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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗
DOE OSTI2025

Key Performance Indicators for Vehicle Grid Integration

Electric vehicle (EV) sales account for a rapidly growing portion of the light-duty vehicle market and a portion of medium and heavy-duty fleet vehicles. However, in many locations, charging stations will require costly utility grid upgrades with long lead times. Today, there are a few methods of smart charge management (SCM) which can reduce the costs and wait times for electric vehicle supply equipment (EVSE) interconnection approvals, as well as reduce impacts of the charging stations on the grid and on EV driver transportation costs. It is crucial for the EV charging industry to understand the vehicle grid integration (VGI) requirements for EVSE to prevent adverse impacts from EVSE interconnections and assure that charging loads and interconnections are affordable.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

The High-Luminosity Large Hadron Collider (HL-LHC) is currently undergoing upgrades to improve its luminosity. In parallel, this requires an upgrade to the Compact Muon Solenoid (CMS)’s Outer Tracker, consisting of Pixel-Strip (PS) and Strip-Strip (2S) modules that can accurately track the path of charged particles originating from the collisions. It follows that such complex modules call for extensive testing, requiring a sophisticated Data Acquisition (DAQ) system that can perform specific tests to assess their performance. In addition, errors caused by the hardware of a given testing station, and its associated data channel, need to be accurately identified to guarantee proper testing of modules. We have developed a software extension to the Phase-II Outer Tracker Analyzer of Test Outputs (POTATO), which is a specialized software designed to analyze and grade all of the module tests through a centralized database. This extension categorizes and analyzes module test results by its station and data channel. Its analysis can be used to identify trends in grading that indicate issues in these channels’ grading process rather than in the individual modules. This poster shows our methodology and results for identifying faulty data channels. Using this extension, we can quickly diagnose and address problems in our DAQ system, ensuring proper evaluation corrections for each module.

Chen, Angus [Fermilab]↗
DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

The High-Luminosity Large Hadron Collider (HL-LHC) is currently undergoing upgrades to improve its luminosity. In parallel, this requires an upgrade to the Compact Muon Solenoid (CMS)’s Outer Tracker, consisting of Pixel-Strip (PS) and Strip-Strip (2S) modules that can accurately track the path of charged particles originating from the collisions. It follows that such complex modules call for extensive testing, requiring a sophisticated Data Acquisition (DAQ) system that can perform specific tests to assess their performance. In addition, errors caused by the hardware of a given testing station, and its associated data channel, need to be accurately identified to guarantee proper testing of modules. We have developed a software extension to the Phase-II Outer Tracker Analyzer of Test Outputs (POTATO), which is a specialized software designed to analyze and grade all of the module tests through a centralized database. This extension categorizes and analyzes module test results by its station and data channel. Its analysis can be used to identify trends in grading that indicate issues in these channels’ grading process rather than in the individual modules. This poster shows our methodology and results for identifying faulty data channels. Using this extension, we can quickly diagnose and address problems in our DAQ system, ensuring proper evaluation corrections for each module.

Chen, Angus [Fermilab]↗
DOE OSTI2023

AWAKEN Site A1 - NREL Ceilometer (Vaisala CL51) / Derived Data

Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗
DOE OSTI2023

WFIP3 - BARG site - NREL Ceilometer (Vaisala CL51) / Derived Data

Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗
DOE OSTI2023

WFIP3 - BARG site - NREL Ceilometer (Vaisala CL51) / Derived Data

BARG ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗
DOE OSTI2025

WFIP3 - NOAA SHIP site - NREL Ceilometer (Vaisala CL51) / Derived Data

NOAA SHIP ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description: L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗
DOE OSTI2025

WFIP3 - CACO site - NREL Ceilometer (Vaisala CL51) / Derived Data

CACO ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description: L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗
DOE OSTI2025

SPRUCE Air 13C and 14C Isotopes, Marcell Experimental Forest, Minnesota, 2016-2025

This data set reports 13C (carbon) and 14C signatures of air from Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental study plots located in the S1-Bog at the Marcell Experimental Forest in northern Minnesota from 2016-2025 (2016-04-13 to 2025-09-24). Air measurements were collected approximately five times throughout each active growing season beginning in 2016 and included the 10 SPRUCE experimental plots (Plots 4, 6, 8, 10, 11, 13, 16, 17, 19, and 20) two ambient co-located ambient plots (Plots 7 and 21) and a site at the Marcell Experiment Station’s S2-Bog meteorological station (MET) located 1.7 km northeast of the SPRUCE experimental site on the S1-Bog. Air samples were assessed for both 13C- and 14C-CO2 (carbon-carbon dioxide) signatures. 13C and 14C isotopic signatures can be used in end-member analysis and C-cycle models to track the movement of C within the experimental ecosystem, calculate turnover times within plant tissues, and to test mechanisms used in models. This dataset contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Electricity and Gas IDS Dataset

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

Industrial Control↗
DOE OSTI2021

Electricity and Gas IDS Dataset

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

Industrial Control↗
DOE OSTI2026

The Influence of Shallow Subsurface Properties on Particle Motion in Acoustic-Seismic Coupling

Atmospheric acoustic waves transmit energy into the solid Earth through air-to-ground coupling. These waves are recorded by seismic sensors and provide insight into both atmospheric phenomena and subsurface properties. Interpreting these signals is often challenging because they are modulated by subsurface structure and the incidence angle of the acoustic wave. This study examines acoustic--seismic coupling generated by the 2012 Camp Minden Explosion, which was recorded by hundreds of seismoacoustic stations. We apply a novel technique to quantify the seismic particle motion, model coupled waves with a propagator matrix approach, and apply a Bayesian inversion to infer properties of the shallow subsurface. Our analysis reveals that prograde motion is widespread and focused in low shear-wave velocity regions, such as the Mississippi Embayment, and retrograde motion is more common in higher shear-velocity areas. Inversion results at some stations produce plausible subsurface models with strong waveform fits, while inversions at other sites are less successful. These results indicate prograde particle motion in air-to-ground coupled waves is more prevalent than previously recognized and may serve as a diagnostic for shallow velocity structure. Our comprehensive modeling and inversion framework provides a potential method to extract layered near-surface properties from acoustic-seismic coupling observations.

58 GEOSCIENCES↗
DOE OSTI2026

Did Seattle go silent during the Superbowl?

Large-scale cultural events such as sports games and music concerts are known to cause localised seismic excitation. In this paper we investigate whether a comparable far-field 'quietening' could be observed during Superbowl LX (February 8, 2026) in the city of Seattle, hometown of the Seattle Seahawks, as people stayed indoors to watch the game. We find that a number of stations in metropolitan Seattle and other urban or semi-urban areas of Washington state experienced their quietest Sunday afternoon in the last year in some or all of the 1-40 Hz frequency band which we examined. Comparable behaviour is not identified at stations in uninhabited areas of Washington. These results support the hypothesis that at least parts of Seattle and the wider state were comparatively 'silent' during Superbowl LX.

58 GEOSCIENCES↗
DOE OSTI2022

Modeling District Heating and Cooling Systems with URBANopt, GeoJSON to Modelica Translator, and the Modelica Buildings Library

The URBANopt project has successfully leveraged OpenStudio/EnergyPlus to model buildings and electrical systems at an urban scale; however, URBANopt has lacked the ability to model district thermal energy systems until recently. This paper will present the modeling infrastructure that was developed specifically for the analysis of district heating and cooling systems, and how it is integrated into the existing URBANopt framework. The paper also discusses the development of new models added to the Modelica Buildings Library to model various district energy system components including loads, energy transfer stations (ETS), distribution networks, and central plants. The paper describes how different building loads can be modeled including time series, TEASER reduced-order models, or Spawn of EnergyPlus models. URBANopt District Energy Systems allows the user to switch between the various configurations.

district heating and cooling↗
DOE OSTI2026

Anion-exchange membrane water electrolysis: insights from round-robin testing

As research and industrial interest in anion-exchange membrane water electrolysis (AEMWE) grows, there is an increasing need for reliable baselines and cross-lab validation of results. The wide variety of material sets and operating conditions under consideration for AEMWE has thus far limited efforts for standardization. In this study, round-robin testing was conducted in deionized water and KOH-based supporting electrolyte by 5 institutions from academia, national laboratories, and industry to provide baseline performance data and identify sources of cross-lab variability. Baseline membrane electrode assemblies were fabricated with commercial catalysts, membranes, and transport layers using standard techniques and tested using reagent-grade electrolytes, aiming for accessibility rather than state-of-the-art performance. From all tests, the average voltage at 1 A/cm 2 was 2.72 ± 0.17 V and 1.87 ± 0.03 V in deionized water and 0.1 M KOH, respectively. The maximum in-house and cross-lab variations at this current density were 118 mV and 476 mV in water and 60 and 88 mV in 0.1 M KOH. The KOH purity, station contamination, and temperature control were identified as possible factors affecting performance between labs, with in-house specific variation attributed to sample-to-sample differences in fabrication, cell assembly, and station contamination. This work provides a commercial baseline for the field and highlights the need for improved standardization and reproducibility in AEMWE research.

08 HYDROGEN↗
DOE OSTI2020

Future Vision for Autonomous Ocean Observations

Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass; and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as EGO for the emerging glider operations should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗