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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2025

Hazard and Operability Analysis for Operating, Refueling, and Maintenance of Fuel Cell Electric Buses

​​Since hydrogen vehicles can be implemented in heavy-duty transportation applications such as buses, it is important to understand safety hazards and risks of hydrogen fuel cell electric bus (FCEB) and refueling technology. We conducted a hazard and operability analysis for FCEB operation/driving, refueling, and maintenance/inspection. We identified failure modes and consequences and defined a qualitative risk metric as the product of the likelihood of a failure and the severity of the worst-ca

08 HYDROGEN↗
DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Development of Additive Manufacturing Agnostic Process Parameter Procedure, 316H Stainless Steel Readiness Level Data Sets, and Machine Maintenance Plan

The University of California, Davis is involved in a project to deploy and enhance an artificial intelligence (AI) system for predicting and preventing plasma disruptions on the DIII D tokamak, under the funding from Department of Energy DE-SC0023500 (title: AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)). The overarching goal is to demonstrate that real-time, AI-guided intervention can proactively modify the plasma state to avoid or mitigate disruptions—a critical challenge for the future of fusion energy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mitigation for the demolition of building 25-4838, vehicle maintenance shop, area 25, Nevada national security site, NYE county, Nevada

The purpose of this letter report is to document the mitigation of adverse effects of a proposed undertaking that would demolish Building 25-4838 in Area 25 of the Nevada National Security Site (NNSS) in compliance with the terms of the 2024 Programmatic Agreement among the U.S. Department of Energy and the Nevada State Historic Preservation Officer and the Advisory Council on Historic Preservation Concerning the Protection of Historic Properties on the Nevada National Security Site, Nye County, Nevada (NNSS PA). Building 25-4838 (State Historic Preservation Office [SHPO] Resource # B19019) is a contributing element to the Nuclear Rocket Development Station (NRDS) Historic District (SHPO Resource # D424), which has been determined eligible for listing in the National Register of Historic Places (NRHP) under Criteria A, B, C, and D (Reno et al. 2023). The proposed demolition constitutes an adverse effect. The U.S. Department of Energy (DOE) National Nuclear Security Administration Nevada Field Office (NNSA/NFO) consulted with the SHPO on an adverse effect finding for the undertaking and notified the SHPO of its intent to use the standard mitigation in the NNSS PA on December 11, 2024 (Julian to Reed). The SHPO concurred on December 30, 2024 (Reed to Julian). Therefore, NNSA/NFO has prepared mitigation pursuant to the standard mitigation in the NNSS PA Appendix D.I.A for contributing elements to recorded, NRHP-eligible districts.

#B19019↗
DOE OSTI2026

Audible Noise Modeling of Hydrogen Release Sonic Hazards in Rail Maintenance Facilities

This study implemented validated literature models to predict audible noise due to pressurized gaseous hydrogen releases through a thermally-activated pressure relief device (TPRD) and attached vent stack. A literature survey discovered limited hydrogen-specific noise prediction models validated by experiments. However, empirical noise prediction models for air flowing through pipes and valves were identified. These empirical models were used to predict noise levels and compared against hydrogen noise data reported in two studies: one experimental study of noise from hydrogen leaking through a pipe and another which modeled hydrogen flowing through a solenoid valve during a fuel cell vehicle refueling. The valve flow model was then applied to predict noise for hydrogen releases through a TPRD. Results show that hydrogen releases through a TPRD can produce harmful noise levels varying from 134 to 150 dB. However, further model validation and additional experimental data are needed to improve prediction confidence and accuracy.

08 HYDROGEN↗
DOE OSTI2024

Hydrogen Storage System Modeling: Public Access, Maintenance, and Enhancements

The overall vision of this project is to provide ownership and support for maintaining existing material-based hydrogen storage systems models. This includes making models accessible to the research community through a public web page and updating and enhancing storage systems models to support material developers in assessing their materials relative to DOE vehicle-level targets. Key elements for FY24: Continue to update and enhance existing models for broader application and user-friendliness. Develop tools to evaluate the performance of hydrogen storage materials developed under HyMARC activities or other fundamental hydrogen storage materials discovery research. Expand the application of current hydrogen storage models beyond light-duty vehicles to include medium-, heavy-duty, and mining vehicles and stationary application(s). Develop models for alternatives, such as LOHCs, to material-based systems (liquefied & gaseous H2) and compare for various mobile and stationary use cases.

ENERGY STORAGE,HYDROGEN↗
DOE OSTI2024

OpenStudio Development, Maintenance, and Support

BEM is a key decision support tool for energy efficiency, demand flexibility, and electrification at the building and building stock levels. The OpenStudio SDK maximizes the value of BEM by enhancing the productivity, consistency, and transparency with which it is applied. OpenStudio enables BEM experts to encapsulate domain and process knowledge in such a way that makes it usable by a range of stakeholders.

BEM↗
DOE OSTI2024

Seasonal Maintenance For Schools

This document is supplemental material for a training webinar for best practices to promote indoor air quality and reduce energy in schools. The document contains key takeaways for each section of the webinar as well as important definitions and terminology to be used as a standalone reference.

Ackerman, Allison↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗