in monitoring and prognostics to incorporate remaining useful life estimates into risk assessment, operations and maintenance planning, and optimal control algorithms. Incorporating equipment condition information in operations planning and control supports greater situational awareness and improved mission completion for complex engineering systems. She is working with colleagues at PNNL to develop an Enhanced Risk Monitor, which will incorporate equipment condition assessment into real-time evaluation of operational risk for advanced small modular reactor (AdvSMR) designs.
Talk Abstract: USA for baseload and peak demand power production and process heat applications (e.g., water desalination, shale oil extraction, hydrogen production). However, AdvSMRs face significant technical hurdles to commercialization due to the unique features and characteristics inherent to their compact designs. The feature may include new materials of construction, employment of modular fabrication techniques, and unique safety and instrumentation and control issues related to the small power output of individual modules. The day-to-day costs of AdvSMRs are expected to be dominated by operations and maintenance (O&M), but the effect of diverse operating missions and unit modularity on O&M is not fully understood. These costs could potentially be reduced by optimized plant control and risk-informed scheduling of maintenance, repair, and replacement of equipment. Currently, most nuclear power plants (NPPs) have a “living” probabilistic risk assessment (PRA), which reflects the as-operated, as-modified plant and combines initiating event frequencies with population-based probability of failure (POF) to evaluate the overall plant safety risk. “Risk monitors” extend the PRA by incorporating the actual and dynamic plant configuration (equipment availability, operating regime, environmental conditions, etc.) into risk assessment. In fact, PRAs are more integrated into plant management in today’s NPPs than at any other time in the history of nuclear power. However, population-based POF curves are still used to populate fault trees; this approach neglects the time-varying condition of the equipment that is relied on during standard and non-standard configurations. Equipment condition monitoring techniques can be used to estimate the POF of a specific component in its specific operating environment. Incorporating this unit-specific estimate of POF in the risk monitor can provide a more accurate estimate of risk in different operating and maintenance configurations. This enhanced risk assessment will be especially important for AdvSMRs that incorporate advanced component designs, which do not have an available operating history to draw from, and often use passive design features, which present additional challenges to PRA. This presentation will outline the requirements and technical gaps that remain in developing a so-called Enhanced Risk Monitor and present the work to date in development of this risk assessment paradigm.