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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interpreting Forced Oscillation Notifications from ESAMS: General Guidance for Reliability Coordinators

The Eastern Interconnection Situational Awareness Monitoring System (ESAMS) project demonstrated the feasibility of aggregating synchrophasor measurements from across an interconnection, analyzing them, and providing real-time wide-area situational awareness to system operators who may have excellent visibility within their footprint but lack an interconnection-wide view. An application within ESAMS that has garnered industry interest involves detecting forced oscillations visible across multiple areas, identifying the region where the oscillation originated from, quantifying the uncertainty in source localization results, and notifying users in real-time if the detected oscillation amplitudes cross a specified threshold. It is expected that system operators will utilize their internal SCADA/EMS/synchrophasor systems in conjunction with information provided by ESAMS to take effective mitigation actions if forced oscillation notifications are received. This report provides some general guidance on how the ESAMS information can be used for source localization and coordination among multiple reliability coordinators; and also identifies potential enhancements to ESAMS notifications for improved interpretability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Efficient, reliable and cost-effective reversible solid oxide cell technology for hydrogen and electricity production

This report summarizes the work performed by University of California San Diego (UCSD) – OxEon Energy LLC (OxEon) for the U. S. Department of Energy/National Energy Technology Laboratory (DOE/NETL) under Cooperative Agreement DE-FE0031940 entitled “Efficient, Reliable and Cost Effective Reversible Solid Oxide Cell Technology for Hydrogen and Electricity Production”. This reversible solid oxide cell (RSOC) technology has two main novel elements: (i) a compact and low-cost stack architecture that consists of multi-cell cell modules in electrical parallel and series connections and (ii) high performance and fuel-flexible reversible cells with electrodes or all components made by sputtering deposition process for efficient operation in both fuel cell (power generation) and electrolysis (hydrogen production) modes.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Mississippi's Strategic Resilience: A multi-systems approach to secure, reliable, and adaptable electric grid infrastructure

Mississippi’s electric grid resilience challenges are linked to an intersection of complex socioeconomic, ecological, technological, historical, and political challenges, exacerbated by increasing severe weather like flooding and tornado events. The state’s legacy of underinvestment in critical energy infrastructure, particularly in rural areas and vulnerable floodplains, have stressed an aging grid, creating long-lasting disruptions in electric service during weather-related outages. Effective emergency management and preparedness is further hampered by a lack of coordination across local, county, and regional scales. Using the TASTI-GRID platform and partnership with Oak Ridge National Laboratory (ORNL), Mississippi is developing a comprehensive regional resilience strategy to overcome energy security and reliability challenges, mitigating the impacts of natural hazards, and positioning Mississippi as a resilient and premier destination for residents, businesses, and economic development.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Detection of Critical Surface Features in PTLs and GDLs for Improved Device Performance and Manufacturing Reliability

High points, or features that protrude above the surface of the material, on porous transport layers (PTLs) and gas diffusion layers (GDLs) can be critical features that may affect the manufacturing process and the performance of the device containing the feature. High points on PTLs and GDLs may stress the membrane of a polymer electrolyte membrane (PEM) during lamination and cell operation of a PEM electrolyzer or fuel cell. Additionally, high points on GDLs may impact the reliability of the manufacturing process. Thus, understanding these critical features and developing procedures to detect them are a key part of developing quality control techniques for PTLs and GDLs. This work evaluates the effectiveness of the Keyence VR6200 benchtop-scale structured light optical profilometer for detection of surface protrusions on PTLs and GDLs. Standard testing procedures for detecting and measuring high points were created for use on both material types. These procedures were evaluated using Gage Repeatability and Reproducibility (Gage R&R), where the repeatability, reproducibility, and effectiveness of the system to detect and measure high points were quantified. We have shown with high statistical power that the system is very effective in detection and measurement of high points, with Gage R&R contributions measured to be 2.2% and 3.6% for PTLs and GDLs, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Incorporating Local Extreme Weather Events into Reliability Assessments

Presentation discussing modeling extreme weather effects on the Bulk power system. Using a Production cost model to analyze weather related demand increases, generator outages, transmission outages, and fuel supply constraints. These results can be analyzed to inform on system reliability effects of extreme weather. This presentation was developed for the 2026 NERC Probabilistic Assessment Forum at EPRI in Charlotte NC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Reliable numerical key rates for quantum key distribution

In this work, we present a reliable, efficient, and tight numerical method for calculating key rates for finite-dimensional quantum key distribution (QKD) protocols. We illustrate our approach by finding higher key rates than those previously reported in the literature for several interesting scenarios (e.g., the Trojan-horse attack and the phase-coherent BB84 protocol). Our method will ultimately improve our ability to automate key rate calculations and, hence, to develop a user-friendly software package that could be used widely by QKD researchers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reliability of Lumped Thermodynamic Hydrogen Fueling Model under Slow-Fill Conditions

This study verifies the reliability of lumped thermodynamic fuel cell electric vehicle (FCEV) tank model under considerably slow-fill conditions. Many countries put efforts into research and development of FCEVs. As part of the efforts, thermodynamic hydrogen fueling models have been developed to understand the hydrogen temperature in the onboard tanks of FCEVs during the fueling process. Most of the models treat the hydrogen inside the tanks to be a lumped system and assume the hydrogen temperature to be uniform throughout the tanks. In other words, the hydrogen temperature is treated as average bulk temperature. This study certifies whether treating the hydrogen temperature in a tank as the bulk gas temperature is suitable by evaluating the temperature distributions inside the tank by a three-dimensional computational fluid dynamics (3D CFD) model.

DIRECT ENERGY CONVERSION,HYDROGEN↗
DOE OSTI2018

Simulation Modeling for Reliable Biomass Supply Chain Design Under Operational Disruptions

Lignocellulosic biomass derived fuels and chemicals are a promising and sustainable supplement for petroleum-based products. Currently, the lignocellulosic biofuel industry relies on a conventional system where feedstock is harvested, baled, stored locally, and then delivered in a low-density format to the biorefinery. However, the conventional supply chain system causes operational disruptions at the biorefinery mainly due to seasonal availability, handling problems, and quality variability in biomass feedstock. Operational disruptions decrease facility uptime, production efficiencies, and increase maintenance costs. For a low-value high-volume product where margins are very tight, system disruptions are especially problematic. In this work we evaluate an advanced system strategy in which a network of biomass processing centers (depots) are utilized for storing and preprocessing biomass into stable, dense, and uniform material to reduce feedstock supply disruptions, and facility downtime in order to boost economic returns to the bioenergy industry. A database centric discrete event supply chain simulation model was developed, and the impact of operational disruptions on supply chain cost, inventory and production levels, farm metrics and facility metrics were evaluated. Three scenarios were evaluated for a 7-year time-period: (1) bale-delivery scenario with biorefinery uptime varying from 20 to 85%; (2) pellet-delivery scenario with depot uptime varying from 20 to 85% and biorefinery uptime at 85%; and (3) pellet-delivery scenario with depot and biorefinery uptime at 85%. In scenarios 1 and 2, tonnage discarded at the field edge could be reduced by increasing uptime at facility, contracting fewer farms at the beginning and subsequently increasing contracts as facility uptime increases, or determining alternative corn stover markets. Harvest cost was the biggest contributor to the average delivered costs and inventory levels were dependent on facility uptimes. We found a cascading effect of failure propagating through the system from depot to biorefinery. Therefore, mitigating risk at a facility level is not enough and conducting a system-level reliability simulation incorporating failure dependencies among subsystems is critical.

09 BIOMASS FUELS↗
DOE OSTI2019

Ultra-Long-Term Reliable Encapsulation Using an Atomic Layer Deposited HfO 2 /Al 2 O 3 /HfO 2 Triple-Interlayer for Biomedical Implants

Long-term packaging of miniaturized, flexible implantable medical devices is essential for the next generation of medical devices. Polymer materials that are biocompatible and flexible have attracted extensive interest for the packaging of implantable medical devices, however realizing these devices with long-term hermeticity up to several years remains a great challenge. Here, polyimide (PI) based hermetic encapsulation was greatly improved by atomic layer deposition (ALD) of a nanoscale-thin, biocompatible sandwich stack of HfO 2 /Al 2 O 3 /HfO 2 (ALD-3) between two polyimide layers. A thin copper film covered with a PI/ALD-3/PI barrier maintained excellent electrochemical performance over 1028 days (2.8 years) during acceleration tests at 60 °C in phosphate buffered saline solution (PBS). This stability is equivalent to approximately 14 years at 37 °C. The coatings were monitored in situ through electrochemical impedance spectroscopy (EIS), were inspected by microscope, and were further analyzed using equivalent circuit modeling. The failure mode of ALD Al 2 O 3 , ALD-3, and PI soaking in PBS is discussed. Encapsulation using ultrathin ALD-3 combined with PI for the packaging of implantable medical devices is robust at the acceleration temperature condition for more than 2.8 years, showing that it has great potential as reliable packaging for long-term implantable devices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Automating Component-Level Stress Measurements for Inverter Reliability Estimation

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Flicker, Jack (ORCID:0000000212232154)↗
DOE OSTI2024

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Binary INformation from Open Clusters Using SEDs (BINOCS) Project: Reliable Photometric Mass Determinations of Binary Star Systems in Clusters

We introduce a new binary detection technique, Binary INformation from Open Clusters using SEDs (binocs), which we show is able to determine reliable stellar multiplicity and masses over a much larger mass range than current approaches. This new technique determines accurate component masses of binary and single systems of the open clusters’ main sequence by comparing observed magnitudes from multiple photometric filters to synthetic star spectral energy distributions (SEDs), allowing us to systematically probe the binary population for low-mass stars in clusters for eight well-studied open clusters. We provide new deep, infrared photometric catalogs (1.2–8.0 μm) for the key open clusters NGC 1960 (M36), NGC 2099 (M37), NGC 2420, and NGC 2682 (M67), using observations from NOAO/NEWFIRM and Spitzer/IRAC. Using these deep multiwavelength catalogs, the binocs method is applied to these clusters to determine accurate component masses for unresolved cluster binaries. We explore binary fractions as a function of cluster age, Galactic location, and metallicity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

India Power Sector Reliability Analysis [Slides]

The objective of the study is to assess pathways for India to achieve its 2070 power sector goals reliably and cost-effectively by considering various supply- and demand-side factors to support planning by national stakeholders including the Central Electricity Authority (CEA).

08 HYDROGEN↗
DOE OSTI2015

Structural Testing at the NWTC Helps Improve Blade Design and Increase System Reliability; NREL (National Renewable Energy Laboratory)

Since 1990, the National Renewable Energy Laboratory’s (NREL's) National Wind Technology Center (NWTC) has tested more than 150 wind turbine blades. NWTC researchers can test full-scale and subcomponent articles, conduct data analyses, and provide engineering expertise on best design practices. Structural testing of wind turbine blades enables designers, manufacturers, and owners to validate designs and assess structural performance to specific load conditions. Rigorous structural testing can reveal design and manufacturing problems at an early stage of development that can lead to overall improvements in design and increase system reliability.

17 WIND ENERGY↗
DOE OSTI2015

Exploration of multi-fold symmetry element-loaded superconducting radio frequency structure for reliable acceleration of low- & medium-beta ion species

Reliable acceleration of low- to medium-beta proton or heavy ion species is needed for future high-current superconducting radio frequency (SRF) accelerators. Due to the high-Q nature of an SRF resonator, it is sensitive to many factors such as electron loading (from either the accelerated beam or from parasitic field emitted electrons), mechanical vibration, and liquid helium bath pressure fluctuation etc. To increase the stability against those factors, a mechanically strong and stable RF structure is desirable. Guided by this consideration, multi-fold symmetry element-loaded SRF structures (MFSEL), cylindrical tanks with multiple (n>=3) rod-shaped radial elements, are being explored. The top goal of its optimization is to improve mechanical stability. A natural consequence of this structure is a lowered ratio of the peak surface electromagnetic field to the acceleration gradient as compared to the traditional spoke cavity. A disadvantage of this new structure is an increased size for a fixed resonant frequency and optimal beta. This paper describes the optimization of the electro-magnetic (EM) design and preliminary mechanical analysis for such structures.

43 PARTICLE ACCELERATORS↗