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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Status and Performance of the GlueX DIRC

The GlueX experiment is located in experimental Hall D at Jefferson Lab (JLab) and provides a unique capability to search for hybrid mesons in high-energy photoproduction, utilizing a $\sim$9 GeV linearly polarized photon beam. Since 2020 a Detector of Internally Reflected Cherenkov light (DIRC) has been in use at GlueX, utilizing components from the decommissioned BaBar DIRC. In this contribution we will discuss the status and performance of the GlueX DIRC with the data collected to date, including recent developments in the use of machine learning for particle identification.

Stevens, Justin [College of William and Mary, Will↗
DOE OSTI2021

Data From: Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO2 and N-2, Journal of Geophysical Research-Biogeosciences

This data package contains pumping data (.txt), parameter matrices, and R code (.R, .RData) to perform bootstrapping for parameter selection for the bioclogging model development. The pumping data were collected from the Russian River Riverbank Filtration site located in Sonoma County, California from 2010-2017 from three riverbank collection wells located alongside the study site. The pumping data is directly correlated with water table oscillations, so the code performs these correlations and simulates stochastic versions of water table oscillations. See Metadata Description.pdf for full details on dataset production. This dataset must be used with the R programming language. This dataset and R code is associated with the publication "Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO2 and N-2"This research was supported by the Jane Lewis Fellowship from the University of California, Berkeley, the Sonoma County Water Agency (SCWA), the Roy G. Post Foundation Scholarship, the U.S. Department of Energy, Office of Science Graduate Student Research (SCGSR) Program, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE-AC02-05CH11231, and the UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Boundary Layer Exploration of Aerosols and Clouds ON Ships (BEACONS)

BEACONS aims to demonstrate autonomous deployment of a shipborne system that routinely observes atmospheric, cloud, and aerosol properties. These observations enable scientists to study aerosol-cloud interactions, advancing energy resilience and the Department of Energy's mission to improve model prediction. Key Project Outcomes Autonomous deployment of aerosol and cloud measurement systems aboard a Pasha Hawaii Marjorie C commercial ship. Six-month campaign for continuous, high-resolution aerosol and cloud data collection with minimal crew intervention. Development and testing of instruments in two phases, advancing from essential to complex systems. AI-ready processed data on the BEACONS Data Platform for near-real-time data access. Validation of aerosol-cloud interaction hypotheses for improved modeling. Establishment of best practices for instrument deployment in marine environments. Scientific collaboration to support the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) mission.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning-enabled model-data integration for predicting subsurface water storage

Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change.

Lu, Dan↗
DOE OSTI2022

Cyberattack Scenarios for the Rancor Microworld Simulator

The digitization of new generation nuclear power plants increases the risk of cyber-physical attacks. Hence, the collection of data from operators that respond to realistic cyberattacks can provide a paramount insight on the correlation of cyber evidence and physical alerts. The simplicity and configurability of the Rancor Microworld Simulator, enables the training of non-expert operators in a PWR environment, and the extensive and tailored collection of data. Therefore, we develop the necessary tools and infrastructure in Rancor that allow such attacks to take place. Additionally, we perform these attacks to examine the attention and situation awareness of operators during malicious infiltrations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Development and Validation of a Scoring System for Abnormalities in the Gopher Frog (Rana capito)

Headstarting efforts are thought to be critical in supplementing populations of the at-risk Gopher Frog (Rana capito); however, recent efforts have occasionally resulted in juveniles with developmental abnormalities. In response, we developed a scoring system to collect quantitative data on the presence and severity of these developmental abnormalities. Our objective was to describe and validate the abnormality scoring system so that it can be used by all Gopher Frog headstarting facilities. The scoring system covers five primary conditions encompassing commonly observed abnormalities. Two groups of participants with different levels of prior experience working with Gopher Frogs assigned scores to a set of images presented to them for each condition. We used intra-class correlation coefficients (ICC) to test the scoring system for inter- and intra-rater agreement as well as agreement with the benchmark standard (established by the authors). We found high ICC values for inter-rater agreement, intra-rater agreement, and agreement to the benchmark standard indicating either excellent or good reliability for all five conditions and for all raters when grouped together. These findings support the reliability and validity of the proposed developmental abnormality scoring system. Gopher Frog headstarting facilities can implement this scoring system to assist in tracking the frequency and severity of abnormalities observed in future headstarting efforts. We hope that by creating a reliable scoring system for Gopher Frogs, it can provide an overall framework and serve as a valuable resource to evaluate abnormalities across any amphibian species.

abnormality↗
DOE OSTI2022

2.3.3.404 - National Lab and University Collaboration for MHK Instrumentation and Data Processing Tools

Field and laboratory validation, testing, demonstration, and operation are critical steps for increasing the technology readiness level of marine energy (ME) converters because they provide high-quality testing and performance data that are critical information used to feed all aspects of technology development. This project, in partnership with industry, enables the marine and hydrokinetic energy (MHK) community to reliably and efficiently collect, process, manage, and share quality data by facilitating access to and development of instrumentation, guidelines and data processing/QA tools. Under this project, open-source data processing code (MHKiT) and tools (ME Data Pipeline, MRE Code Hub, PRIMRE Code Catalog), instrumentation (loads measurements), data acquisition systems (miniDAQ), and measurement guidance tools (Telesto, high EMI guidance) were developed to facilitate the collection and processing of quality laboratory and field data. Overall, this project is intended to improve the quality of the data collected during laboratory and field demonstration projects by standardizing the collection and processing techniques, as well as by improving access to instrumentation, code, and measurement guidance. Quality data will, in turn, lead to improved knowledge capture following ME device testing.

data processing↗
DOE OSTI2020

Building Interfaces with Echo Using R

The following report details the functionality that was developed to directly query the MongoDB and read in echo records from within R, and the ability to create a cinema database directly from echo records. Both of these efforts used the data collected under the MINOS project for development. The nuclear facility under consideration is the High-Flux Isotope Reactor (HFIR) and co-located Radiochemical Engineering Development Center (REDC) at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of a medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are fabricated and processed and dissolved at REDC, in addition to other glove-box and hot-cell type activities. An overhead view of the facility is provided. The rest of the report is organized as follows: Section 2 describes how to query the MongoDB and load in echo records from within R, with background information on the database type, MongoDB, and the file format of echo records, hdf5, as well as lessons learned during the development phase. Section 3 details the functionality and usage of creating a cinema database directly from echo records within R. A series of appendixes display all the R code (Section 5), some example scripts where the code is used (Section 6), and useful links for further exploration (Section 7).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗
DOE OSTI2022

The Epidermal Microbiome Within an Aggregation of Leopard Sharks ( Triakis semifasciata ) Has Taxonomic Flexibility with Gene Functional Stability Across Three Time-points

The epidermis of Chondrichthyan fishes consists of dermal denticles with production of minimal but protein-rich mucus that collectively, influence the attachment and biofilm development of microbes, facilitating a unique epidermal microbiome. Here, we use metagenomics to provide the taxonomic and functional characterization of the epidermal microbiome of the Triakis semifasciata (leopard shark) at three time-points collected across 4 years to identify links between microbial groups and host metabolism. Our aims include (1) describing the variation of microbiome taxa over time and identifying recurrent microbiome members (present across all time-points); (2) investigating the relationship between the recurrent and flexible taxa (those which are not found consistently across time-points); (3) describing the functional compositions of the microbiome which may suggest links with the host metabolism; and (4) identifying whether metabolic processes are shared across microbial genera or are unique to specific taxa. Microbial members of the microbiome showed high similarity between all individuals (Bray–Curtis similarity index = 82.7, where 0 = no overlap, 100 = total overlap) with the relative abundance of those members varying across sampling time-points, suggesting flexibility of taxa in the microbiome. One hundred and eighty-eight genera were identified as recurrent, including Pseudomonas, Erythrobacter, Alcanivorax, Marinobacter, and Sphingopxis being consistently abundant across time-points, while Limnobacter and Xyella exhibited switching patterns with high relative abundance in 2013, Sphingobium and Sphingomona in 2015, and Altermonas, Leeuwenhoekiella, Gramella, and Maribacter in 2017. Of the 188 genera identified as recurrent, the top 19 relatively abundant genera formed three recurrent groups. The microbiome also displayed high functional similarity between individuals (Bray–Curtis similarity index = 97.6) with gene function composition remaining consistent across all time-points. These results show that while the presence of microbial genera exhibits consistency across time-points, their abundances do fluctuate. Microbial functions however remain stable across time-points; thus, we suggest the leopard shark microbiomes exhibit functional redundancy. We show coexistence of microbes hosted in elasmobranch microbiomes that encode genes involved in utilizing nitrogen, but not fixing nitrogen, degrading urea, and resistant to heavy metal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cryotrapping peroxide in the active site of human mitochondrial manganese superoxide dismutase crystals for neutron diffraction

Structurally identifying the enzymatic intermediates of redox proteins has been elusive due to difficulty in resolving the H atoms involved in catalysis and the susceptibility of ligand complexes to photoreduction from X-rays. Cryotrapping ligands for neutron protein crystallography combines two powerful tools that offer the advantage of directly identifying hydrogen positions in redox-enzyme intermediates without radiolytic perturbation of metal-containing active sites. However, translating cryogenic techniques from X-ray to neutron crystallography is not straightforward due to the large crystal volumes and long data-collection times. Here, methods have been developed to visualize the evasive peroxo complex of manganese superoxide dismutase (MnSOD) so that all atoms, including H atoms, could be visualized. The subsequent cryocooling and ligand-trapping methods resulted in neutron data collection to 2.30 Å resolution. The P 6 1 22 crystal form of MnSOD is challenging because it has some of the largest unit-cell dimensions ( a = b = 77.8, c = 236.8 Å) ever studied using high-resolution cryo-neutron crystallography. The resulting neutron diffraction data permitted the visualization of a dioxygen species bound to the MnSOD active-site metal that was indicative of successful cryotrapping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗
DOE OSTI2023

3P Program: Phenotyping X Prediction = Productivity (Final Scientific/Technical Report)

The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unconventional solitonic high-temperature superfluorescence from perovskites

Fast thermal dephasing limits macroscopic quantum phenomena to cryogenic conditions and hinders their use at ambient temperatures. For electronic excitations in condensed media, dephasing is mediated by thermal lattice motion. Therefore, taming the lattice influence is essential for creating collective electronic quantum states at high temperatures. Although there are occasional reports of high-T c quantum effects across different platforms, it is unclear which lattice characteristics and electron–lattice interactions lead to macroscopically coherent electronic states in solids. Here we studied intensity fluctuations in the macroscopic polarization during the emergence of superfluorescence in a lead halide perovskite and showed that spontaneously synchronized polaronic lattice oscillations accompany collective electronic dipole emission. We further developed an effective field model and theoretically confirmed that exciton–lattice interactions lead to a new electronically and structurally entangled coherent extended solitonic state beyond a critical polaron density. The analysis shows a phase transition with two processes happening in tandem: incoherent disordered polaronic lattice deformations establish an order, while macroscopic quantum coherence among excitons simultaneously emerges. Recombination of excitons in this state culminates in superfluorescence at high temperatures. Our study establishes fundamental connections between the transient superfluorescence process observed after the impulsive excitation of perovskites and general equilibrium phase transitions achieved by thermal cooling. By identifying various electron–lattice interactions in the perovskite structure and their respective role in creating collectively coherent electronic effects in solids, our work provides unprecedented insight into the design and development of new materials that exhibit high-temperature macroscopic quantum phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Time Distribution Analysis for Task Primitives to Support Dynamic Human Reliability Analysis

To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.

Dynamic Human Reliability Analysis↗
DOE OSTI2023

Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

58 GEOSCIENCES↗