Reliable Forward Walking Parameters from Head-Track Data Alone.
Abstract not provided.
Wendt, Jeremy Daniel.↗
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Strategic future data collection for complex systems based on multiple reliability objectives
Abstract Not Provided
99 GENERAL AND MISCELLANEOUS↗
Dynamic Line Rating Forecast Time Frames
As dynamic line ratings (DLR) are used to support different use cases, the preferred data source varies according to the forecast horizon. The below guidance is one possibility to utilize weather forecasting. Note that the time-periods of forecasts and spatial resolution may be subject to change as National Oceanic and Atmospheric Administration (NOAA)/National Weather Service (NWS) periodically upgrades its forecast models and data servers. Moreover, Persistence/ML Observations advances in analytics may justify combining data sources for more reliable DLR forecasts. The list below gives a time period (t) followed by possible guidance for data in that interval.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Lower Snake River Dams Contribution to Grid Services
Hydroelectric generation and water storage have long been components of the clean energy mix, providing both reliable steady output and operational flexibility. As variable renewable energy sources such as wind and solar increasingly replace traditional generation, the role of all flexible resources—including hydropower—in balancing supply and demand continues to evolve. This study examined the contribution of the Lower Snake River (LSR) Dam plants to Bonneville Power Administration grid services in maintaining power system reliability within the Western Interconnection. By analyzing publicly available data, the study evaluated various reliability services through performance metrics including energy capacity, balancing and ramping, voltage and reactive power support, frequency response, and transmission impact. Results indicated that the LSR plants deliver services as expected based on their size, contributing to the balancing process, ramping capabilities, and operational reserves, particularly during peak load conditions and weather events, while also providing measurable frequency and voltage support to the grid.
13 HYDRO ENERGY↗
The SACADA database for human reliability and human performance
Lack of appropriate and sufficient human performance data has been identified as a key factor affecting human reliability analysis (HRA) quality especially in the estimation of human error probability (HEP). The Scenario Authoring, Characterization, and Debriefing Application (SACADA) database was developed by the U.S. Nuclear Regulatory Commission (NRC) to address this data need. An agreement between NRC and the South Texas Project Nuclear Operating Company (STPNOC) was established to support the SACADA development with aims to make the SACADA tool suitable for implementation in the nuclear power plants' operator training program to collect operator performance information. The collected data would support the STPNOC's operator training program and be shared with the NRC for improving HRA quality. This paper discusses the SACADA data taxonomy, the theoretical foundation, the prospective data to be generated from the SACADA raw data to inform human reliability and human performance, and the considerations on the use of simulator data for HRA. Each SACADA data point consists of two information segments: context and performance results. Context is a characterization of the performance challenges to task success. The performance results are the results of performing the task. The data taxonomy uses a macrocognitive functions model for the framework. At a high level, information is classified according to the macrocognitive functions of detecting the plant abnormality, understanding the abnormality, deciding the response plan, executing the response plan, and team related aspects (i.e., communication, teamwork, and supervision). The data are expected to be useful for analyzing the relations between context, error modes and error causes in human performance.
99 GENERAL AND MISCELLANEOUS↗
A Methodology to Evaluate the Grid Reliability Impact of Oscillations Induced by Large Loads
The rapid growth of hyperscale AI data centers is bringing renewed attention to the reliability risk that sustained forced oscillations pose to bulk power systems, with cyclic computational workloads emerging as a new forcing source. Unlike the broadband, stochastic disturbances from traditional industrial loads such as arc furnaces, AI training and inference facilities can inject large active power swings concentrated at specific frequencies over extended durations - characteristics that existing grid planning practices do not account for. While the North American Electric Reliability Corporation (NERC) has recognized this gap and called for system-level studies of large load interconnections, no standardized methodology exists to screen, simulate, and quantify these risks at the planning stage. This report presents the Risk Assessment Tool for Large Load-induced Events (RATLLE), a Python-based, publicly available script suite developed at the Pacific Northwest National Laboratory to evaluate bulk power system reliability risks from data center-induced oscillations. RATLLE implements a three-module workflow: a screening module that identifies vulnerable interconnection locations and excitable system modes; a simulation module that models cyclic data center load behavior using a commercial positive sequence simulation platform; and an analysis module that computes risk metrics and generates interactive visualization dashboards. The risk metrics, formulated around simulation observables, map oscillation impacts to a three-stage severity scale spanning latent equipment fatigue through imminent cascading failure. The methodology is demonstrated on two Western Electricity Coordinating Council (WECC) system models: a publicly available 240-bus reduced representation and a detailed 2031 Heavy Winter planning case. Case studies illustrate that even modest 50 MW forced oscillations at resonant frequencies can produce wide-area power swings, N-1 security constraint violations, and cascading generator trips through protection actions - outcomes that would not occur under normal operating conditions without oscillations present. The results underscore the need for standardized oscillation impact assessment in large load interconnection studies and provide a reproducible, extensible framework for utilities to adopt or customize within their existing planning workflows.
Biswas, Shuchismita↗
Solar-to-Grid: Trends in System Impacts, Reliability, and Market Value in the United States with Data Through 2019
With continued deployment of solar across the United States, assessing the interactions of solar with the power system is an increasingly important complement to studies tracking the cost and performance of solar plants. This project focuses on the historical contribution to reliability, trends in market value, and impacts on the bulk power system of solar deployed in the U.S. through the end of 2019.
14 SOLAR ENERGY↗
Solar-to-Grid: Trends in System Impacts, Reliability, and Market Value in the United States with Data Through 2020
With continued deployment of solar across the United States, assessing the interactions of solar with the power system is an increasingly important complement to studies tracking the cost and performance of solar plants. This project focuses on the historical contribution to reliability, trends in market value, and impacts on the bulk power system of solar deployed in the U.S. through the end of 2020. The scope of this analysis includes the seven organized U.S. wholesale power markets and is based on historical hourly solar generation profiles for each individual plant larger than 1 MW or county-level aggregate profiles for smaller solar. In addition, we present a limited set of results for ten utilities that are outside of the independent system operator (ISO)/regional transmission organization (RTO) markets.
14 SOLAR ENERGY↗
Correcting incompatible DN values and geometric errors in nighttime lights time series images
The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multi-year quantitative research. In this study we extend and improve previous studies on inter-calibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time series (NLT) image data for China and the United States (US) through four steps: inter-calibration, geometric correction, steady increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008 respectively and the US suffered from nighttime lights decay in large areas after 2001.
47 OTHER INSTRUMENTATION↗
PV Fleet Performance Data Initiative 2026 Update
We provide an update on the PV Fleet Performance Data Initiative at the 2026 PV Reliability Workshop. Our latest runs incorporate additional data sources and an integrated analysis pipeline run on our Kestrel HPC cluster. Initial degradation findings suggest that single-axis tracked PV systems exhibit higher performance loss rates than fixed-tilt systems, an increase of 0.5 %/yr, almost double. We discuss multiple methods for identifying stuck tracker rows, which are suspected to be a contributor to the enhanced degradation. Through satellite image detection and data-driven approaches we address the topic of identifying when stuck trackers are occuring and to what extent the problem exists. Preliminary results suggest that the increased performance loss detected for the tracked systems would be consistent with stuck tracker rows affecting on the order of 5% - 10% of the system.
14 SOLAR ENERGY↗
APOGEE Net: Improving the Derived Spectral Parameters for Young Stars through Deep Learning
Machine learning allows for efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of machine-learning approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main-sequence (MS) or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low-mass MS stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work, we generate labels for YSOs and low-mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict logg, T {sub eff}, and Fe/H for stars with Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra in the DR14 data set. This “APOGEE Net” has produced reliable predictions of logg for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-MS evolutionary tracks, and it correlates well with independently derived stellar radii. These values will be useful for studying pre-MS stellar populations to accurately diagnose membership and ages.
79 ASTRONOMY AND ASTROPHYSICS↗
Prediction Models for Dynamic Demand Response
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2 R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2 R, which we address in this paper. Our first contribution is the formal definition of D 2 R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2 R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2 R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2 R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2 R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2 R.
Aman, Saima↗
Fracture Toughness Evaluation for Spent Nuclear Fuel Clad Systems Using Spiral Notch Torsion Fracture Toughness Test
Many radioactive materials within the nuclear fuel cycle present a significant hazard. Such materials include spent nuclear fuel, high-level waste, legacy waste and other nuclear materials. These materials are often held in long-term storage as an interim stage within their lifecycle, including reuse or disposal. Safety and security of spent nuclear fuel (SNF) interim storage installations and follow-on SNF transportations to its final repository sites are very important, due to a great concentration of fission products, actinides and activation products. Fracture mechanics approach in applying to SNF system reliability investigation, especially for the high burn up (HBU) SNF, during SNF long term dry storage or SNF transportation is necessary due to the inherited flaws and inhomogeneity structures existed in a SNF system from the reactor operation. For instance, such as hydride and oxide formation, surface flaw induced by SNF assembly contact interactions, or internal flaws induced by pellet-clad mechanical interaction (PCMI). However, none of the existing fracture toughness data deal with fuel cladding specific geometry or SNF material conditions, such as cladding structure with the segment fuel pellets configuration and the PCMI mechanism. Consequently, the application of existing fracture toughness data to SNF system reliability investigation can be error prone; thus, the development of an industry-wide consensus fracture mechanics approach is needed. The objective of this research is to develop an in-situ fracture mechanics testing protocol, including the associated analytical procedure, that is suitable for evaluating the SNF fracture toughness. Fracture toughness data was obtained under quasi-static fracture loading using Oak Ridge National Laboratory (ORNL) developed Spiral Notch Torsion Test (SNTT) technology carried out on a biaxial tension/torsion tester. These data will be used to support SNF reliability investigation during long-tern SNF dry storage stage or in the follow-on SNF system transport to the final repository sites. SNTT has been a recent breakthrough in measuring fracture toughness for different materials, including metals, ceramics, concrete and polymers composites. Due to its high geometry constraint and unique loading condition, SNTT can be used to measure the fracture toughness with smaller specimens without concern of size effects. The application of SNTT to brittle materials has been proved to be successful. The micro-cracks induced by original notches in brittle materials could ensure crack growth in SNTT samples. Therefore, no fatigue pre cracks are needed for brittle materials specimens. The application of SNTT to the ductile material to generate valid toughness data will require a test sample with sufficient pre-crack length to increase the sample localized constraint at crack front. Fatigue pre-crack growth techniques with the associated compliance function estimated was developed for estimating the crack penetration depth to monitor the fatigue crack growth evolution. In order to extend SNTT approach to a thin shell cladding structure material, a new testing protocol and the associated analytical procedure for estimating the SNF fracture toughness was developed. The detailed SNTT approach and its estimated fracture toughness for the Zr-4 cladding structure with segment alumina inserts are presented in this report. The SNTT test results indicate that SNTT method is a reliable test approach with good repeatability in applying to Zr-4 cladding material. For the medium and the short crack length, the estimate J Q upon fracture for the baseline Zr-4 cladding is at 285.7 lb/in with 2-sigma uncertainty of 18.59 lb/in., and the associated K Q is at 61.4 Ksi√in. For a long crack length, the crack initialization orientation is apparently deviated from the principle tensile stress contour – 45° spiral crack front (i.e., Mode I + Mode III failure mechanism); and the estimated J Q is at 108 lb/in., the associated K Q is at 37.7 Ksi√in. The estimate Mode I J Q (along the 45° pitch principle tensile stress contour) is at 200 lb/in.; which indicate a significant reduction in the fracture toughness in a tubing structure under mixed mode loading condition, Mode I + Mode III, compared to that of Mode I (tensile stress dominated) alone.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?
Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.
54 ENVIRONMENTAL SCIENCES↗
Enhanced In-pile Instrumentation for Material Testing Reactors
An increasing number of U.S. nuclear research programs are requesting enhanced in-pile instrumentation capable of providing real-time measurements of key parameters during irradiations. For example, fuel research and development funded by the U.S. Department of Energy now emphasize approaches that rely on first principle models to develop optimized fuel designs that offer significant improvements over current fuels. To facilitate this approach, high fidelity, real-time data are essential for characterizing the performance of new fuels during irradiation testing. Furthermore, sensors that obtain such data must be miniature, reliable and able to withstand high flux/high temperature conditions. Depending on user requirements, sensors may need to obtain data in inert gas, pressurized water, or liquid metal environments. To address these user needs, in-pile instrumentation development efforts have been initiated as part of the Advanced Test Reactor (ATR) National Scientific User Facility (NSUF), the Fuel Cycle Research & Development (FCR&D), and the Nuclear Energy Enabling Technology (NEET) programs. This paper reports on recent INL achievements to support these programs. Specifically, an overview of the types of sensors currently available to support in-pile irradiations and those sensors currently available to MTR users are identified. In addition, recent results and products available from sensor research and development are detailed. Specifically, progress in deploying enhanced in-pile sensors for detecting elongation and thermal conductivity are reported. Results from research to evaluate the viability of ultrasonic and fiber optic technologies for irradiation testing are also summarized.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Photon Strength Functions at the low-energy tail of the GEDR Obtained from Different Reactions
In this contribution the main difficulties with the extraction of the PSFs from different experiments at {gamma}-ray energies below the particle emission are emphasized. A special attention is paid to a mutual comparison of PSFs deduced from (n,{gamma}), ({gamma},{gamma}') and {sup 3}He-induced reactions. It is shown that with the available techniques for extracting the data on PSFs the different reactions yield inconsistent results. At present, it is thus very difficult to make a reliable comparison of experimental data on these quantities with theoretical predictions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
"PoliMOR: A Policy Engine \"Made-to-Order\" for Automated and Scalable Data Management in Lustre"
Modern supercomputing systems are increasingly reliant on hierarchical, multi-tiered file and storage system architectures due to cost-performance-capacity trade-offs. Within such multi-tiered systems, data management services are required to maintain healthy utilization, performance, and capacity levels. We present PoliMOR, a pragmatic and reliable policy-driven data management framework. PoliMOR is composed of modular, single-purpose agents that gather file system metadata and enforce policies on storage systems. PoliMOR facilitates automated and scalable data management with customizable agents tailored to HPC facility-specific storage systems and policies. Our evaluations demonstrate the scalability and performance of PoliMOR both by its individual agents and as a collective entity. We believe PoliMOR is widely applicable across HPC facilities with large-scale data management challenges and will garner interest from the HPC community, given its flexible and open-source nature.
George, Anjus↗
Triboelectric Nanogenerator Repeatability and Reproducibility Study
The development of triboelectric nanogenerators (TENGs) has largely focused on optimizing output performance, often at the expense of other critical research considerations such as the development of reliable technical procedures. In particular, the reliability of reported results—specifically repeatability and reproducibility—remains underexplored and is frequently limited to brief discussion within available literature. Without rigorous validation through repeatability and reproducibility studies, the credibility and broader applicability of reported findings remain uncertain. This study addresses this gap by systematically evaluating the repeatability and reproducibility of TENG performance data. Five polymer materials—Kapton, polyethylene (PE), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF)—were investigated across all pairwise combinations of 25 total combinations for the reproducibility study and three selected pairs of the 25 samples were selected for the repeatability study. For each TENG pairing, we analyzed the methodology, experimental procedures, and resulting performance data to quantify consistency and reliability. The objective of this work is to assess the validity of the collected dataset and determine whether the observed performance trends are consistent for use in future TENG design and optimization studies. Establishing reliable and reproducible data is essential for advancing the development of high-output TENG systems and ensuring their dependable implementation in practical applications.
36 MATERIALS SCIENCE↗