A key national energy priority to promote energy security is sustainable nuclear power. Nuclear energy currently contributes approximately 20% of baseload electrical needs in the United States and is considered a reliable generation source to meet future electricity needs. Advanced small modular reactors (AdvSMRs) using non-light-water reactor coolants such as liquid metal, helium, or liquid salt are promising mid-to long-term options being explored for added functionality and affordability in future reliable nuclear power deployment. AdvSMRs can offer potential advantages over more conventional technologies in the areas of safety and reliability, sustainability, affordability, functionality, and proliferation resistance. However, a number of technical challenges will need to be addressed before AdvSMRs are ready for deployment, given their potential for remote deployment with minimal staffing, longer operating cycles between planned refueling and maintenance outages, and support for multiple energy applications. In addition, AdvSMRs (like SMRs based on more conventional light-water reactor technologies) will have reduced economy-of-scale savings when compared to current generation lightwater reactors (LWRs). Issues related to AdvSMR deployment can be addressed through cross-cutting RD&D involving instrumentation, controls, and human-machine interface (ICHMI) technologies. Specifically, diagnostics and prognostics technologies provide a mechanism for improving safety and reliability of AdvSMRs through integrated health management of passive components. This report identifies activities and develops an outline of a research plan to address the high-priority technical needs for demonstrating prototypic prognostic techniques to manage degradation of passive AdvSMR components. Concepts for AdvSMRs span a wide range of design maturity, specificity, and concepts of operation, including multi-unit, multi-product-stream generating stations. Key to the development and deployment of AdvSMRs will be the ability to ensure safe and affordable operation of these reactor designs. AdvSMR designs generally place more emphasis on passive systems to assure safety. However, degradation in all passive components will need to be well-managed to maximize safety, operational lifetimes, and plant reliability while minimizing maintenance demands, if reduced economies-of-scale are to be overcome. Traditional approaches such as periodic in-service nondestructive inspections are likely to have limited applicability to AdvSMRs, given the expectation of longer operating periods and potential difficulties with inspection access to critical components. Advanced instrumentation and control (I&C) technologies can provide a mechanism for achieving these goals. However, the significant technology and environmental differences between AdvSMRs and conventional LWRs and the potential for modularized deployment result in unique challenges and needs for advanced ICHMI applications in AdvSMRs. v prognostics is also documented. This assessment, combined wi...

Executive SummaryAdvanced small modular reactors (AdvSMRs) may provide a longer-term alternative to traditional light-water reactors and SMRs based on integral pressurized water reactor concepts currently being considered. AdvSMRs are designed to incorporate multiple modules (which may or may not have shared components and structures) at a single location, comprising a full "plant." AdvSMR operation differs fundamentally from full-size plants because the smaller plants may be used for load-following or peakdemand power generation, instead of baseload generation. AdvSMRs are also being considered for dualuse, where process heat would be used for both electricity generation and another purpose such as hydrogen production or water desalination, shown in Figure Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment. Although some of the loss of economies of scale inherent to AdvSMRs can be recovered, controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs.Technologies that help characterize real-time risk are important to controlling O&M costs and improving affordability of AdvSMRs. Given the possibility of frequently changing plant configurations in AdvSMRs, advanced plant configuration information, equipment condition information, and risk monitors are needed to support real-time decisions on O&M. For this purpose, approaches are needed to integrate these three elements in a manner that provides a measure of risk that is customized for each AdvSMR unit, and accounts for the specific operational history of the unit. By integrating technologies for condition assessment with risk monitors, asset optimization and improved economics of AdvSMRs may be enabled by:• Maximizing generation by assessing the potential impact of taking key components offline for testing or maintenance, iv• Supporting reduced O&M staff by aiding in optimization of O&M planning (specifically by assessing the contribution of individual components to changes in risk and using this information for scheduling maintenance activities),• Enabling real-time decisions on stress-relief for risk-significant equipment susceptible to degradation and damage, and• Supporting potential remote siting by providing early warning of potential increases in plant risk.This report describes research results from an initial methodology for such enhanced risk monitors (ERMs) that integrate real-time information about equipment condition and probability of failure (POF) into risk monitors to provide an assessment of dynamic risk as plant equipment ages. This integration occurs at the level of the POF within risk monitors.Risk monitors extend probabilistic risk assessment (PRA) frameworks by incorporating the actual and dynamic plant configuration (e.g., equipment availability, operating regimes, and environmental conditions) into the risk assessment. PRA is itself a systematic safety analysis methodology that follows four steps: identify undesirable consequences (e.g., reactor unavailability, core da...

vii in risk is seen to reduce under certain circumstances. This appears to depend on the contribution of the component to the overall risk (i.e., the "importance" of the component). Repairs or replacements (bringing the components to as-new condition) reduce the risk, although aging of other components may still drive the overall risk higher. As well, we assume that the uncertainty associated with the component condition after repair or replacement is reduced. While this contributes to reducing the uncertainty bounds in the risk metric, uncertainty in the aging of other components may still drive the overall uncertainty higher as well. These pieces of information, when compared to traditional PRA analysis, appear to provide useful information for scheduling maintenance activities based on actual degradation condition and consequent failure probabilities. Specifically, if thresholds may be set on the risk metric of interest, the projected risk and uncertainty bounds provide a mechanism for scheduling maintenance activities whenever the risk (plus uncertainty) exceeds the threshold. Key to accurate uncertainty quantification within the ERM will be the ability to accurately identify failure probabilities of typical components used in AdvSMRs. Such reliability data is not readily available, and for AdvSMR concepts, may comprise data from instrumented test reactors that were operated between the 1970s and 1990s. Available data from such test reactors is being examined for applicability to this project. The ERM can provide additional value through the development of alternative risk metrics. Metrics associated with quantities such as cost or losses due to lost generation or unanticipated plant shutdown may provide valuable insights into the tradeoffs associated with continued plant operation while maintaining adequate safety margins. To this end, alternative risk metrics associated with these quantities are being identified and will be evaluated next. Ongoing and planned research is focused on evaluating alternative risk metrics (including the options described earlier) and the impact of uncertainty on these risk metrics. In addition, we anticipate integrating the ERM methodology with simulation tools that simulate advanced reactor/AdvSMR modules and the impact of component degradation on their performance to perform comprehensive evaluations of the ERM methodology. In addition, we will explore the possibility of evaluations using experimental data, and to this end, will continue to evaluate sources of relevant reliability data, including data from test reactors, and available test-beds. xi Acronyms AC alternating current AdvSMR advanced small modular reactor CAFTA Computer Aided Fault Tree Analysis (system) CCF common cause failure CDF core damage frequency CREDO Centralized Reliability Data Organization (component reliability database

High-Temperature Gas-Cooled Reactor in-Containment Sensing and Control Systems .

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Enabling real-time decisions on stress-relief for risk-significant equipment susceptible to degradation and damage, thereby supporting optimized lifetime management. As described in previous reports in this series, probabilistic risk assessment (PRA) provides a static representation of risk associated with operation and maintenance (O&M) of nuclear power plants. Technologies for characterizing real-time risk (so called Enhanced Risk Monitors or ERMs) take into account plant-specific normal, abnormal, and deteriorating states of systems, structures, and components (SSCs) in the estimation of current and future risk to safe and economic operation. Additionally, technologies for characterizing real-time risk provide a mechanism for compensating for the relatively small amount of long-term reliability data from AR systems, structures, and components. The ability to monitor performance and characterize changes in operational risk in real-time can reduce the level of dependence on such performance data. Proactively establishing a viable ERM methodology before AR component design specifications are established also supports: (i) building in opportunities for automated monitoring (on-line and off-line) of those components for optimizing performance with respect to anticipated demands on these reactors; and (ii) improving the maintainability of components from the perspective of time-to-repair and component cost. This research report summaries the development and evaluation of a prototypic ERM methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the PRA model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real-time based on ECA will provide a mechanism for optimizing plant performance while staying within specified safety margins. The report provides an overview of the methodology for integrating time-dependent failure probabilities into risk monitors. This prototypic ERM methodology was evaluated using a hypothetical PRA model, generated using a simplified design of a liquid-metal-cooled AR. Component failure data from industry compilation of failures of components similar to those in the simplified AR model were used to initialize the PRA model. By using time-dependent probability of failure (POF) that grows from the initial probability when equipment is in like-new condition to a maximum POF, which occurs before a scheduled maintenance action that restores or repairs the component to "as-new" condition, the changes in core damage frequency (CDF) over time were computed and analyzed. vii Acronyms and Abbreviations AC alternating current AdvSMR advanced small modular reactor AFI aging fractional increase AST aging start time CAFTA Computer Aided Fault Tree Analysis (system) CCF common cause failure CDF core damage frequency CREDO Centralized Reliability Data Organization (component reliabili...

Prognostic health management technologies are expected to play a vital role in the deployment and safe, cost-effective operation of advanced reactors by providing the technical means for lifetime management of significant passive components and reactor internals. This report describes a Bayesian methodology for the prediction of remaining life of materials and passive AR components. This approach, previously applied to predict time-to-failure of materials subjected to localized aging and degradation, is adapted for component-level prognostics. The Bayesian framework for component-level prognostics incorporates the ability to fuse information from multiple sources, including information on localized degradation and component-level condition indicators. The ability to switch between multiple models of degradation accumulation rate and/or multiple models of measurement physics becomes important in this context, and a reversible jump Markov chain Monte Carlo methodology has been developed for this purpose. Evaluations of the Bayesian framework and the model switching and selection methodology were performed using synthetic data as well as experimental measurements on a high-temperature creep testbed. Results to date indicate that the feasibility of the proposed Bayesian framework for prognostics, though an improvement over previous methods' accuracy, will require the ability to quantify sources of uncertainties within the various models used in the prognostic framework. Ongoing efforts are focused on sensing and measurement (particularly in-situ measurements) that would be applied as inputs to the prognostics framework, with the objective of identifying measurement methods that can provide early indicators of material degradation and quantifying the reliability and sensitivity of these measurement methods.

This study provides an overview of the methodology for integrating time-dependent failure probabilities into nuclear power reactor risk monitors. This prototypic enhanced risk monitor methodology was evaluated using a hypothetical probabilistic risk assessment (PRA) model, generated using a simplified design of a liquid-metal-cooled advanced reactor (AdvRx). Component failure data from industry compilation of failures of components similar to those in the simplified AR model were used to initialize the PRA model. Core damage frequency (CDF) over time were computed and analyzed. In addition, a study on alternative risk metrics for AdvRx was conducted. Risk metrics that quantify the normalized cost of repairs, replacements, or other operations and management (O&M) actions were defined and used, along with an economic model, to compute the likely economic risk of future actions such as deferred maintenance based on the anticipated change in CDF due to current component condition and future anticipated degradation. Such integration of conventional-risk metrics with alternate-risk metrics provides a convenient mechanism for assessing the impact of O&M decisions on safety and economics of the plant. It is expected that, when integrated with supervisory control algorithms, such integrated-risk monitors will provide a mechanism for real-time control decision-making that ensure safety margins are maintained while operating the plant in an economically viable manner.