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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modernizing the SNS Control System

The Spallation Neutron Source at Oak Ridge National Laboratory has been operating since 2006. An upgrade to double the machine power from 1.4 MW to 2.8 MW is currently underway and a project to add a second target station is in the preliminary design phase. While each project will add the controls needed for their specific scope, the existing control system hardware, software, and infrastructure require upgrades to maintain high availability and ensure the system will meet facility requirements into the future. While some systems have received new hardware due to obsolescence, much of the system is original apart from some maintenance and technology refresh. Software will also become obsolete and must be upgraded for sustainability. Further, requirements for system capacity can be expected to increase as more subsystems upgrade to smarter devices capable of higher data rates. This paper covers planned improvements to the integrated control system with a focus on reliability, sustainability, and future capability.

White, Karen S.↗
DOE OSTI2025

An On-Demand Electric Transit Case Study of New Rochelle, New York

This work explores the extent to which an on-demand mobility service utilizing lightweight electric vehicles (EVs) provides community and sustainability benefits in New Rochelle, New York. Travel and survey data from September 2019 through 2023 are used to describe the system and estimate impacts on travelers. The system was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service is utilized primarily for short trips (86% under 2 miles), and the small, right-sized EVs have carbon dioxide emissions associated with charging the fleet that are roughly one-quarter of the fleet emissions of conventional hybrid vans and nearly 50 times less than a fleet of diesel buses. Mapping current socio-spatial dynamics of travel demand can inform equity performance, as well as assist future planning and service area development and possible extensions to similar smaller, lower-density environments that are nearby and connected to major metropolitan areas. The findings in this case study suggest on-demand electric transit may be a significant and growing space for advancing clean and highly valued public mobility services. Sustainable public transport interventions that consider right-sized, electric, on-demand vehicles can help achieve improved accessibility and reduce energy use and greenhouse gas emissions. This work was presented at the Transportation Research Board (TRB) 2025 Annual Meeting on January 7, 2025.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha-1) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha-1. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.

Aging↗
DOE OSTI2021

TEAMER: Electrically Engaged Undulation System for Unmanned Underwater Vehicles

This TEAMER RFTS 1 (Request for Technical Support) project supported the flume tank testing of a long range, high endurance unmanned underwater vehicle (UUV) to monitor maritime space. Today, battery-powered remotely operated vehicles (ROVs) lack the duration to make persistent, wide-area data collection possible.The proposed solution, an Electrically Engaged UnduLation (EEL) drone, can sustain missions for longer duration through hydrodynamic energy harvesting. Power is provisioned via the piezoelectric effect, a material-led phenomenon that converts applied stress into electricity. The EEL subsystems include power, propulsion, navigation, ballast, telemetry, and instrumentation. By mimicking the gait of aquatic eels, EEL can counter currents during maneuvering and level-flight. The identified opportunity is in the future capability of extreme endurance UUVs in swarms. The specific goal for the EEL development is to expand the spatio-temporal coverage of the existing ocean observation mission by overcoming significant challenges of autonomous robotics. Some of the challenges presented include novel compliant mechanism for robust actuation, bio-inspired design to emulate efficient locomotion, smart material-based energy harvesting for sustained power, and swarming architecture through enabled autonomy.

16 TIDAL AND WAVE POWER↗
NASA NTRS2011

Data Service Provider Cost Estimation Tool

The Data Service Provider Cost Estimation Tool (CET) and Comparables Database (CDB) package provides to NASA s Earth Science Enterprise (ESE) the ability to estimate the full range of year-by-year lifecycle cost estimates for the implementation and operation of data service providers required by ESE to support its science and applications programs. The CET can make estimates dealing with staffing costs, supplies, facility costs, network services, hardware and maintenance, commercial off-the-shelf (COTS) software licenses, software development and sustaining engineering, and the changes in costs that result from changes in workload. Data Service Providers may be stand-alone or embedded in flight projects, field campaigns, research or applications projects, or other activities. The CET and CDB package employs a cost-estimation-by-analogy approach. It is based on a new, general data service provider reference model that provides a framework for construction of a database by describing existing data service providers that are analogs (or comparables) to planned, new ESE data service providers. The CET implements the staff effort and cost estimation algorithms that access the CDB and generates the lifecycle cost estimate for a new data services provider. This data creates a common basis for an ESE proposal evaluator for considering projected data service provider costs.

Fontaine, Kathy↗
DOE OSTI2023

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station’s capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station’s use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station’s open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗
DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1997

Pre-Flight Dark Forward Electrical Testing of the Mir Cooperative Solar Array

The Mir Cooperative Solar Array (MCSA) was developed jointly by the United States (US) and Russia to provide approximately 6 kW of photovoltaic power to the Russian space station Mir. After final assembly in Russia, the MCSA was shipped to the NASA Kennedy Space Center (KSC) in the summer of 1995 and launched to Mir in November 1995. Program managers were concerned of the potential for MCSA damage during the transatlantic shipment and the associated handling operations. To address this concern, NASA Lewis Research Center (LERC) developed an innovative dark-forward electrical test program to assess the gross electrical condition of each generator following shipment from Russia. The use of dark test techniques, which allow the array to remain in the stowed configuration, greatly simplifies the checkout of large area solar arrays. MCSA dark electrical testing was successfully performed at KSC in July 1995 following transatlantic shipment. Data from this testing enabled engineers to quantify the effects of potential MCSA physical damage that would degrade on-orbit electrical performance. In this paper, an overview of the principles and heritage of photovoltaic array dark testing is given. The specific MCSA dark test program is also described including the hardware, software, testing procedures and test results. The current-voltage (4) response of both solar cell circuitry and by-pass diode circuitry was obtained. To guide the development of dark test hardware, software and procedures, a dedicated FORTRAN computer code was developed to predict the dark 4 responses of generators with a variety of feasible damage modes. By comparing the actual test data with the predictions, the physical condition of the generator could be inferred. Based on this data analysis, no electrical short-circuits or open-circuits were detected. This suggested the MCSA did not sustain physical damage that affected electrical performance during handling and shipment from Russia to the US. Good agreement between the test data and computational predictions indicated MCSA electrical performance was amenable to accurate analysis and was well understood.

Kerslake, Thomas W.↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Processed Soil Respiration at the TRACE experimental Warming project, Aug 2015 - Sep 2017, Sabana, Luquillo, Puerto Rico

This data package contains processed measurements of soil carbon dioxide (CO₂) efflux collected using LI-COR LI-8100 soil respiration chambers at the Tropical Responses to Altered Climate Experiment (TRACE) located at the Sabana Field Research Station near Luquillo, Puerto Rico. The TRACE site is a mature, closed-canopy tropical wet forest within the Luquillo Experimental Forest. These data quantify soil surface CO₂ fluxes from both ambient (control) and experimentally warmed plots to evaluate how long-term soil warming affects belowground carbon cycling in tropical ecosystems. The data files include time-series tables of CO₂ flux (µmol CO₂ m⁻² s⁻¹), soil temperature (°C), and ancillary environmental variables, stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement intervals, and processing steps. These data were generated to address the research question: How does sustained soil warming influence soil respiration and carbon flux dynamics in tropical wet forests?

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

A torque, tension and stress corrosion evaluation of high strength A286 bolts

The problems associated with overtorque applied to the Booster Separation Motor (BSM) Igniter Adapter high strength 200 KSI (1379 Mpa) A286 CRES bolts and the threaded holes of the 7075-T73 aluminum alloy BSM cases are addressed. The evaluation included torque, tensile, and stress corrosion tests incorporating the A286 CRES bolts and the 7075-T73 aluminum alloy BSM cases. The tensile test data includes ultimate tensile load (UTL), Johnson's 2/3 yield load (J2/3YL), proportional limit load (PLL), and total bolt stretch. Torque tension data includes torque, torque induced load, and positive and negative break-away torque. Stress corrosion test data reflect the overtorque and the resulting torque induced loads sustained by the A286 CRES bolts torqued into a 7075-T73 aluminum alloy forged dome with threaded holes. After 60 days of salt fog exposure, the positive and the negative break-away torques, the subsequent mechanical property tensile test results, and the BSM dome threaded hole axial tensile pullout loads are reported.

Montano, J. W.↗
NASA NTRS1986

Early photometry of comet p/Halley - Development of the coma

The observation of comets over a large range of heliocentric distances can provide important information, taking into account, for instance, the appearance of true cometary characteristics. Photometric evidence is provided regarding coma formation at approximately 5.9 AU, giving attention to aspects of water-ice sublimation. New photometry of p/Halley was obtained when the comet was at 5.9 and 5.1 AU, during 1984 October and 1985 January, respectively. The new observations are compared with data obtained at larger heliocentric distances. It is argued that sustained mass loss from the nucleus began at about 5.9 AU, at a rate which is consistent with production by sublimation from a predominantly water-ice nucleus.

Meech, K. J.↗
NASA NTRS2011

Arctic Collaborative Environment: A New Multi-National Partnership for Arctic Science and Decision Support

The Arctic Collaborative Environment (ACE) project is a new international partnership for information sharing to meet the challenges of addressing Arctic. The goal of ACE is to create an open source, web-based, multi-national monitoring, analysis, and visualization decision-support system for Arctic environmental assessment, management, and sustainability. This paper will describe the concept, system architecture, and data products that are being developed and disseminated among partners and independent users through remote access.

Laymon, Charles A,↗
NASA NTRS2008

Self-Powered Wireless Sensors

NASA's integrated vehicle health management (IVHM) program offers the potential to improve aeronautical safety, reduce cost and improve performance by utilizing networks of wireless sensors. Development of sensor systems for engine hot sections will provide real-time data for prognostics and health management of turbo-engines. Sustainable power to embedded wireless sensors is a key challenge for prolong operation. Harvesting energy from the environment has emerged as a viable technique for power generation. Thermoelectric generators provide a direct conversion of heat energy to electrical energy. Micro-power sources derived from thermoelectric films are desired for applications in harsh thermal environments. Silicon based alloys are being explored for applications in high temperature environments containing oxygen. Chromium based p-type Si/Ge alloys exhibit Seebeck coefficients on the order of 160 micro V/K and low thermal conductance of 2.5 to 5 W/mK. Thermoelectric properties of bulk and thin film silicides will be discussed

Dynys, Fred↗