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DOE OSTI2015

Development of a Novel Gas Pressurized Stripping Process-Based Technology for CO₂ Capture from Post-Combustion Flue Gases

A novel Gas Pressurized Stripping (GPS) post-combustion carbon capture (PCC) process has been developed by Carbon Capture Scientific, LLC, CONSOL Energy Inc., Nexant Inc., and Western Kentucky University in this bench-scale project. The GPS-based process presents a unique approach that uses a gas pressurized technology for CO₂ stripping at an elevated pressure to overcome the energy use and other disadvantages associated with the benchmark monoethanolamine (MEA) process. The project was aimed at performing laboratory- and bench-scale experiments to prove its technical feasibility and generate process engineering and scale-up data, and conducting a techno-economic analysis (TEA) to demonstrate its energy use and cost competitiveness over the MEA process. To meet project goals and objectives, a combination of experimental work, process simulation, and technical and economic analysis studies were applied. The project conducted individual unit lab-scale tests for major process components, including a first absorption column, a GPS column, a second absorption column, and a flasher. Computer simulations were carried out to study the GPS column behavior under different operating conditions, to optimize the column design and operation, and to optimize the GPS process for an existing and a new power plant. The vapor-liquid equilibrium data under high loading and high temperature for the selected amines were also measured. The thermal and oxidative stability of the selected solvents were also tested experimentally and presented. A bench-scale column-based unit capable of achieving at least 90% CO₂ capture from a nominal 500 SLPM coal-derived flue gas slipstream was designed and built. This integrated, continuous, skid-mounted GPS system was tested using real flue gas from a coal-fired boiler at the National Carbon Capture Center (NCCC). The technical challenges of the GPS technology in stability, corrosion, and foaming of selected solvents, and environmental, health and safety risks have been addressed through experimental tests, consultation with vendors and engineering analysis. Multiple rounds of TEA were performed to improve the GPS-based PCC process design and operation, and to compare the energy use and cost performance of a nominal 550-MWe supercritical pulverized coal (PC) plant among the DOE/NETL report Case 11 (the PC plant without CO₂ capture), the DOE/NETL report Case 12 (the PC plant with benchmark MEA-based PCC), and the PC plant using GPS-based PCC. The results reveal that the net power produced in the PC plant with GPS-based PCC is 647 MWe, greater than that of the Case 12 (550 MWe). The 20-year LCOE for the PC plant with GPS-based PCC is 97.4 mills/kWh, or 152% of that of the Case 11, which is also 23% less than that of the Case 12. These results demonstrate that the GPS-based PCC process is energy-efficient and cost-effective compared with the benchmark MEA process.

Chen, Shiaoguo↗

DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRSDate not supplied

The effect of reduced pressure on the characteristics of spreading flames

Flame spread over solid fuels is a canonical problem in fire science, due to its direct implications on material flammability and importance in fire development. In a microgravity environment, such as onboard a spacecraft, flames can behave very differently than on Earth. This is concerning for spaceflight life safety, especially in higher-oxygen environments. Due to the difficulties associated with microgravity testing, low-pressure environments have been proposed as an alternative to approximately replicate the burning behavior of solid fuels observed in reduced gravity conditions because of similar diffusion and heat transfer mechanisms. However, the roles played by gravity and pressure on flame length, standoff distance, and flame spread rate vary with the burning configuration. In concurrent flame spread, the buoyant flow enhances the spread rate by bringing the flame closer to the fuel surface and increasing the heating of the solid fuel. In opposed flame spread, the sample is preheated by the flame ahead of the flame leading edge, which is strongly affected by the surrounding flow field. In this work, we consider flames spreading over thin cotton samples in both downward (opposed) and upward (concurrent) configurations to investigate the effect of pressure (30-100 kPa) on flame characteristics, such as spread rate and standoff distance. A small forced flow is induced upward so that the flames are exposed to a mixed (forced and free) flow. By reducing pressure, flames become less bright, their standoff distance increases, and their spread rates decrease similar to what is observed in low-gravity environments. These results could in help understanding the differences between flames spreading at low pressure and low gravity environments for these similar, yet very different, spreading configurations. They could also provide more information about potential Earth-based flammability testing of materials for spacecraft applications.

fire safety↗

DOE OSTI2014

Submersion criticality safety of tungsten-rhenium urania cermet fuel for space propulsion and power applications

Nuclear thermal rockets are the preferred propulsion technology for a manned mission to Mars, and tungsten–uranium oxide cermet fuels could provide significant performance and cost advantages for nuclear thermal rockets. A nuclear reactor intended for use in space must remain subcritical before and during launch, and must remain subcritical in launch abort scenarios where the reactor falls back to Earth and becomes submerged in terrestrial materials (including seawater, wet sand, or dry sand). Submersion increases reflection of neutrons and also thermalizes the neutron spectrum, which typically increases the reactivity of the core. This effect is typically very significant for compact, fast-spectrum reactors. This paper provides a submersion criticality safety analysis for a representative tungsten/uranium oxide fueled reactor with a range of fuel compositions. Each submersion case considers both the rhenium content in the matrix alloy and the uranium oxide volume fraction in the cermet. The inclusion of rhenium significantly improves the submersion criticality safety of the reactor. While increased uranium oxide content increases the reactivity of the core, it does not significantly affect the submersion behavior of the reactor. There is no significant difference in submersion behavior between reactors with rhenium distributed within the cermet matrix and reactors with a rhenium clad in the coolant channels. The combination of the flooding of the coolant channels in submersion scenarios and the presence of a significant amount of spectral shift absorbers (i.e. high rhenium concentration) further decreases reactivity for short reactor cores compared to longer cores.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRSDate not supplied

The Effect of Reduced Pressure on the Characteristics of Spreading Flames

fire safety↗

NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

Systems Health Managent↗

NASA NTRS2007

Gravity-Dependent Combustion and Fluids Research - From Drop Towers to Aircraft to the ISS

Driven by the need for knowledge related to the low-gravity environment behavior of fluids in liquid fuels management, thermal control systems and fire safety for spacecraft, NASA embarked on a decades long research program to understand, accommodate and utilize the relevant phenomena. Beginning in the 1950s, and continuing through to today, drop towers and aircraft were used to conduct an ever broadening and increasingly sophisticated suite of experiments designed to elucidate the underlying gravity-dependent physics that drive these processes. But the drop towers and aircraft afford only short time periods of continuous low gravity. Some of the earliest rocket test flights and manned space missions hosted longer duration experiments. The relatively longer duration low-g times available on the space shuttle during the 1980s and 1990s enabled many specialized experiments that provided unique data for a wide range of science and engineering disciplines. Indeed, a number of STS-based Spacelab missions were dedicated solely to basic and applied microgravity research in the biological, life and physical sciences. Between 1980 and 2000, NASA implemented a vigorous Microgravity Science Program wherein combustion science and fluid physics were major components. The current era of space stations from the MIR to the International Space Station have opened up a broad range of opportunities and facilities that are now available to support both applied research for technologies that will help to enable the future exploration missions and for a continuation of the non-exploration basic research that began over fifty years ago. The ISS-based facilities of particular value to the fluid physics and combustion/fire safety communities are the Fluids and Combustion Facility Combustion Integrated Rack and the Fluids Integrated Rack.

Urban, David L.↗

DOE OSTI2022

Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment

Abstract Self-Piercing Riveting (SPR) is one of the most commonly used methods for joining dissimilar materials in the automotive industry. These joints are popular due to their adaptability, high performance and short cycle time. However, since these joints involve two dissimilar materials, they are susceptible to galvanic corrosion in the presence of an electrolyte which is driven by the difference in the equilibrium potential of the metals. This can affect the safety and resilience of these joints. In this paper, we focus on galvanic corrosion in Al-Fe SPR joints. A Machine learning (ML) based surrogate model, which is based off of FE simulations, for statistical corrosion analysis is developed. This model enables the resilience and reliability analysis of SPR joints under corrosion environment. In this study, first a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode of a SPR joint. This model takes into account the effect of the crystal microstructure of the Al anode and the precipitation of the corrosion product. Several geometric and environmental factors including crevice gap, roughness of anode, conductivity, pH and the temperature of the electrolyte that effect corrosion are investigated. A thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al SPR joints using a novelistic Probabilistic Confidence-Based Adaptive Sampling (PCAS) technique. PCAS is used to train the surrogate model by identifying the critical sampling points and thus reducing the overall computational costs. It is found that the electrolyte temperature has the largest effects on the material loss and needs to be managed closely for better corrosion control. By understanding the corrosion performance and resultant uncertainty impact on SPR joints, the reliability and resilience of these joints can be improved.

36 MATERIALS SCIENCE↗

DOE OSTI2015

PRELIMINARY EVALUATION OF FeCrAl CLADDING AND U-Si FUEL FOR ACCIDENT TOLERANT FUEL CONCEPTS

Since the accident at the Fukushima Daiichi Nuclear Power Station, enhancing the accident tolerance of light water reactors (LWRs) has become an important research topic. In particular, the community is actively developing enhanced fuels and cladding for LWRs to improve safety in the event of accidents in the reactor or spent fuel pools. Fuels with enhanced accident tolerance are those that, in comparison with the standard UO2-zirconium alloy system, can tolerate loss of active cooling in the reactor core for a considerably longer time period during design-basis and beyond design-basis events while maintaining or improving the fuel performance during normal operations and operational transients. This paper presents early work in developing thermal and mechanical models for two materials that may have promise: U-Si for fuel, and FeCrAl for cladding. These materials would not necessarily be used together in the same fuel system, but individually have promising characteristics. BISON, the finite element-based fuel performance code in development at Idaho National Laboratory, was used to compare results from normal operation conditions with Zr-4/UO2 behavior. In addition, sensitivity studies are presented for evaluating the relative importance of material parameters such as ductility and thermal conductivity in FeCrAl and U-Si in order to provide guidance on future experiments for these materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Differential degradation patterns of photovoltaic backsheets at the array level

There are relatively few field studies on the degradation of non-fluoropolymer-based backsheets, and understanding their in-field behavior is critical for further development of such products. In this paper, backsheet degradation of modules with one of these new types of backsheets (polyethylene naphthalate (PEN)-based) was documented at a four-year old utility-scale array located in Maryland (USA). Visual inspection, colorimetry, glossimetry, and Fourier-transform infrared spectroscopy (FTIR) revealed highly varied properties depending on module position within the array. Specifically, modules near the edge of the array and with higher mounting elevations underwent greater amounts of backsheet degradation, as indicated by yellowing and gloss-loss. The reason for these unique degradation patterns were differential backside exposure conditions, especially of ultraviolet light. This was strongly influenced by the array design, including array structural and environmental factors, such as module spacing and ground cover, respectively. Within the array, no clear link between backsheet degradation and module output or safety has been identified. However, such a relationship may be expected to become more pronounced with time, affecting system lifetime and ultimately the levelized cost of electricity (LCOE). Finally, the observed phenomena have implications for both backsheet product development and array design, especially for modules that utilize newer classes of non-fluoropolymer-based backsheets which are typically more susceptible to environmental degradation.

14 SOLAR ENERGY↗

DOE OSTI2014

Strain-Based Design Methodology of Large Diameter Grade X80 Linepipe

Continuous growth in energy demand is driving oil and natural gas production to areas that are often located far from major markets where the terrain is prone to earthquakes, landslides, and other types of ground motion. Transmission pipelines that cross this type of terrain can experience large longitudinal strains and plastic circumferential elongation as the pipeline experiences alignment changes resulting from differential ground movement. Such displacements can potentially impact pipeline safety by adversely affecting structural capacity and leak tight integrity of the linepipe steel. Planning for new long-distance transmission pipelines usually involves consideration of higher strength linepipe steels because their use allows pipeline operators to reduce the overall cost of pipeline construction and increase pipeline throughput by increasing the operating pressure. The design trend for new pipelines in areas prone to ground movement has evolved over the last 10 years from a stress-based design approach to a strain-based design (SBD) approach to further realize the cost benefits from using higher strength linepipe steels. This report presents an overview of SBD for pipelines subjected to large longitudinal strain and high internal pressure with emphasis on the tensile strain capacity of high-strength microalloyed linepipe steel. The technical basis for this report involved engineering analysis and examination of the mechanical behavior of Grade X80 linepipe steel in both the longitudinal and circumferential directions. Testing was conducted to assess effects on material processing including as-rolled, expanded, and heat treatment processing intended to simulate coating application. Elastic-plastic and low-cycle fatigue analyses were also performed with varying internal pressures. Proposed SBD models discussed in this report are based on classical plasticity theory and account for material anisotropy, triaxial strain, and microstructural damage effects developed from test data. The results are intended to enhance SBD and analysis methods for producing safe and cost effective pipelines capable of accommodating large plastic strains in seismically active arctic areas.

03 NATURAL GAS↗

DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗

NASA NTRSDate not supplied

Human Factors and Behavioral Performance Exploration Measures Harmonized Across HERA, NEK, and ISS: Teams Risk

BACKGROUNDThe Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture risks. HFBP-EM were collected during Human Exploration Research Analogs (HERA) campaigns 4 (C4) and 5 (C5), and during SIRIUS 17 and 19 missions in the Russian Ground Based Experiment Complex, NEK, to document the feasibility, flexibility, and acceptability of these measures in analogsof the spaceflight environment. A subset of the HFBP-EM suite was collected during spaceflight as part HRP’s Standard Measures in Spaceflight Project. Whenever possible, the HFBP-EM protocol and measures are kept the same across studies, however, differences across research settings (e.g., experimental manipulations, mission scenarios, mission length) and implementation of the measures require the data are harmonized to ensure comparable views across missions. The purpose of our project is to develop a harmonized database of HFBP-EM data from different settings, and to summarize the trajectory of behavioral health and performance within and between research settings. In this presentation, we will summarize the harmonized dataset and the trajectory of measures related to the Team Risk, including team performance, team cohesion, team processes, and psychological safety, over time and between and within settings.METHODSWe followed best practices for data harmonization. Characteristics of each research setting were assessed for harmonization potential, and we deemed NEK-SIRIUS 17 as inappropriate due to study aims, length, and data quality. Common variables of interest were identified. Study characteristics and key variables with which datafiles could be merged were defined as “meta-data.” HFBP-EM data from all settings were processed under a common format. We then created a harmonized team-level database designed to facilitate analyses that address HRP research gaps related to HRP’s Team risk. In this database, team cohesion, processes, performance, psychological safety, and group living were operationalized as the team mean of the crew responses for each data collection (e.g., on mission day 7). Data collected on the International Space Station (ISS) included a subset of scale items administered to participants. Data collected from ISS team members within +/- 7 days of the first data collection and every following 30 +/-4 days were aggregated to the team level. We generated figures that display the mean and variability of team constructs across research settings. We also generated plots of changes in team constructs with mission day and with percentage of the mission completed. Where possible, we compared data from spaceflight analogs with data from astronauts aboard the ISS. RESULTS AND DISCUSSIONThe team-level harmonized database was structured such that each row represents data for a team on a specific mission day. Team constructs (e.g., team cohesion) were included as columns (i.e., wide format) with repeated measures across mission days given in rows (i.e., long format). The database included data from 18 crews: five, 4-person American crews in HERA C4, four 4-person American crews in HERA C5, one 6-person multinational crew in NEK-SIRIUS 19, and eight, 2–11-person multinational crews aboard the ISS. Results provide insights into mission and campaign differences in team functioning and performance. For example, between campaign differences were observed for team processes—the interdependent team actions that orchestrate taskwork in pursuit of the team’s goals [1]. Greater between-team variability on team processes was detected during HERA C4 than during C5, and a downward trend was observed over the duration of C4, but not C5 or SIRIUS 19. This may be due to the campaign-level differences such as the sleep deprivation implemented during C4. Responses on the subset of team process items administered on the ISS indicate between-crew variability more like those observed in C4 than C5 and NEK, with some, but not all ISS crews demonstrating a downward trend over time. Additional findings will be presented. REFERENCESMarks, M. A., et al (2001) Academy of Management Review, 26(3), 356-376.

S T Bell↗

DOE OSTI2025

Modeling a Thermal-Spectrum LEU-fueled Molten Salt Reactor Co-fueled with Thorium in SCALE 6.3.1 and Serpent-2

Interest in the development of advanced nuclear fission reactors for commercial electricity production has risen in recent years, with Generation IV reactor designs offering numerous advantages in safety, efficiency, fuel cycle sustainability, and waste management. In particular, thorium-fueled molten salt reactors (MSRs) are considerably promising for enhancing fuel cycle sustainability in their ability to breed fissile 233U fuel from thorium, a presently untapped and widely abundant resource. A novel MSR fuel cycle concept, the ”Sourdough” refueling and waste management strategy, has previously been demonstrated with a traditional uranium-based fuel cycle in a thermal-spectrum MSR operating with low enriched uranium (LEU) fuel with favorable neutronic performance. However, the ability to use this unique fuel cycle approach with thorium-based molten salt fuels has not yet been studied. In this work, the Sourdough fuel cycle was implemented in a small, thermal-spectrum MSR fueled with high assay low enriched uranium (HALEU) and fertile 232Th for breeding 233U fuel. Relevant neutronic data, including fuel and isothermal temperature feedback behavior, was studied using the SCALE 6.3.1 and Serpent-2 code systems, and keff data was measured during simulated depletion at 400 MWth. The Sourdough fuel cycle concept is shown to perform favorably with a thorium-fueled MSR model, thus warranting further study into its use in other MSR designs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Wildfire towers drive firebrand lofting: insights from coupled fire-atmosphere model simulations

Wildfire behavior is shaped by complex fire dynamics, with firebrands playing a critical role in spot fire ignition and fire spread. While previous studies have explored firebrand generation and transport, the specific role of towers and troughs from wildland fires in the lofting of firebrands remains unquantified. This study addresses that gap by using physics-based coupled fire-atmosphere model simulations to examine how wildfire towers (updrafts) and troughs (downdrafts) influence firebrand lofting. Our results show that the majority of firebrands (78.85%) are lofted from towers, where strong updrafts drive long-range transport. In contrast, only 21.15% of firebrands are lofted within troughs, where downdrafts cause most firebrands to fall near the fireline. We also find that firebrand size significantly influences lofting behavior, with smaller particles (1 mm radius) exhibiting the strongest correlation with updraft intensity. These findings highlight the dominant role of wildfire towers in promoting long-distance firebrand dispersal—an essential factor in rapid wildfire growth and wildland-urban interface (WUI) fire risks. By quantifying the relationship between firebrand lofting and fire-induced atmospheric features, this study provides critical insights to improve spot fire modeling, support mitigation planning, and enhance firefighter and WUI community safety in spot fire-prone regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Supervisory Control System for Multi-Modular Advanced Reactors

The proposed supervisory control system (SCS) may provide considerable benefits to advanced small modular reactors, including reduced plant staffing, optimized maintenance activities, greater plant availability, and higher operating efficiency. The SCS makes risk-informed decisions based on (1) a probabilistic assessment of the likelihood of success given the status of the plant/systems and component health, and (2) a deterministic assessment between plant operating parameters and reactor protection parameters to prevent unnecessary trips and challenges to plant safety system—one measure of SCS success. The probabilistic portion of the decision-making engine of the SCS is based on the control actions associated with an advanced liquid-metal reactor (ALMR) Power Reactor, Innovative, Small Module (PRISM). Within the SCS, the probabilistic assessment provides a ranking of viable control actions; however, certain instructions generated by the probabilistic model only include an abstract notion of action without specifications. For instance, one instruction may be to reduce power without specifying how much reduction is needed. The prognostic/diagnostic models incorporate the health of components into the decision-making process. Once the control options are identified and ranked based on the likelihood of success, the SCS transmits the options to the deterministic portion of the platform. The performance-based system models assess and rank each of the probabilistically identified control actions by taking into account the physical behavior (current and projected) of the system. The performance-based decision making module receives inputs from the probabilistic decision-making module and the ERM module to generate a single solution. Interfaces to these modules are defined later in the section. A utility theory algorithm factors into the decision making by estimating the distance from and approach to a trip setpoint for each control option. If the magnitude of a negative utility value increases rapidly as the system approaches the trip setpoint, that option is not likely to be the preferred option. This can lead to a re-ranking of the control options. The SCS then transmits a control signal(s) to a component or system and informs the operator of actions taken based on the action chosen. The SCS successfully coupled probabilistic and performance-based system models to arrive at optimal control decisions based on the actual status of the plant and components. The automatic, autonomous, and real-time performance requirements for a control system were met by the SCS. The value of coupling probabilistic and performance-based system models was demonstrated by the re-ranking of control options based on the use of a utility algorithm. The use of ERM monitors provides added value to the SCS as demonstrated by the re-ranking of control options based on a components degraded state.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗