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Spierling, Ruth↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Power and particle exhaust for the ARC fusion power plant
To successfully show that fusion is an attractive energy source, the ARCTM fusion power plant will need to operate with a robust, integrated power and particle exhaust solution. To maximise ARC’s fusion power output while avoiding excessive erosion of the plasma-facing components, we will need to radiatively dissipate most of the power crossing the last-closed flux surface, injecting radiating impurities such as argon or neon to access divertor detachment. Divertor detachment will need to be integrated with a high-performance core plasma, and with efficient impurity pumping to prevent the accumulation of helium ash in the core. To access and control detachment in high-performance plasmas, we have designed ARC with up–down-symmetric divertors, with secondary X-points in long, tightly baffled outer legs. Using a core-edge modelling workflow, we predict that with this divertor design, ARC will access detachment with modest argon seeding in the divertor (c Ar,div ∼0.9%), which should have minimal impact on the core ( Z eff,core <0.5) for reasonable argon enrichment (c Ar,div /c Ar,core =6). Due to the high predicted divertor neutral pressure (p div ∼20 Pa), we predict that ARC will sufficiently pump helium to limit ash accumulation in the core (c He,core <2%) for a helium enrichment of c He,div /c He,core =0.4. ARC’s divertor design is expected to increase the stability of a detachment front in the outer divertor leg, helping to prevent divertor reattachment during smaller heat-flux transients such as scrape-off-layer filaments associated with the quasi-continuous exhaust regime. However, this buffering will not be sufficient to prevent divertor reattachment during large type-I edge-localised modes (ELMs), and as such these will need to be avoided on ARC. Experiments on SPARC will be used to select an integrated scenario which avoids or mitigates type-I-ELMs while maintaining access to detachment, good core fusion performance and sufficient impurity exhaust. SPARC experiments will also be used to finalise ARC’s divertor design, by studying the impact of magnetic and first-wall geometry on detachment stability, impurity enrichment and neutral baffling under conditions similar to those expected for ARC. In conclusion, our analysis finds that ARC will have a viable power and particle exhaust solution which is compatible with high-power operations, and this solution will be validated in experiments on SPARC.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel - Supplement 22, Duane Arnold
This report is Supplement 22 to Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel (PNNL-35730) and contains the infrastructure evaluation for Duane Arnold.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Neural-based time series forecasting of loss of coolant accidents in nuclear power plants
During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.
42 ENGINEERING↗
Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel - Supplement 21, Three Mile Island
This report is Supplement 21 to Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel (PNNL-35730) and contains the infrastructure evaluation for Three Mile Island.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Fog and Froth-based Post Combustion CO 2 Capture in Fossil Fuel Power Plants
The University of Kentucky (UK) project titled “Fog+Froth-Based Post-Combustion CO 2 Capture in Fossil Fuel Power Plant”, DE-FE0031733, was conducted from 5/1/2019 to 12/31/2023 in two Budget Periods (BPs), with BP1 from 5/1/2019 to 1/31/2021 and with BP2 from 2/1/2021 to 12/31/2023. The overall goal is to reduce the CO 2 capture capital and operating cost by reducing the absorber size. Other participants included Industrial Climate Solutions, Inc. (ICSI), ALL4 and NexantECA. All project deliverables, milestones, and success criteria were met. Scaled-up demonstration of the compact absorber is planned and two specific aspects of the project demonstration, the solvent spray and the low absorber bottom packing temperature, have been incorporated into the overall UK CO 2 capture process which is on the road to large scale verification and commercialization.
01 COAL, LIGNITE, AND PEAT↗
Quantifying the reductions in mortality from air-pollution by cancelling new coal power plants
Deep decarbonization paths to the 1.5°C or 2°C temperature stabilization futures require a rapid reduction in conventional coal-fired power plants, but countries are currently building 223 GW of new coal power capacity and plan to build 377 GW more in the next decade. Coal-fired plants are also a major contributor to air pollution related health impacts. Here, we couple an integrated human-earth system model (GCAM) with an air quality model (TM5-FASST) to examine regional health co-benefits from cancelling new coal-fired plants worldwide. Additionally, we find that cancelling all new proposed projects would decrease air pollution related premature mortality between 101,388 - 213,205 deaths (2-5%) in 2030, and 213,414 - 373,054 (5-8%) in 2050, globally, but heavily concentrated in developing Asia. Furthermore, we estimate that strengthening the climate target from 2°C to 1.5°C would avoid 326,351 additional mortalities in 2030, of which 251,011 (75%) are attributable to the incremental coal plant shutdown.
63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
Auxiliary heating and current drive physics for the ST-E1 fusion power plant
This work describes the physics basis for the proposed auxiliary heating and current drive system on the ST-E1 fusion power plant. The ST-E1 flattop plasma considered here is fully non-inductive with a bootstrap fraction of 0.9 and the remaining current driven by EC waves. Using the recently published physics-based optimization method for EC launchers (Lopez et al 2025 Plasma Phys. Control. Fusion 67 055012), we show that the target flattop ECCD can be achieved with a net efficiency of 52 kA MW −1 using fundamental O-mode (O1) with frequency range 160–200 GHz launched from the low-field side top half of the vacuum vessel (LFS top-launch). From considering two candidate rampup scenarios, we conclude that LFS top-launch O1 ECCD can be equally effective during the early stages of plasma operation, although poloidal steering might be needed. X-mode waves injected from the LFS midplane are also shown to be effective for rampup even when T e < 1 keV. We also present modeling results for the pre-conceptual design of an ICRH system proposed for ST-E1. Using TORIC, we find that an ICRH system aiming for 42–48 MHz and toroidal mode number n φ ~ 10 robustly achieves dominant ion damping via Helium-3 minority heating transitioning to second-harmonic Tritium heating. We then show that such waves can be efficiently generated by a 5-strap traveling-wave antenna (TWA) using the Petra-M code. The TWA has a 40–45 MHz passband within which ~60% of the power entering the TWA is coupled to the plasma with the remaining ~40% of the power being transmitted through the TWA and possibly recirculated; the power reflected back into the transmission lines is negligible. This passband structure persists even when the evanescent distance is increased by a factor of two, or when the magnetic-field angle is increased by 30°, demonstrating inherent load resilience that will be crucial for effective ICRH on ST-E1.
electron cyclotron↗
Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance
Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants
An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Control System of Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS) & HIL Platforms for Design
Multi-port autonomous reconfigurable solar power plant (MARS) provides an attractive alternative to connect photovoltaic (PV) and energy storage systems (ESSs) to high-voltage direct current (HVdc) links and high-voltage alternating current (ac) grids. In this paper, a unique hierarchical control system of MARS is proposed and evaluated. To evaluate the control system and associated algorithms in early-stage research of complex architectures like MARS, it is important to develop unique suitable hardware-in-the-loop (HIL) platforms. In this paper, the HIL platforms for MARS to evaluate the performance of the hierarchical control system and the control algorithms implemented are presented. Further, they help with the design process of control systems. The real-time simulation models and algorithms that are utilized for MARS in the HIL platforms are also discussed in the paper. The HIL experiments of the control system of MARS showcase the capability to provide continuity of operation under faults and frequency support to the power grid during loss of generation. They also showcase the stability of the proposed hierarchical control system of MARS.
14 SOLAR ENERGY↗
Inter-Area Oscillation Damping with Type-5 Wind Power Plant
This paper investigates the potential of using brushless excitation (BLE) for not only riding through the fault but also to damp inter-area power oscillation with the help of a synchronous generator (SG) used in Type 5 Wind Power Plant (WPP). In BLE, an auxiliary synchronous generator (ASG) behaving like an exciter is coupled and driven by the rotor of the main SG. The BLE's field current's ASG is fed by two separate loops of the automatic voltage regulator (AVR) and power system stabilizer (PSS). The AVR system implements a control loop to regulate the generator terminal voltage, VT. For the PSS, the kinetic energy of the wind turbine is utilized according to the estimated rotational speed of the synchronous generator shaft of the SG. It may mitigate the necessity of any curtailment of active power for damping. The effectiveness of the proposed control scheme is verified with a three-phase short circuit fault in a two-area power system.
brushless↗
Industry-Average Performance for Components and Initiating Events at U.S. Commercial Nuclear Power Plants: 2020 Update
This report documents the quantitative results of the current industry-average performance for components and initiating events (IEs) at U.S. commercial nuclear power plants (NPPs). It represents the third update of the original analysis in NUREG/CR-6928 with data through 2020. Continuous characterization and updating of current industry-average performance with the latest industry data available are important steps in maintaining up-to-date risk models. Typically, data from 1998–2002 were used in NUREG/CR-6928, data from 1998–2010 in the first update, data from 1998–2015 in the second update, and data from 2006–2020 in this update, although many IEs required longer periods for adequate characterization of frequencies in all these analyses. As with NUREG/CR-6928 and previous updates, four types of events are covered in this report: component unreliability (e.g., a pump that fails to start or fails to run), component or train unavailability resulting from test or maintenance outages, special event probabilities covering operational issues (e.g., pump restarts and injection valve re-openings during unplanned demands), and IE frequencies. Results (in the form of beta distributions for failure probabilities upon demand and gamma distributions for rates) are used as inputs to the U.S. Nuclear Regulatory Commission standardized plant analysis risk models covering U.S. commercial NPPs.
99 GENERAL AND MISCELLANEOUS↗
Techno-Economic Analysis of Product Diversification Options for Sustainability of the Monticello and Prairie Island Nuclear Power Plants
The objective of this work was to perform techno-economic analysis (TEA) of hybrid options that could be integrated with light water reactor (LWR) nuclear power plants (NPPs) in order to improve the viability and sustainability of existing LWRs through product diversification by utilizing nuclear energy not only to produce grid electricity but to also produce carbon free products such as hydrogen, ammonia or synthetic fuels. Much of the analysis herein could be generally applied to any LWR (high temperature steam electrolysis- HTSE design), but hydrogen demand and the optimization of the HTSE was completed with specific collaboration and data from Xcel Energy’s Prairie Island and Monticello nuclear generating stations and the surrounding market and logistics potential in the greater Minneapolis region. Xcel Energy has set aggressive goals with regards to decarbonization, including an 80% reduction in CO 2 emissions from 2005 levels, by 2030 and 100% carbon free by 2050. Other TEAs regarding hydrogen production with nuclear energy have been completed in collaboration with other utility companies previously. Besides being the first TEA in this regard specific to Xcel Energy and the surrounding markets and logistics, this TEA adds to the previously completed TEAs by providing the most up to date and state of the art analysis and optimization of the hydrogen production plant integrated with nuclear power (NPP-HTSE).
08 HYDROGEN↗
Inter-Area Oscillation Damping with Type-5 Wind Power Plant: Preprint
This paper investigates the potential of using brushless excitation (BLE) for not only riding through the fault but also to damp inter-area power oscillation with the help of a synchronous generator (SG) used in Type 5 Wind Power Plant (WPP). In BLE, an auxiliary synchronous generator (ASG) behaving like an exciter is coupled and driven by the rotor of the main SG. The BLE's field current's ASG is fed by two separate loops of the automatic voltage regulator (AVR) and Power System Stabilizer (PSS). The AVR system implements a control loop to regulate the generator terminal voltage, VT. For the PSS, the kinetic energy of the wind turbine is utilized according to the estimated rotational speed of the synchronous generator shaft of the SG. It mitigates the necessity of any curtailment of active power for damping. The effectiveness of the proposed control scheme is verified with a three-phase short circuit fault in a two-area power system.
brushless↗
Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant
In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.
61 RADIATION PROTECTION AND DOSIMETRY↗
5G integrated edge computing platform for efficient component monitoring in coal-fired power plants
This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.
20 FOSSIL-FUELED POWER PLANTS↗
Quantitative resilience evaluation on recovery from emergency situations in nuclear power plants
Here, this paper mainly introduces how to develop a resilience evaluation model by quantifying the relationship between resilience and resilience components in regard to recovering from emergency situations in nuclear power plants (NPPs). It is an extension of the author’s previous researches. To develop the model, the first step was to analyze event reports published in Republic of Korea in terms of resilience factors from different safety perspectives. Second, statistical methods such as factor analysis, principal component analysis, and multivariate regression analysis were applied to the dataset to identify relations among resilience factors and develop a quantitative resilience evaluation model for recovery from emergency situations in NPPs. Third, the model was validated using the recent event report data released in Korea. Lastly, we present a discussion on how to apply the quantitative resilience evaluation model to results from stress tests performed in Korea.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗