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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 OSTI2022

The Source Physics Experiment (SPE) Science Plan

The Source Physics Experiment (SPE) series is a long-term NNSA research and development effort designed to improve U.S. arms control and nuclear nonproliferation verification and monitoring capabilities. The findings from the SPE will advance the United States’ nuclear explosion monitoring capabilities, particularly with respect to detection, discrimination and determination of yields associated with small nuclear explosions that can be lost amid the noisy seismo-acoustic background from other sources. The data generated from the SPE, a series of well-designed and recorded chemical explosions, will contribute to the development and validation of first-principles explosive source generated seismo-acoustic modeling codes. These codes will then facilitate the update of semi-empirical methods, currently based on historic test site data, such that key explosion observables can be reproduced, thus improving confidence in nuclear test monitoring in new areas and/or under novel emplacement conditions. The overall SPE project is comprised of both the development of the new explosion simulation codes and the chemical explosion test series. The chemical explosion test series will generate the empirical data required to both develop and validate the new simulation codes.

58 GEOSCIENCES↗
DOE OSTI2022

Utah FORGE: Well 16A(78)-32 Stimulation Tracer Test Results

This archive contains data from the tracer test performed during the Utah FORGE well 16A(78)-32 stimulation. This includes: 1. the raw data file from Pason in the csv format; 2. a one-page Word document from Pason that explains the headers and units used in their data file; 3. an Excel spreadsheet with concentration data and relevant Pason flow data; 4. a Word document with jpg versions of concentration charts (Figs. 1-3) and a tracer mass recovery chart (Fig. 4).

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

ModuleOT: A Hardware Security Module for Operational Technology: Preprint

With increasing penetration levels of distributed energy resources (DERs) on the distribution grid, as well as new technological advancements in the cyber space, new cyberattack vectors are being introduced, and the available attack surface is constantly increasing. Despite this increasing risk, the standard IEEE 1547-2018 does not yet recommend cybersecurity measures for DERs. To address this and to better protect data on the distribution grid - from the standpoints information security as well as operational security - ModuleOT has been developed. The module aims to significantly reduce cyberattack vectors by improving data privacy for user applications. This is accomplished by performing the core functions of encryption, authentication, authorization, certificate management, and user access control. The module integrates a custom security application with hardware cryptographic acceleration. The application secures all communications using Transmission Control Protocol over Internet Protocol (TCP/IP). These include the three most commonly used communications protocols for power systems information exchange: Modbus, Distributed Network Protocol 3 (DNP3), and Smart Energy Profile 2.0 (SEP2.0). These three protocols are also supported by IEEE 1547-2018 for all DER devices. This paper tests the data encryption/decryption feature on a physical networking test bed with emulated Modbus devices reporting grid data and presents the results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Challenges in practical button cell testing for hydrogen production from high temperature electrolysis of water

High temperature electrolysis of water using solid oxide electrochemical cells (SOEC) is a promising technology for hydrogen production with high energy efficiency and may promote decarbonization when coupled with renewable energy sources and excess heat from nuclear reactors. Over the past several decades there have been extensive scientific and engineering studies on cell materials and degradation behaviors that have greatly improved current density, decreased total resistance, and lowered degradation rates. Although the technology is now at a near-commercial level, maintaining consistency in cell testing and minimizing variance in practical testing environments is an often overlooked but crucial topic. To promote high quality data collection, testing procedures and balance of plant component details are extremely important to consider. This work discusses some key factors affecting the reproducibility of practical SOEC testing on the button cell level, namely, current collection layers, cell sealing procedures, the reliability of steam and hydrogen delivery systems, cell testing fixture design, and reduction procedures. To provide a baseline and a level of standardization for the SOEC community, this work also discloses details of the standard operating procedure and techniques adopted for o-SOEC testing at Idaho National Laboratory (INL).

08 HYDROGEN↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2022

Strain gauge for testing microreactor hexagonal core blocks in the Single Primary Heat Extraction and Removal Emulator

To support the development and deployment of microreactor technologies, experiments that help verify and validate reactor systems and components are performed at non-nuclear test facilities. The Single Primary Heat Extraction Removal Emulator at Idaho National Laboratory is one of these test facilities and was used to monitor a test article that has a prototypic geometry of a heat-pipe cooled microreactor core block. To collect crucial temperature and strain data during testing, temperature and strain sensing fiber optics were embedded to the surface of the test article using an ultrasonic additive manufacturing technique. To support and provide benchmark strain data for the embedded sensors, a commercial resistive strain gauge was attached. This report will discuss the results from the deployment of the commercial strain gauge which includes the setup/attachment strategies, data acquisition, and analysis of the strain data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Prediction of Climate-Specific Degradation Rate for Photovoltaic Encapsulant Discoloration

Encapsulant discoloration is a well-known field degradation mode of crystalline-silicon photovoltaic modules, particularly in the hot climate zones. The discoloration rate is influenced by several weathering factors, such as UV light, module temperature, and humidity, as well as the permeability of oxygen into the module. Here, a rate dependence model employing the modified Arrhenius equations to predict the degradation rate for encapsulant discoloration in different climates is presented. Two modeling approaches are introduced, which utilize the field and accelerated UV testing degradation data in conjunction with the field meteorological data to determine the acceleration factor for encapsulant browning. A novel method of accelerated UV stress testing at three simultaneous module temperatures in a single environmental chamber test run is implemented to estimate the activation energy for browning. The test was performed on three field-retrieved modules to capture the wear-out failure mechanism. The degradation in short-circuit current Isc rather than maximum power is used as a decisive parameter for the discoloration analysis. Furthermore, the developed model has been used to predict the Isc degradation rate for the Arizona field characterized by a hot and dry climate and is validated against the field-measured value. It has also been applied to other climate types, e.g., the cold and dry climate of New York.

14 SOLAR ENERGY↗
DOE OSTI2025

Cataloging Legacy Data from the Tritium Systems Test Assembly Program

The Tritium Systems Test Assembly (TSTA) at Los Alamos National Laboratory, operational from 1984 to 2001, was critical in advancing fusion fuel cycle technologies, including tritium storage, gas separation, and pumping. TSTA’s contributions, particularly in safe tritium operations, have influenced subsequent fusion projects. This paper discusses the ongoing effort to digitize and catalog TSTA’s historical data to create a searchable resource for the fusion research community. While the long-term objective is to develop a relational database for structured data management, the project remains in the early phase, with current efforts focused on scanning and indexing physical documents. Initial plans for database implementations are also presented, outlining key considerations for structure, query indexing, and standardization. As digitization progresses, future discussions will refine these implantation details to ensure an efficient and comprehensive system. This initiative aims to preserve critical legacy data, enhance the design of tritium system facilities, and support the next generation of fusion energy research.

42 ENGINEERING↗
DOE OSTI2024

Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios.To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

Aykac, Deniz↗
DOE OSTI2022

Datasets of a Multizone Office Building under Different HVAC System Operation Scenarios

Abstract This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗
DOE OSTI2021

Code validation of SAM using natural-circulation experimental data from the compact integral effects test (CIET) facility

The primary objective of this study is to validate the system analysis code, SAM, using experimental data from the Compact Integral Effects Test (CIET) experimental loop. SAM is a modern system analysis code being developed at Argonne National Laboratory for safety analysis of designs for advanced non-light water reactors (non-LWRs), such as sodium-cooled fast reactors, high-temperature gas-cooled reactors, and fluoride salt-cooled high-temperature reactors (FHRs). To support SAM code development for the wide range of non-LWR applications, it is of paramount importance to validate the code against experiments highly relevant to these reactor concepts. Additionally, the CIET facility, which was designed to reproduce the thermal-hydraulics response of FHRs under both forced- and natural-circulation conditions, has been identified and selected as one of the benchmark test facilities for SAM code validation. In this study, two sets of available CIET tests were selected for SAM code validation purposes, namely, power step change transient tests and steady-state natural-circulation tests. For all selected tests, SAM-predicted results show very good agreement with experimental data. The successful validation of SAM against these selected CIET experiments demonstrates that the computer code is well suited for thermal-hydraulics analysis of FHR designs.

42 ENGINEERING↗
DOE OSTI2024

Denoising of imaginary time response functions with Hankel projections

Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost—in quantum Monte Carlo simulations, the reduction of noise by a factor of two incurs a simulation cost of a factor of four. In this paper, we relate certain imaginary-time response functions to an inner product on the space of linear operators on Fock space. We then show that data with noise typically does not respect the positive definiteness of its associated Gramian. The Gramian has the structure of a Hankel matrix. As a method for denoising noisy data, we introduce an alternating projection algorithm that finds the closest positive definite Hankel matrix consistent with noisy data. We test our methodology at the example of fermion Green's functions for continuous-time quantum Monte Carlo data and show remarkable improvements of the error, reducing noise by a factor of up to 20 in practical examples. We argue that Hankel projections should be used whenever finite-temperature imaginary-time data of response functions with errors is analyzed, be it in the context of quantum Monte Carlo, quantum computing, or in approximate semianalytic methodologies. Published by the American Physical Society 2024

Yu, Yang (ORCID:0000000186178878)↗
DOE OSTI2022

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics. Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

neuropsychological↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗