Search NASA⌕ Search

SEARCH · Search NASA

Results for “Green Software”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

AmeriFlux FLUXNET-1F US-Me7 Metolius Low Burn Severity Subcanopy

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Me7 Metolius Low Burn Severity Subcanopy. This is the FLUXNET version of the carbon flux data for the site US-Me7 Metolius Low Burn Severity Subcanopy produced by applying the standard ONEFlux (1F) software. Site Description - This is a sub-canopy flux site measuring a low burn severity segment of the 2020 green ridge fire. Intact ponderso pine canopy with a mean stand age of 73 years old as of 2022. Understory largely absent.

Hanson, Chad [Oregon State University]↗
DOE OSTI2021

Coupled cluster Green's function: Past, Present, and Future

Coupled cluster Green's function (CCGF) approach has drawn much attention in recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. Here, we will present a brief review of the history of how the Green's function method evolved with the wavefunction, early and recent development of CCGF theory, and more recently scalable CCGF software development. We will highlight some of the recent applications of CCGF approach and propose some potential applications that would emerge in the near future.

Peng, Bo↗
NASA NTRS1994

Optimization of 100-meter Green Bank Telescope

Candidate designs for NRAO's 100-m clear-aperture radio telescope were evaluated and optimized by JPL using JPL-developed structural optimization and analysis software. The weight of a non-optimum design was reduced from 9.4 million pounds to 9.2 million pounds. The half-pathlength error due to gravity deformations was reduced from 0.041-inch rms to 0.034-inch rms.

Strain, Douglas↗
NASA NTRSDate not supplied

Fracture Mechanics Testing of Titanium 6AL-4V in LMP-103S Propellant

Propellant tank safe-life analysis is a spacecraft propulsion subsystem Launch Range processing requirement. The material property inputs for the safe-life analysis include the stress intensity factor for environment-assisted cracking (KEAC). The KEAC for titanium alloy Ti 6Al-4V in LMP-103S propellant was determined experimentally using an innovative test method following ASTM E1681, Standard Test Method for Determining Threshold Stress Intensity Factor for Environment-Assisted Cracking of Metallic Materials. This effort was a collaboration between NASA Goddard Space Flight Center and NASA Kennedy Space Center to expose the test specimens in the propellant, while loaded in the test fixtures, for 1000- hour (42 days) at 50°C. Two test rounds have been completed to date; 1) testing mechanically loaded specimens but without propellant and 2) testing mechanically loaded specimens wetted with LMP-103S propellant. The dry test was to evaluate the crack growth without exposure to the LMP-103S propellant, and further assess the un-wetted stress intensity factor for the full 1000-hour duration. The second round of testing included the propellant soak for the requisite 1000- hour at 50°C. Current results show that the threshold intensity factor for environment-assisted cracking (exposed to LMP103S) is 22.5 ksi√in for the bulk material and 36. 2 ksi√in for the weld material. A generic ‘candidate’ tank geometry and material thickness is developed for a damage tolerance of known surface cracks below the nondestructive inspection technique detection limit. Evaluations with and without the use of a threshold intensity factor for environment-assisted cracking is performed for the required 4x propellant tank service life using the software tool NASGRO. SP2024_00239

Green propulsion↗
DOE OSTI2022

GeoTGo: AI/ML software for development of community geothermal resources

For effective and equitable outcomes in achieving the national goal of net-zero carbon emissions, communities must be not only included, but even lead the implementation of innovative green-energy technologies. Collaborations with communities should happen through informed decision-making, community-centered research and engagement of stakeholders at the local, state, and regional levels. Community-led research and implementation are fundamental to achieving success. These collaborations include rule makers, environmental regulators, clean energy industries, and technology researchers and developers. Unfortunately, many green infrastructure initiatives still adhere to a top-down and expert-driven process of site selection and design without awareness and acknowledgment of public engagement needs. This can lead to costly delays, including lawsuits, and ultimately less than desired or lacking outcomes as well as missed opportunities1. Geothermal, like many new technologies whose social and economic impacts are not fully understood, often cause disproportionately high adverse effects on disadvantaged communities. These effects can be related to human health, environmental, climate, and other cumulative impacts, as well as the accompanying economic challenges of these impacts. We are focusing our work on the needs of the New Mexico Native American Pueblos and Tribes (NMP&T). To address these needs, we are developing a novel web-based interactive software and user friendly interface called GeoTGO (https://geotgo.com) that provides everything that is needed for communities to better understand and develop their geothermal resources. We will bridge the gap between technology advancements and community needs by facilitating the interactions between the geothermal industry, regulators, stakeholders, and end-users. GeoTGO will merge data, software (including data analysis, text mining, artificial intelligence, and modeling tools), knowledge, expertise, and experience to provide fast processing and dissemination of the latest information about cutting-edge geothermal technologies to users and communities. More information about the project is available at https://envitrace.com/projects/geotgo.html.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗
NASA NTRS2001

Improving the Capture and Re-Use of Data with Wearable Computers

At the Goddard Space Flight Center, members of the Real-Time Software Engineering Branch are developing a wearable, wireless, voice-activated computer for use in a wide range of crosscutting space applications that would benefit from having instant Internet, network, and computer access with complete mobility and hands-free operations. These applications can be applied across many fields and disciplines including spacecraft fabrication, integration and testing (including environmental testing), and astronaut on-orbit control and monitoring of experiments with ground based experimenters. To satisfy the needs of NASA customers, this wearable computer needs to be connected to a wireless network, to transmit and receive real-time video over the network, and to receive updated documents via the Internet or NASA servers. The voice-activated computer, with a unique vocabulary, will allow the users to access documentation in a hands free environment and interact in real-time with remote users. We will discuss wearable computer development, hardware and software issues, wireless network limitations, video/audio solutions and difficulties in language development.

Pfarr, Barbara↗
DOE OSTI2021

Software-defined Networking for Energy Delivery Systems (SDN4EDS): An Architectural Blueprint (Final Report)

This is the initial version of a reference for suppliers and energy companies of all sizes to deploy networks based on software-defined networking technology (SDN) to improve reliability, reduce cyber security attack surface, and facilitate mitigation of adversarial behavior. It is a living document and will progress over the life cycle of the Software-Defined Networking for Energy Delivery Systems (SDN4EDS) project. Version 2 of this report provides information on the Red Team tabletop assessment performed against the initial reference architecture. Version 3 of this report updates the reference architecture with lessons learned from the Red Team tabletop assessment, as well as provides additional details for the use cases. It also provides information on the decision process that could be used by an organization when considering deploying SDN in their environment. The final version of this report consolidates all the interim reports generated by the project into a final report. It also draws from PNNL’s experience in deploying SDN to make recommendations on how SDN could be deployed in a utility environment, and provides rationale for those decisions allowing individual utilities to make risk-based and knowledge-based decisions on how to best deploy SDN in their own environment

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Software-defined Networking for Energy Delivery Systems (SDN4EDS): An Architectural Blueprint (Final Summary Report)

This is the initial version of a reference for suppliers and energy companies of all sizes to deploy networks based on software-defined networking technology (SDN) to improve reliability, reduce cyber security attack surface, and facilitate mitigation of adversarial behavior. It is a living document and will progress over the life cycle of the Software-Defined Networking for Energy Delivery Systems (SDN4EDS) project. Version 2 of this report provides information on the Red Team tabletop assessment performed against the initial reference architecture. Version 3 of this report updates the reference architecture with lessons learned from the Red Team tabletop assessment, as well as provides additional details for the use cases. It also provides information on the decision process that could be used by an organization when considering deploying SDN in their environment. The final version of this report consolidates all the interim reports generated by the project into a final report. It also draws from PNNL’s experience in deploying SDN to make recommendations on how SDN could be deployed in a utility environment, and provides rationale for those decisions allowing individual utilities to make risk-based and knowledge-based decisions on how to best deploy SDN in their own environment. This summary report provides a higher-level overview of the project reports. Readers interested in additional detail, including results of the Red Team assessments and the final configuration, are encouraged to read the full final report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1980

As-built design specification for P1A software system modified display subsystem

This document contains the design of the proportional estimate processor which was written to satisfy the software requirement of Part A of the P1A experiment. The purposes of the project are: (1) to select the dots to be labelled; (2) to create tables of green numbers and brightness values for all selected dots per acquisition; (3) to create scatter plots of green numbers vs brightness for each acquisition for all selected dots. If labels have been provided then scatter plots of only categories of interest can be optionally produced; and (4) to produce trajectory plots of green number vs brightness at differing acquisition times for each dot. These plots need to be in the same order as the list of selected dots. When labels are provided only plots of dots of categories of interest are to be produced.

Horton, C. L.↗
NASA NTRS1982

Rangeland biomass estimation demonstration

Because of their sensitivity to chlorophyll density, green leaf density, and leaf water density, two hand-held radiometers which have sensor bands coinciding with thematic mapper bands 3, 4, and 5 were used to calibrate green biomass to LANDSAT spectral ratios as a step towards using portable radiometers to speed up ground data acquisition. Two field reflectance panels monitored incoming radiation concurrently with sampling. Software routines were developed and used to extract data from uncorrected tapes of MSS data provided in NASA LANDSAT universal format. A LANDSAT biomass calibration curve estimated the range biomass over a four scene area and displayed this information spatially as a product in a format of use to ranchers. The regional biomass contour map is discussed.

Newton, R. W.↗
DOE OSTI2020

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Generalizable, fast, and accurate DeepQSPR with fastprop

Abstract Quantitative Structure–Property Relationship studies (QSPR), often referred to interchangeably as QSAR, seek to establish a mapping between molecular structure and an arbitrary target property. Historically this was done on a target-by-target basis with new descriptors being devised to specifically map to a given target. Today software packages exist that calculate thousands of these descriptors, enabling general modeling typically with classical and machine learning methods. Also present today are learned representation methods in which deep learning models generate a target-specific representation during training. The former requires less training data and offers improved speed and interpretability while the latter offers excellent generality, while the intersection of the two remains under-explored. This paper introduces , a software package and general Deep-QSPR framework that combines a cogent set of molecular descriptors with deep learning to achieve state-of-the-art performance on datasets ranging from tens to tens of thousands of molecules. provides both a user-friendly Command Line Interface and highly interoperable set of Python modules for the training and deployment of feedforward neural networks for property prediction. This approach yields improvements in speed and interpretability over existing methods while statistically equaling or exceeding their performance across most of the tested benchmarks. is designed with Research Software Engineering best practices and is free and open source, hosted at github.com/jacksonburns/fastprop.

Burns, Jackson W. (ORCID:0000000206579426)↗
DOE OSTI2021

The Modeling of Synfuel Production Process: ASPEN Model of FT production with electricity demand provided at LWR scale

Synfuels, or electro-fuels (e-fuels) have the unique potential to significantly reduce greenhouse gas (GHG) emissions across the transportation sector. This is especially true for applications with substantial payloads and daily miles traveled, such as long-haul heavy-duty vehicles, rail locomotives, marine vessels and aviation aircrafts that are challenging to directly electrify via battery or fuel cell powertrain technologies. Synfuels, or electro-diesel/electro-jet fuels, have similar properties with the incumbent petroleum fuels, compatible with current infrastructure but have much lower GHG emissions relative to the petroleum counterpart, because they utilize waste carbon dioxide (CO2) streams and green hydrogen (H2) sourced from electrolysis. To achieve substantial reductions in GHG emissions, electricity sources must be zero carbon or near-zero carbon, which is the case with solar, wind, hydro and nuclear power. Compared to the intermittency of solar, wind and hydro, nuclear energy provides a steady energy source. In addition, it’s advantageous for nuclear power to produce synfuels because it provides not only near-zero carbon electricity to displace grid electricity, but also near-zero carbon steam to displace carbon-intensive natural gas combustion for steam generation. The availability of electricity and steam also enables more efficient green hydrogen production by using high-temperature electrolysis. In this work, Argonne National Laboratory (ANL) models a synfuel production process via the Fischer- Tropsch (FT) reaction by using nuclear power to provide electricity and steam. In 2021, using ASPEN Plus software, ANL established a detailed process model of a stand-alone FT production facility, assuming feedstocks of pure CO2 and H2. This stand-alone model can be expanded to integrate H2 production from nuclear power via low-temperature and high-temperature electrolysis at light-water reactor (LWR) scale. This report summarizes the stand-alone ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 351 MT/day (130,000 gal/day) of FT fuel (a mixture of naphtha, jet fuel, and diesel) by converting 223 MT/day of H2 and 2,387 MT/day of CO2. The FT fuel production energy efficiency is 58% and the carbon conversion efficiency (from CO2 to FT fuel) is 46%. The production of green hydrogen requires 390–470 MWe of electricity, which is compared with the capacity of an LWR plant. For the stand-alone FT process, the detailed energy demand (electricity and heat) is summarized in the table below. Based on the energy supply source and the required temperature, potential insertion points of nuclear energy are identified. Based on the potential nuclear energy utilization, this report discusses potential modification options for expanding the system boundary to integrate nuclear power use, for example on-site hydrogen production via water electrolysis. Modeling of the integrated system is conducted by closely working with ANL and Idaho National Laboratory (INL) collaborators to harmonize design parameters of nuclear plants and the FT production process.

Zang, Guiyan↗
DOE OSTI2021

Commissioning the H i Observing Mode of the Beam Former for the Cryogenically Cooled Focal L-band Array for the GBT (FLAG)

We present the results of commissioning observations for a new digital beam-forming back end for the Focal plane L-band Array for the Robert C. Byrd Green Bank Telescope (FLAG), a cryogenically cooled Phased Array Feed (PAF) with the lowest measured T {sub sys}/η of any PAF outfitted on a radio telescope to date. We describe the custom software used to apply beam-forming weights to the raw element covariances to create research-quality spectral-line images for the new fine-channel mode, study the stability of the beam weights over time, characterize FLAG’s sensitivity over a frequency range of 150 MHz, and compare the measured noise properties and observed distribution of neutral hydrogen emission from several extragalactic and Galactic sources with data obtained with the current single-pixel L-band receiver. These commissioning runs establish FLAG as the preeminent PAF receiver currently available for spectral-line observations on the world’s major radio telescopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗