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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rocket Launch Detection with Smartphone Audio and Transfer Learning

Rocket launches generate infrasound signatures that have been detected at great distances. Due to the sparsity of the networks that have made these detections, however, most signals are detected tens of minutes to hours after the rocket launch. In this work, a method of near-real-time detection of rocket launches using data from a network of smartphones located 10–70 km from launch sites is presented. A machine learning model is trained and tested on the open-access Aggregated Smartphone Timeseries of Rocket-generated Acoustics (ASTRA), Smartphone High-explosive Audio Recordings Dataset (SHAReD), and ESC-50 datasets, resulting in a final accuracy of 97% and a false positive rate of <1%. The performance and behavior of the model are summarized, and its suitability for persistent monitoring applications is discussed.

acoustics↗
DOE OSTI2016

Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS

Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions--native resolution (134 BAs), state-level, and NERC region level--and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Quantifying Message Aggregation Optimisations for Energy Savings in PGAS Models

Upon breaking past the exascale barrier, HPC systems are facing their greatest challenge yet - a power wall that must be addressed through new methods in both hardware and software. While energy costs are becoming a major issue at all levels, of particular concern is that of the network, as the relative cost of moving data is increasing faster than ever. The partitioned global address space (PGAS) model is critical within certain HPC domains, but is known to suffer from the small message problem, where irregular many-to-many access patterns result in congesting the network with excessive numbers of small messages. To address this, the conveyor aggregation library was developed to defer individual messages and group them for subsequent bulk processing. In this paper, we investigate its impact on energy use related to the network, with a focus on the Slingshot 11 interconnect. We will demonstrate that this strategy is not only highly performant, but also crucial to reducing energy footprints to remain within target power envelopes.

Welch, Aaron [ORNL]↗
DOE OSTI2018

Analysis of Solar Energy Aggregation Under Various Billing Mechanisms

Ongoing reductions in the cost of solar photovoltaic (PV) systems are driving increased installations by residential households. Various incentive programs such as feed-in tariff, net metering, net purchase, and sale that allow the prosumers to sell their generated electricity to the grid are also powering this trend. In this paper, we investigate sharing of PV systems among a community of households, who can also benefit further by pooling their production. Using cooperative game theory, we find conditions under which such sharing decreases their net total cost. We also develop allocation rules such that the joint net electricity consumption cost is allocated to the participants. These cost allocations are based on the cost causation principle. The allocations also satisfy the standalone cost principle and promote PV solar aggregation. Here, we also perform a comparative analytical study on the benefit of sharing under the mechanisms favorable for sharing, namely net metering, and net purchase and sale. The results are illustrated in a case study using real consumption data from a residential community in Austin, TX, USA.

14 SOLAR ENERGY↗
DOE OSTI2017

Fe-containing nanocomposites based on a biocompatible copolymer 1-vinyl-1,2,4-triazole with N-vinylpyrrolidone

New polymer nanocomposites containing iron oxide nanoparticles stabilized with a biocompatible copolymer of 1-vinyl-1,2,4-triazole with N-vinylpyrrolidone were produced. The synthesis was conducted using the method of chemical reduction of iron ions with hydrazine hydrate in an aqueous medium in the presence of a polymer matrix. The ESR spectroscopy data showed that the core—shell type nanoparticles were obtained. The core generally consistsed of zero-valence iron coated with an oxide shell. According to the data of transmission electron microscopy, the obtained polymer nanocomposites consisted of nanoparticles of mainly spherical shape with a diameter from 1 to 14 nm. Aggregates formed from individual stabilized nanoparticles of up to 75 nm in size (in most cases) were also observed. These aggregated particles were found to self-organize and form branched chains. Nanocomposites were characterized by a different particle-size distribution, which was determined by the initial ratio of the copolymer and the precursor of iron nanoparticles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Superionic conduction in solid polymer electrolytes – decoupling ion transport from segmental relaxation

Solvent-free, solid polymer electrolytes (SPEs) are promising candidates for next-generation, electrochemical energy storage systems due to their potential to enhance safety and performance, enable flexible device architectures, and streamline manufacturing processes. Conventional SPEs suffer from limited ionic conductivity due to the strong coupling between ion transport and (generally slow) polymer segmental relaxation. The realization of superionic conduction in SPEs, in which ions move faster than the structural relaxation of the polymers, requires a shift in design principles to promote this type of decoupled ion motion. In this perspective, we discuss how polymer architecture, ion–ion correlations, and ion–polymer interactions can unlock superionic behavior. We highlight several key design features, such as crystallinity, bulky side groups, high molecular weight, and percolating ionic aggregation, with a focus on creating low-barrier transport pathways in various polymer systems. We also demonstrate opportunities to combine polymer chemistry and data science through high-throughput and automated screening approaches to reveal how phase behavior, ion dynamics, and ionic interactions govern transport, thereby potentially enabling data-driven discovery of superionic polymer electrolyte materials.

Yang, Mengying [Univ. of Delaware, Newark, DE (Uni↗
DOE OSTI2017

FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool

Abstract Summary FQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data. Availability and implementation FQC is implemented in Python 3 and Javascript, and is maintained under an MIT license. Documentation and source code is available at: https://github.com/pnnl/fqc.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Study of structural order in porphyrin-fullerene dyad ZnDHD6ee monolayers by electron diffraction and atomic force microscopy

The structure of porphyrin-fullerene dyad ZnDHD6ee monolayers formed on the surface of aqueous subphase in a Langmuir trough and transferred onto solid substrates has been studied. The data obtained are interpreted using simulation of the structure of isolated molecules and their packing in monolayer and modeling of diffraction patterns from molecular aggregates having different sizes and degrees of order. Experiments on the formation of condensed ZnDHD6ee monolayers are described. The structure of these monolayers on a water surface is analyzed using {pi}-A isotherms. The structure of the monolayers transferred onto solid substrates is investigated by electron diffraction and atomic force microscopy. The unit-cell parameters of two-dimensional domains, which are characteristic of molecular packing in monolayers and deposited films, are determined. Domains are found to be organized into a texture (the molecular axes are oriented by the [001] direction perpendicular to the substrate). The monolayers contain a limited number of small 3D domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Life-cycle Analysis of Conversion of Post-Use Plastic via Pyrolysis with the GREET Model

In this report, we present an updated LCA of the PTF pathway developed at Argonne. Benavides et al. (2017) analyzed the environmental impact of producing ultra-low sulfur (ULS) diesel fuel from PUP via pyrolysis (the most common PTF technology). We used the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model and collected data from eight PTF companies to assess the potential energy and environmental benefits associated with this technology (Argonne, 2020). We conducted a survey and aggregated the dataset based on the different responses, which included plant capacity, process yields, feedstock composition, material and energy inputs, and outputs, and other parameters. We calculated the energy, greenhouse gas (GHG) emissions, and water consumption of its intermediate product pyrolysis oil and final product PUP-derived ULS diesel and compared the results to those metrics for their conventional counterparts (crude naphtha and petroleum ULS diesel fuel).

10 SYNTHETIC FUELS↗
DOE OSTI2014

Consumer Choice of E85 Denatured Ethanol Fuel Blend: Price Sensitivity and Cost of Limited Fuel Availability

The promotion of greater use of E85, a fuel blend of 85% denatured ethanol, by flex-fuel vehicle owners is an important means of complying with the Renewable Fuel Standard 2. A good understanding of factors affecting E85 demand is necessary for effective policies that promote E85 and for developing models that forecast E85 sales in the United States. In this paper, the sensitivity of aggregate E85 demand to E85 and gasoline prices is estimated, as is the relative availability of E85 versus gasoline. The econometric analysis uses recent data from Minnesota, North Dakota, and Iowa. The more recent data allow a better estimate of nonfleet demand and indicate that the market price elasticity of E85 choice is substantially higher than previously estimated.

09 BIOMASS FUELS↗
DOE OSTI2025

Bias correcting regional scale Earth system model projections: novel approach using empirical mode decomposition

Bias correction is a crucial step in using Earth system model outputs for assessments, as it adjusts systematic errors by comparing the model to observations. However, standard methods – ranging from mean-based linear scaling to distribution-based quantile mapping typically treat bias correction as a single-scale process, overlooking the fact that biases can manifest differently across daily, seasonal, and annual timescales. In this study, we propose a novel, timescale-aware bias-correction approach built on Empirical Mode Decomposition. By decomposing the meteorological signal into multiple oscillatory components and aggregating them to represent distinct timescales, we apply targeted corrections to each component, thereby preserving both short- and long-term structure in the data. Experimental illustrations show that the timescale-aware EMDBC framework matches the performance of conventional quantile-delta mapping (QDM) at the native daily scale and achieves progressively larger bias reductions at bi-weekly, seasonal, and annual scales. As a result, the proposed approach offers a more robust path to accurate and reliable Earth system projections, strengthening their utility for resilience and adaptation planning.

Ganguli, Arkaprabha [Argonne National Laboratory (↗
DOE OSTI2019

Fascinating Reporting with Postgres psql and sendmail

Among many things, psql, the Postgres command line tool, can create decent output to share with coworkers and customers, and that is where many Postgres users stop. The extreme limits of psql's formatting will be explored, delving into Unicode and HTML reports and the backslash/meta commands. Pretty reports are useless unless good data can be reported. The past and current developers of Postgres have packed a wonderful set functionality into the query language such as array aggregation and un-nesting. The staggering usefulness of "DISTINCT ON ()" will also be shown (without stepping into the quagmire of window functions). Environmentally conscious developers don't want to dump endless reports onto paper, so the power of psql is paired with the power of the UNIX/Linux sendmail command to generate electronic reports to send to coworkers and customers. The icing on the cake will be an example set of simple database tables to manage recipients of an automated reporting system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Highly Efficient Control Framework for Centralized Residential Charging Coordination of Large Electric Vehicle Populations

The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve global coordination benefits. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

TURBO: Terabits/s Using Reconfigurable Bandwidth Optics (Final Report)

Large scale, big science applications are generating petabytes of data that need to be shared among multiple locations. Moving a petabyte of data at 100 Gb/s takes 22 hours. Reducing this to single digit hours or below is clearly attractive and with future applications anticipating exabyte scale transfers, multi-terabit per second capacities are needed. This scale of capacity is available using optical systems. Core optical systems have aggregate per fiber capacities on the order of 10 Tb/s and more than 100 Tb/s system experiments have been realized in the lab. But large optical capacities are not just reserved for the core as similar system capacities are available in metro networks right up to the enterprise, campus, or data center. In general, the same technology is used today in the metro area as in the long-haul networks—reconfigurable optical add drop multiplexing (ROADM) node-based wavelength division multiplexing (WDM) systems. Data centers already support massive internal capacities on the scale of 100’s of Tb/s. As new photonic integrated technologies mature, for example through initiatives such as the Integrated Photonics Innovative Manufacturing Institute (IP-IMI), the cost of optical interfaces is expected to decrease, and higher capacity and higher performance interfaces will become more affordable. This set of circumstances creates the potential that Tb/s capacities will be available at the enterprise and campus level to support large scale science network applications.

42 ENGINEERING↗
DOE OSTI2022

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer

The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Investigation of the Interaction between Nafion Ionomer and Surface Functionalized Carbon Black Using Both Ultrasmall Angle X-ray Scattering and Cryo-TEM

In making a catalyst ink, the interaction between Nafion ionomer and catalyst support are the key factors that directly affect both ionic conductivity and electronic conductivity of the catalyst layer in a membrane electrode assembly (MEA). One of the major aims of this investigation is to understand the behavior of the catalyst support, Vulcan XC-72 (XC-72) aggregates, in the existence of the Nafion ionomer in a catalyst ink to fill the knowledge gap of the interaction of these components. The dispersion of catalyst ink not only depends on the solvent, but also depends on the interaction of Nafion and carbon particles in the ink. The interaction of Nafion ionomer particles and XC-72 catalyst aggregates in liquid media was studied using ultra small angle x-ray scattering (USAXS) and cryogenic TEM techniques. Carbon black XC-72) and functionalized carbon black systems were introduced to study the interaction behaviors. A multiple curve fitting was used to extract the particle size and size distribution from scattering data. The results suggest that the particle size and size distribution of each system changed significantly in Nafion + XC-72 system, Nafion + NH2-XC72 system, and Nafion + SO3H-XC-72 system, which indicates that an interaction among these components (i.e. ionomer particles and XC-72 aggregates) exists. The cryogenic TEM, which allows for the observation the size of particles in a liquid, was used to validate the scattering results and shows excellent agreement.

Catalyst Ink↗
DOE OSTI2019

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗