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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
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 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 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 OSTI2020

Reliable voltage scaled links for compressed data

Systems, apparatuses, and methods for reliably transmitting data over voltage scaled links are disclosed. A computing system includes at least first and second devices connected via a link. In one implementation, if a data block can be compressed to less than or equal to half the original size of the data block, then the data block is compressed and sent on the link in a single clock cycle rather than two clock cycles. If the data block cannot be compressed to half the original size, but if the data block can be compressed enough to include error correction code (ECC) bits without exceeding the original size, then ECC bits are added to the compressed block which is sent on the link at a reduced voltage. The ECC bits help to correct for any errors that are generated as a result of operating the link at the reduced voltage.

42 ENGINEERING↗
DOE OSTI2020

Charging Infrastructure Technologies: Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE)

Annual Merit review of the Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE) project. This project will demonstrate the value of smart charge management to reduce the impact of Electric Vehicles at Scale. Assess management of Plug-in Electric Vehicle (PEV) charging at scale to avoid negative grid impacts, identify critical strategies and technologies, and enhance value for PEV / EVSE / grid stakeholders.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Employing Fracture Statistics to Track Cell Reliability Through Module Fabrication

In this work we demonstrate how fracture statistics may be employed to track cell reliability through module fabrication steps. We were able to detect how cell cutting equipment could reduce cell fracture strain by 25 % as well as increase its variability. A smaller reduction was found to result from the ribbon soldering operation, and all cells measured were weaker when strained in an orientation perpendicular to the gridlines. Cell fracture measurements were found to be consistent with the number of cracked cells experienced in full sized modules exposed to mechanical loading. A structural mechanics model was created to elucidate how the requisite fracture strain is developed in a cell encapsulated within a PV module and demonstrates that this strain decreases more than 50 % from its center to edge when placed within the inner loading span of four-point flexure. The associated implications on the apparent cell strength is examined with an effective volume treatment.

14 SOLAR ENERGY↗
DOE OSTI2021

Reliability Barriers to Enhanced Solar PV Deployment: Selected Research Findings - State Outreach Technical Sessions

This presentation provides summaries of three reports selected from a body of work that examines potential reliability challenges related to planning future power systems with higher solar photovoltaic (PV) penetrations in the western United States. The three topics covered are 1) Power system flexibility requirements and supply; 2) Resource adequacy and the capacity credit of solar; 3) Simulating distributed energy resource responses to transmission system-level faults considering IEEE 1547 performance categories on three major WECC transmission paths. The full reports and related resources are available at the project website, https://www.westernenergyboard.org/western-interstate-energy-board/barrier-mitigation-to-enhanced-distributed-solar-photovoltaic/. This presentation is targeted toward a technical audience. A companion policy presentation provides higher-level summaries of the same material.

distributed energy resource↗
DOE OSTI2021

Reliability Barriers to Enhanced Solar PV Deployment: Selected Research Findings - State Outreach Policy Sessions

This presentation provides high-level summaries of three reports selected from a body of work that examines potential reliability challenges related to planning future power systems with higher solar photovoltaic (PV) penetrations in the western United States. The three topics covered are 1) Power system flexibility requirements and supply; 2) Resource adequacy and the capacity credit of solar; 3) Simulating distributed energy resource responses to transmission system-level faults considering IEEE 1547 performance categories on three major WECC transmission paths. The full reports and related resources are available at the project website, https://www.westernenergyboard.org/western-interstate-energy-board/barrier-mitigation-to-enhanced-distributed-solar-photovoltaic/. This presentation is targeted toward policy decision makers. A companion technical presentation provides more in-depth summaries of the same material.

distributed energy resource↗
DOE OSTI2021

Ultra-high reliability wireless communication systems and methods

Systems and methods for ultra-high reliability (UHR) wireless communications systems and methods are disclosed. The disclosed UHR wireless communications systems and methods make the networked components on a communications infrastructure robust to interference caused by unintentional jamming, intermittent connectivity, weather, and physical barriers.

Russell, John J.↗
DOE OSTI2021

Challenges of Interpreting and Applying New IPC-J-STD-001 Cleanliness Standards in High-Reliability Applications

The electronics industry has rapidly and widely embraced the replacement of ROSE testing with an SIR-focused approach to testing for certification of a qualified manufacturing plan. These new cleanliness documentation standards, housed in IPC J-STD-001 Rev. H, have just begun to impact electronics manufacturers as design and production teams work through how to meet them. This research focuses on the particular challenges of applying these new standards in high-reliability product applications, through the lens of interpreting SIR test results and what they mean for process capability, repeatability and diagnosing a need for process changes. The experiments examined a variety of factors that influence SIR results and suggest areas where SIR test plans can be greatly improved.

Capen, William↗
DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗
DOE OSTI2022

ADMS and DER Coordinated Capability Demonstrations to Assist Utilities to Increase Resilience and Reliability

The proliferation of distributed energy resources (DERs) presents opportunities and challenges to distribution grid operators. The National Renewable Energy Laboratory (NREL) and Survalent are collaborating to demonstrate advanced distribution management system (ADMS) and DER coordinated capability to assist utilities to increase resilience and reliability. ADMS and Distributed Energy Resource Management System (DERMS) Coordination for Peak Load Management (PLM). Fault Location, Isolation, and Service Restoration (FLISR) in the Presence of DERs. Grid-Edge Intelligent Distribution Automation System for Self-Healing Distribution Grids. These laboratory demonstrations use NREL's ADMS Test Bed.

ADMS↗