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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗
DOE OSTI2025

Ambient field seismology in critical zone hydrological sciences

Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.

58 GEOSCIENCES↗
DOE OSTI2025

Unique & challenging aspects of plutonium metal standards exchange program for actinide measurements

The Los Alamos National Laboratory exchange program is the only program of its kind for the distribution of plutonium (Pu) standards materials with a range of impurity contents to multiple laboratories for destructive measurements of elemental concentration. This paper discusses statistical methods used to address challenges in Pu metal exchange data by way of two case studies. Challenges include how to evaluate a data set when a large fraction of the values are minimum detection limits (MDLs), and how to determine potential outliers with limited in-formation on the true spread of the data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Glass-contact refractory of the nuclear waste vitrification melters in the United States: a review of corrosion data and melter life

The performance of the refractory lining in glass melters used for nuclear waste vitrification is critical to the melter reliability for long-term continuous operation. Monofrax® K-3, a high Cr 2 O 3 fused cast refractory material, has been widely used to build the liners of nuclear waste glass melters in the United States. Corrosion behaviour of Monofrax® K-3 refractory has been evaluated based on crucible-scale testing, inspection of the refractory components following scaled melter testing, and inspections of the Defense Waste Processing Facility (DWPF) melter refractory after service. The literature generally consists of empirical models based on short-term testing to describe refractory corrosion dependence on glass composition. Corrosion data from tests with longer testing times, at various temperatures, in the presence of molten salts, and with different redox reactions in the plenum atmosphere exist, may be insufficient to provide accurate refractory service life estimates. Additionally, the corrosion data collected under actual and scaled melter operating conditions are limited. Recommendations to achieve more direct correlation between the laboratory refractory corrosion data predictions and the observed melter service life are discussed to allow for more accurate predictions of the useful life of melter refractory linings.

Jin, Tongan↗
DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2025

Historical Bolide Infrasound Dataset (1960–1972)

We present the first fully curated, publicly accessible archive of infrasonic records from ten large bolide events documented by the U.S. Air Force Technical Applications Center’s global microbarometer network between 1960 and 1972. Captured on analog strip-chart paper, these waveforms predate modern digital arrays and space-based sensors, making them a unique window on meteoroid activity in the mid-twentieth century. Prior studies drew important scientific conclusions from the records but released only limited artifacts, chiefly period–amplitude tables and unprocessed scans, leaving the underlying data inaccessible for independent study. The present release transforms those limited excerpts into a research-ready resource. By capturing ten large events in the mid-20th century, the dataset constitutes a critical reference point for assessing bolide activity before the advent of modern space-based and digital ground-based monitoring. The multi-year coverage and worldwide distribution of events provide a valuable reference for comparing past and more recent detections, facilitating assessments of long-term flux and the dynamics of acoustic wave propagation in Earth’s atmosphere. The dataset’s availability in a consolidated format ensures straightforward access to waveforms and derived measurements, supporting a wide range of scientific inquiries into bolide physics and infrasound monitoring. By preserving these historical acoustic observations, the collection maintains a significant record of mid-20th-century meteoroid entries. It thereby establishes a basis for further refinement of impact hazard evaluations, contributes to historical continuity in atmospheric observation, and enriches the study of meteoroid-generated infrasound signals on a global scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Unsupervised learning for identifying events in active target experiments

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46 Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 Simonyan and Zisserman (2015) convolutional neural network. We study clustering performance on both data from a simulated 46 Ar experiment, and real events from the AT-TPC detector. We find that a -means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+-means approach finds high purity clusters of proton events for real experimental data. Here, we also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

DES Y1 results: Splitting growth and geometry to test $\Lambda$CDM

We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters—focusing on Ωm—are split into versions associated with structure growth (e.g., Ωmgrow) and expansion history (e.g., Ωmgeo). Once the parameters have been specified for the ΛCDM cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves both as a (largely) model-independent test for beyond-ΛCDM physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multiprobe DES data as [Phys. Rev. Lett. 122, 171301 (2019)PRLTAO0031-900710.1103/PhysRevLett.122.171301] : DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with Ωmgrow=Ωmgeo. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure Ωmgrow, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parametrization where we split both Ωm and w, but find that even our most constraining data combination is unable to separately constrain Ωmgrow and wgrow. Relative to ΛCDM, splitting growth and geometry weakens bounds on σ8 but does not alter constraints on h.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

High-density Lipoprotein (HDL) Structure and Function Proteomics (JM-DP1)

The purpose of this experiment was to investigate how the interactions between APOA1 and APOA2 on the surface of high-density lipoproteins (HDL) impact particle function by studying the effect of exogenous APOA2 on HDL structure through limited proteolysis. Interactions were investigated on HDL isolated from human blood plasma using structural proteomics tools such as chemical cross-linking and limited proteolysis (LiP). The structural proteomics data was acquired using a Q-Exactive HF-X mass spectrometer and processed using MaxQuant software (v.1.6.17.0).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Fluorine Limits and Impacts in High-Level Waste Glass Compositions

The impact of elevated fluorine (F) content on Hanford high-level waste (HLW) glasses has not previously been studied in detail. This effort represents the first systematic study to determine what F concentration limits should be used for the design of alkali-borosilicate-based Hanford Waste Treatment and Immobilization Plant (WTP) HLW glasses, and to document the technical basis for that limit. If alkali borosilicate glass made from Hanford HLW can accommodate a large amount of F, the large capital costs for complex sludge washing facilities may be avoided, as would much of the operational costs and negative schedule impacts associated with handling the large volumes of water required to dissolve these salts. In order to determine a limit for F in likely HLW glass compositions, an evaluation was conducted on glasses with F ≤ 0.90 mass% from previous nuclear waste glass studies. The collected dataset contains 239 glasses (232 HLW glasses and 7 LAW glasses) including 109 glasses with 0.9 ≤ F mass% ≤ 2.5, 116 with 2.5 < F mass% ≤ 8.0, and 14 with F mass% ≥ 8 (max. F mass% = 17.42). The collected composition and property data were analyzed to determine the basis for the F tolerance, i.e. the maximum F concentration that can be processed without potential issues. Fluorine volatility, product consistency test (PCT) response, liquidus temperature (T L ), glass melt viscosity, and crystallinity have been investigated. No limits for F concentration can be made based on F volatility, T L , or glass melt viscosity, because the data show that high F in glasses do not indicate, with high probability, being restricted by those property constrains. However, crystallinity and PCT response were used to estimate the F tolerance. The results show that glasses with high F (≥ 0.90 mass%) are more likely to form large fractions of F-containing crystal phases which may increase PCT responses, i.e. decrease the glass durability. Based on the results of crystallinity and PCT data, the F tolerance of 4.5 mass% is estimated. There is no evidence of other glass components, such as calcium oxides and alkali metal oxides have combined impacts with F on the glass properties. Overall, the available high-F glass data is limited, especially in the designed HLW glass composition regions. Future work on formulation and testing of HLW glasses with F ≥ 0.9 mass% will close the data gaps and expand operational flexibility with respect to the fluoride tolerances. Volatility of F from melters and corrosion of materials in contact with glass melts are important for processing of high-F wastes; yet no test data are currently available. It is recommended tests be conducted to address these two potential issues.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

MINERvA s Open Data Product: A First for Neutrino Data Preservation

Access to information on neutrino nucleus interactions is critical to the success of all neutrino oscillation experiments. MINERvA's rich dataset covers a range of energies and nuclei unique amongst experiments, and as such is critical to the community in building the important shared knowledge needed to unravel the mysteries of the neutrino. In particular, its dataset provides the greatest statistical coverage in in the range of neutrino energies pertinent for DUNE until DUNE's near detector begins operation. Historically, such significant datasets in neutrino physics have been preserved primarily through their published results. While meaningful and useful, this limits the ability to explore the data to its fullest extent as new perspectives continue to form. MINERvA has undertaken a major effort to break this trend and preserve its data in a format to be as analyzable as possible from outside the collaboration. This has culminated in the officially-released MINERvA Open Data Product for the community to take advantage of and utilize. Maintaining direct access to the dataset in an analyzable form will allow new insights to continue to be extracted indefinitely. This talk will cover the contents of this product, the information included (and excluded), the tools provided to utilize the product effectively, the support MINERvA intends to provide in its use, and some lessons learned through the process.

Last, David [Rochester U.] (ORCID:0000000245147183↗
DOE OSTI2023

Examining the Impact of Data Layout on Tape on Data Recall Performance for ATLAS

Increases in data volumes are forcing high-energy and nuclear physics experiments to store more frequently accessed data on tape. Extracting the maximum performance from tape drives is critical to make this viable from a data availability and system cost standpoint. The nature of data ingest and retrieval in an experimental physics environment make achieving high access performance difficult given the inherent limitations of magnetic tape. Tailoring the layout of data on tape is one key to improving read performance. This paper highlights the work in progress to characterize ATLAS data ingested in the tape system, understand how data layout, i.e. file co-location on tape and file distribution over tapes, affect read performance and how optimal data layout might be achieved in a production environment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk

We present a surrogate modeling framework for conservatively estimating measures of risk from limited realizations of an expensive physical experiment or computational simulation. We adopt a probabilistic description of risk that assigns probabilities to consequences associated with an event and use risk measures, which combine objective evidence with the subjective values of decision makers, to quantify anticipated outcomes. Given a set of samples, we construct a surrogate model that produces estimates of risk measures that are always greater than their empirical estimates obtained from the training data. These surrogate models not only limit over-confidence in reliability and safety assessments, but produce estimates of risk measures that converge much faster to the true value than purely sample-based estimates. We first detail the construction of conservative surrogate models that can be tailored to the specific risk preferences of the stakeholder and then present an approach, based upon stochastic orders, for constructing surrogate models that are conservative with respect to families of risk measures. The surrogate models introduce a bias that allows them to conservatively estimate the target risk measures. We provide theoretical results that show that this bias decays at the same rate as the L 2 error in the surrogate model. Our numerical examples confirm that risk-aware surrogate models do indeed over-estimate the target risk measures while converging at the expected rate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗
DOE OSTI2021

Distributed ADMM Using Private Blockchain for Power Flow Optimization in Distribution Network With Coupled and Mixed-Integer Constraints

The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.

25 ENERGY STORAGE↗
DOE OSTI2024

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

59 BASIC BIOLOGICAL SCIENCES↗