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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 415 records · Page 23
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DOE OSTI2022

A Statistical Interpolation Code for Ocean Analysis and Forecasting

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .

Srinivasan, Ashwanth↗
DOE OSTI2025

BOSC 2025, the 26th Bioinformatics Open Source Conference

The 26th annual Bioinformatics Open Source Conference (BOSC 2025, open-bio.org/events/bosc-2025) brought its community-driven focus on open-source bioinformatics and open science to the 2025 conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2025). Since its launch in 2000, BOSC has been the premier annual meeting covering open-source bioinformatics and open science. Framed by two keynote addresses and a thought-provoking panel discussion, the two-day conference included sessions dedicated to open data, analytic tools and pipelines, workflow platforms, knowledge representation, and the application of AI/ML. The first keynote talk was delivered by Christine Orengo: “Working together to develop, promote and protect our data resources: Lessons learnt developing CATH and TED.” A joint session with the Bio-Ontologies and Knowledge Representation (BOKR) track the second day of BOSC started with a keynote talk by Chris Mungall entitled “Open Knowledge Bases in the Age of Generative AI”. A closing panel on Data Sustainability, moderated by Mónica Muñoz Torres, featured panelists Scott Edmunds, Varsha Khodiyar, Tony Burdett, Nicky Mulder, and Chris Mungall. This year, the CollaborationFest collaborative work event that typically precedes or follows ISMB was incorporated as part of the main conference and organized by BOSC with help from the Function and 3D-SIG tracks.

bioinformatics↗
DOE OSTI2024

WELLBASE - An Interactive Platform for Wellbore Material Assessment

This project seeks to build an open-source wellbore material data repository with adequate material performance and contextual data to support Geological Carbon Storage (GCS). By appropriately evaluating the data types as mentioned earlier made available by the WELLBASE tool, stakeholders can make more informed decisions regarding well selections, risk assessment, and economic analysis for geologic carbon storage projects. Advanced Natural Language Processing models and other custom python scripts will be deployed in an automated process to extract unstructured data from documents, reports, and web applications and subsequently parse to more usable formats. The processed data will then be integrated into a robust and comprehensive database architecture, optimizing data accessibility, and usability for analytical purposes. The final data products will be accessible through a user-friendly visualization platform that will allow users to query and visualize the data, as well as download data in usable formats.

Tetteh, Daniel A.↗
DOE OSTI2021

D2U: Data Driven User Emulation for the Enhancement of Cyber Testing, Training, and Data Set Generation

Whether testing intrusion detection systems, conducting training exercises, or creating data sets to be used by the broader cybersecurity community, realistic user behavior is a critical component of a cyber range. Existing methods either rely on network level data or replay recorded user actions to approximate real users in a network. Our work is the first to produce generative models trained on actual user data (sequences of application usage) collected on endpoints. Once trained to the user's behavioral data, these models can generate novel sequences of actions %that appear to come from the same distribution as the training data. These sequences of actions are then fed to our custom software via configuration files, which replicate those behaviors on end devices. Notably, our models are platform agnostic and could generate behavior data for any emulation software package. In this paper we present our model generation process, software architecture, and an initial evaluation of the fidelity of our models. Our software is currently deployed in a cyber range to help evaluate the efficacy of defensive cyber technologies. We suggest additional ways that the cyber community as a whole can benefit from more realistic user behavior emulation. The data used to train our model, as well as sample configuration files produced by the model, are available at [redacted].

Oesch, T↗
DOE OSTI2022

Data collection from crystals grown in microfluidic droplets

Protein crystals grown in microfluidic droplets have been shown to be an effective and robust platform for storage, transport and serial crystallography data collection with a minimal impact on diffraction quality. Single macromolecular microcrystals grown in nanolitre-sized droplets allow the very efficient use of protein samples and can produce large quantities of high-quality samples for data collection. However, there are challenges not only in growing crystals in microfluidic droplets, but also in delivering the droplets into X-ray beams, including the physical arrangement, beamline and timing constraints and ease of use. Here, the crystallization of two human gut microbial hydrolases in microfluidic droplets is described: a sample-transport and data-collection approach that is inexpensive, is convenient, requires small amounts of protein and is forgiving. It is shown that crystals can be grown in 50–500 pl droplets when the crystallization conditions are compatible with the droplet environment. Local and remote data-collection methods are described and it is shown that crystals grown in microfluidics droplets and housed as an emulsion in an Eppendorf tube can be shipped from the US to the UK using a FedEx envelope, and data can be collected successfully. Details of how crystals were delivered to the X-ray beam by depositing an emulsion of droplets onto a silicon fixed-target serial device are provided. After three months of storage at 4°C, the crystals endured and diffracted well, showing only a slight decrease in diffracting power, demonstrating a suitable way to grow crystals, and to store and collect the droplets with crystals for data collection. This sample-delivery and data-collection strategy allows crystal droplets to be shipped and set aside until beamtime is available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring

The objective of this project is to develop a robust event diagnostics (RED) platform by integrating state-of-the-art tensor analytics and machine learning into real-time grid monitoring. The proposed platform can effectively analyze and discover the information hiding within the provided PMU data for effective real-time grid monitoring. The proposed RED platform provides a set of robust diagnostics tools for grid operation and management, including 1) data quality assessment, 2) data completion, 3) event detection, and 4) robust event classification. All the functionalities of the RED platform can help the operator to make informed decisions and respond in a timely manner. The developed RED platform will serve as an innovative advisory tool to reliably identify key events and discover new insights about the events and grid characteristics in the PMU data, and contribute to the efficient, safe, reliable operation and design of the nation’s electric system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Abstract Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deliverable D11 – Data Sharing, Storage, Security Protocols, and a Specification of a Potential Data Sharing Portal

Pacific Northwest National Laboratory (PNNL) and Technical University of Denmark (DTU) completed this deliverable as part of Work Package 2: Data Information Catalog for Distributed Wind Research for the International Energy Agency (IEA) Wind Technology Collaboration Programme Task 41: Enabling Wind to Contribute to a Distributed Energy Future. As part of the work plan, Deliverable D11 requires the development of data sharing, storage, and security protocols for metadata to be stored on the platform, if needed. The specification of a potential data sharing portal that expands on the catalog is also required.

17 WIND ENERGY↗
DOE OSTI2024

HERO WEC V1 Upgrade - 2023 Laboratory Testing (Raw Data)

This submission contains the original, unprocessed data from the 2023 Large Amplitude Motion Platform (LAMP) testing of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This data serves as a companion to MHKDR #520. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in TDMS format. Specifications of TDMS files can be found on the NI website. The TDMS files have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water (monochromatic sine wave) profile (not run in drivetrain configuration) - Heave - Heave only (monochromatic sine wave) profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) Reference documents: - "HERO WEC Lamp Test Run Log.xlsx": contains specifications for each test run - "Lamp Data Description.docx": provides detailed information about data types and processing methods For those interested in the processed data the authors have created a separate submission, MHKDR #520, linked below. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. Here, we focused our analysis on standard meteorological variables; fluxes of CO 2 , sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO 2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m -2 y -1 , mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m -2 y -1 ). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.

Carbon cycle↗
DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A new database website for nuclear level densities

We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform 10 Be Concentrations and Numerical Modeling

Rocky coast cliff retreat presents a hazard to coastal communities and infrastructure that is potentially amplified under rising sea level conditions, among other factors. Unfortunately, constraints on retreat rates are typically limited to those derived from imagery and maps spanning the last ~100 years. Here, we use a newly developed coupled model of shore platform profile evolution and cosmogenic radionuclide production that considers the influence of relative sea level (RSL) rise, weathering, material resistance, and wave height decay to model cliff retreat over millennial timescales and its potential drivers in Del Mar, California. We demonstrate the ability to use topographic and bathymetric measurements from a narrow shore platform along with a limited data set of nine cosmogenic 10Be concentrations extending ~125 m from the cliff base to obtain modeled cliff retreat rates that steadily range from 5.0 to 12.5 cm yr -1 over the last two millennia until 100 years before present. These rates are consistent with modern retreat rates of about 2–19 cm yr -1 here. RSL rise in Southern California remained relatively constant during the late Holocene, potentially explaining the relatively stable modeled cliff retreat rate over this time. We also explore the relative influence of weathering, material resistance, and wave erosion efficacy and find that both weathering and wave-driven erosion are necessary to replicate the measured data at this location, with the latter exerting a stronger control on model acceptance, suggesting that waves may provide a possible mechanism by which RSL rise may influence coastal cliff erosion in southern California.

58 GEOSCIENCES↗
DOE OSTI2026

Development and Validation of the Near-Miss Safety Score (NMSS) Framework for Heavy-Duty Vehicle Safety Assessment

Heavy-duty commercial vehicles present unique safety challenges due to their size, articulation dynamics, and operational complexity. As Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) become more common in Class 8 tractor-trailers, traditional crash-based metrics are no longer sufficient to evaluate safety performance. This study introduces the Near-Miss Safety Score (NMSS)—a quantitative, physics-informed framework developed as a leading indicator of safety for advanced commercial vehicle technologies. NMSS quantifies how close a vehicle or operator comes to a collision or safety-critical event by integrating vehicle kinematics (relative distance, velocity, and acceleration) with driver or system response latency and time-to-collision. A modifier function adjusts the base score for vehicle-specific and environmental factors such as trailer articulation, load distribution, braking condition, and roadway environment. The framework enables systematic evaluation of ADAS/ADS performance under a range of operational and degraded conditions. By capturing near-miss dynamics rather than relying on crash data, NMSS provides a proactive approach to risk assessment, accelerates technology validation, and enhances interpretability for regulators and fleet operators. The proposed NMSS was validated using data collected from a motorcoach platform, demonstrating the framework’s applicability to heavy-duty safety evaluation and performance benchmarking. Results and key insights are presented in this paper.

Siekmann, Adam [ORNL] (ORCID:0000000284653935)↗
DOE OSTI2025

Characterizing laser-heated polymer foams with simultaneous x-ray fluorescence spectroscopy and Thomson scattering at the Matter in Extreme Conditions Endstation at LCLS

Understanding the behavior of polymer foams at high energy density conditions is crucial to advance inertial fusion energy research. Here, we present a new experimental platform designed to measure the thermodynamic state of these materials at megabar pressures. At the Matter in Extreme Conditions Endstation of the Linac Coherent Light Source, we heat samples using an optical, high-intensity, femtosecond laser and dynamically probe them with ultra-short, coherent x-ray pulses of high peak brightness. We perform x-ray Thomson scattering measurements in forward and backward scattering geometries to capture both collective and non-collective electron behavior in the sample. Simultaneously, x-ray fluorescence spectroscopy is used to measure the emission from a mid-Z dopant, providing complementary information on the plasma conditions. By combining these techniques, we obtain temporally resolved temperature measurements of the transient warm dense matter states. Our initial experiments designed to benchmark the platform with carbon samples yielded data resolving the ultrafast response to laser heating with sub-picosecond resolution, measuring plasma temperatures exceeding 50 eV. These findings lay the foundation for precision studies of the dynamic evolution of laser-heated polymer foams.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Interactive Supercomputing With Jupyter

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist-user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high performance computing (HPC) workflows. Project Jupyter’s core design philosophy of extensibility, abstraction, and agnostic deployment, has allowed HPC centers like NERSC to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems.

97 MATHEMATICS AND COMPUTING↗