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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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All sources NASA NTRS DOE OSTI

NASA NTRS2023

The Community Radiative Transfer Model (CRTM): Community-Focused Collaborative Model Development Accelerating Research to Operations

The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1-D radiative transfer model used in numerical weather prediction, calibration/validation, etc. across multiple federal agencies and universities. The key benefit of the CRTM is that it is a satellite simulator. It provides a highly accurate representation of satellite radiances by using the specific sensor response functions convolved with a line-by-line radiative transfer model (LBLRTM). CRTM covers the spectral ranges consistent with all present operational and most research satellites, from visible to microwave. The capability to simulate ultraviolet radiances and support space-based radar sensors is being added over the next two years in CRTM Version 3.0. In addition to simulated radiances, the CRTM also provides Jacobian outputs needed to interpret satellite observations for numerical weather prediction. The Jacobian estimates how changes in geophysical parameters affect simulated measurements from satellite sensors. Using the Jacobian in modeling and weather prediction improves the accuracy and efficiency of data analysis, leading to better weather predictions. The CRTM model's success and growth depend on community contributions and evaluation. To facilitate this, we have made the CRTM highly accessible through modular programming, clear documentation and tutorials, public domain licensing, unfettered public access via Github, and a clear path to operational implementation for innovative research. We encourage and welcome contributions from the community to help us continue to improve the CRTM.

Benjamin T Johnson↗

NASA NTRS2003

High-Frequency Testing of Composite Fan Vanes With Erosion-Resistant Coating Conducted

The mechanical integrity of hard, erosion-resistant coatings were tested using the Structural Dynamics Laboratory at the NASA Glenn Research Center. Under the guidance of Structural Mechanics and Dynamics Branch personnel, fixturing and test procedures were developed at Glenn to simulate engine vibratory conditions on coated polymer-matrix- composite bypass vanes using a slip table in the Structural Dynamics Laboratory. Results from the high-frequency mechanical bench testing, along with concurrent erosion testing of coupons and vanes, provided sufficient confidence to engine-endurance test similarly coated vane segments. The knowledge gained from this program will be applied to the development of oxidation- and erosion-resistant coatings for polymer matrix composite blades and vanes in future advanced turbine engines. Fan bypass vanes from the AE3007 (Rolls Royce America, Indianapolis, IN) gas turbine engine were coated by Engelhard (Windsor, CT) with compliant bond coatings and hard ceramic coatings. The coatings were developed collaboratively by Glenn and Allison Advanced Development Corporation (AADC)/Rolls Royce America through research sponsored by the High-Temperature Engine Materials Technology Project (HITEMP) and the Higher Operating Temperature Propulsion Components (HOTPC) project. High-cycle fatigue was performed through high-frequency vibratory testing on a shaker table. Vane resonant frequency modes were surveyed from 50 to 3000 Hz at input loads from 1g to 55g on both uncoated production vanes and vanes with the erosion-resistant coating. Vanes were instrumented with both lightweight accelerometers and strain gauges to establish resonance, mode shape, and strain amplitudes. Two high-frequency dwell conditions were chosen to excite two strain levels: one approaching the vane's maximum allowable design strain and another near the expected maximum strain during engine operation. Six specimens were tested per dwell condition. Pretest and posttest inspections were performed optically at up to 60 magnification and using a fluorescent-dye penetrant. Accumulation of 10 million cycles at a strain amplitude of two to three times that expected in the engine (approximately 670 Hz and 20g) led to the development of multiple cracks in the coating that were only detectable using fluorescent-dye penetrant inspection. Cracks were prevalent on the trailing edge and on the convex side of the midsection. No cracking or spalling was evident using standard optical inspection at up to 60 magnification. Further inspection may reveal whether these fine cracks penetrated the coating or were strictly on the surface. The dwell condition that simulated actual engine conditions produced no obvious surface flaws even after up to 80 million cycles had been accumulated at strain amplitudes produced at approximately 1500 Hz and 45g.

Bowman, Cheryl L.↗

NASA NTRS2007

The Distributed Space Exploration Simulation (DSES)

The paper describes the Distributed Space Exploration Simulation (DSES) Project, a research and development collaboration between NASA centers which focuses on the investigation and development of technologies, processes and integrated simulations related to the collaborative distributed simulation of complex space systems in support of NASA's Exploration Initiative. This paper describes the three major components of DSES: network infrastructure, software infrastructure and simulation development. In the network work area, DSES is developing a Distributed Simulation Network that will provide agency wide support for distributed simulation between all NASA centers. In the software work area, DSES is developing a collection of software models, tool and procedures that ease the burden of developing distributed simulations and provides a consistent interoperability infrastructure for agency wide participation in integrated simulation. Finally, for simulation development, DSES is developing an integrated end-to-end simulation capability to support NASA development of new exploration spacecraft and missions. This paper will present current status and plans for each of these work areas with specific examples of simulations that support NASA's exploration initiatives.

Crues, Edwin Z.↗

DOE OSTI2023

Enabling Dark Energy Measurements from DESI and LSST (Final Technical Report)

This grant enabled efforts to lead the Survey Validation of the Luminous Red Galaxy (LRG) sample for DESI, entailing intensive work to prepare target samples, test their performance with DESI data, and validate that the requirements of the survey for this key sample are met. Luminous Red Galaxies represent the gold standard target class for Baryon Acoustic Observation experiments to study Dark Energy. It also funded work to co-leading the Follow-up Task Force within the LSST Dark Energy Science Collaboration (LSST DESC), which is intended to help develop collaborations with external groups and to produce cross-working group proposals for telescope time, policy proposals and white papers as needed in order to help the collaboration obtain and make use of complementary data which will strengthen LSST Dark Energy constraints. The PI has been particularly engaged in efforts to obtain access to spectroscopic training sets for LSST photometric redshifts in the first years of the survey, which requires developing relationships with groups that are obtaining such data for other purposes. This group has evolved into an External Synergies working group within DESC, also co-led by the PI. These groups have developed white papers for Astro2020, letters of intent and white papers for Snowmass2021, and a response to a DOE-NASA RFI. It also has enabled smaller contributions to improving LSST DESC pipeline infrastructure for photometric redshifts.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

OpenCHAMI Developer Summit [Slides]

The mission of the OpenCHAMI consortium is to steward the collaborative development and continuous evolution of cloud-like software to manage High Performance Computing capacity regardless of the size or deployment platform. We are guided by the operators and practitioners who use modern tooling and concepts to address the needs of classical HPC applications and the growing AI/ML and Data Science community that wish to leverage HPC capacity within their own workflows, to meet their needs with their own tools.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2021

Integrated Demand Management Techport Closeout

Over the period 2016 to 2020, Integrated Demand Management (IDM) concept, procedures, and tools have been developed - first for clear-weather and a single airport constraint and then expanded to handle a multi-constraint problem during convective weather at Newark (EWR), LaGuardia (LGA) and Philadelphia (PHL) airports. The concept was evaluated in a series of human-in-the-loop simulations to confirm concept benefits in terms of better schedule predictability, reduction of delays, and increased throughput, especially during convective weather. In addition, other conditions with mixed participation of multi-trajectories from the airlines were evaluated that showed significant benefits to the individual airlines as well as the overall traffic flow. The IDM concept was initiated in the SMART-NAS project, was completed under the ATM-X project, and was developed collaboratively with the FAA and airline partners. Over the course of IDM development, NASA researchers produced 19 conference papers and publications. Outside supporting organizations, funded by IDM, produced 20 additional conference papers and publications, in which they advanced fundamental research on topics such as better stochastic traffic demand prediction, and application of machine learning techniques for modeling traffic management initiatives. Final concept procedures and tool specifications have been transferred to the FAA Air Traffic Organization Operational Concepts, Validation, and Requirements group.

William N Chan↗

DOE OSTI2022

Facilitating Machine Learning Collaborations Between Labs, Universities, And Industry

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

Edelen, J.P.↗

NASA NTRS2011

Making Human Spaceflight Practical and Affordable: Spacecraft Designs and their Degree of Operability

As we push toward new and diverse space transportation capabilities, reduction in operations cost becomes increasingly important. Achieving affordable and safe human spaceflight capabilities will be the mark of success for new programs and new providers. The ability to perceive the operational implications of design decisions is crucial in developing safe yet cost competitive space transportation systems. Any human spaceflight program - government or commercial - must make countless decisions either to implement spacecraft system capabilities or adopt operational constraints or workarounds to account for the lack of such spacecraft capabilities. These decisions can benefit from the collective experience that NASA has accumulated in building and operating crewed spacecraft over the last five decades. This paper reviews NASA s history in developing and operating human rated spacecraft, reviewing the key aspects of spacecraft design and their resultant impacts on operations phase complexity and cost. Specific examples from current and past programs - including the Space Shuttle and International Space Station - are provided to illustrate design traits that either increase or increase cost and complexity associated with spacecraft operations. These examples address factors such as overall design performance margins, levels of redundancy, degree of automated failure response, type and quantity of command and telemetry interfaces, and the definition of reference scenarios for analysis and test. Each example - from early program requirements, design implementation and resulting real-time operations experience - to tell the end-to-end "story" Based on these experiences, specific techniques are recommended to enable earlier and more effective assessment of operations concerns during the design process. A formal method for the assessment of spacecraft operability is defined and results of such operability assessments for recent spacecraft designs are provided. Recent experience in applying these techniques to Orion spacecraft development is reviewed to highlight the direct benefits of early operational assessment and collaborative development efforts.

Crocker, Alan R.↗

NASA NTRS2018

Collaborative Data Curation to Support the Multi-Mission Algorithm and Analysis Platform (MAAP)

Upcoming space-borne missions will offer unprecedented data about Earth but will also feature exponentially high data volumes. These high data volumes will change the way the scientific community works with data and will also create a unique need for improved data sharing and collaboration. NASA and ESA are working together to address these issues by collaboratively developing the Multi-Mission Algorithm and Analysis Platform (MAAP) to improve the understanding of global aboveground terrestrial carbon dynamics. The MAAP will support ESA’s BIOMASS mission, NASA’s GEDI mission and NASA/ISRO’s NISAR mission. The MAAP will be developed in two phases: a pilot phase and a full production phase. The pilot phase will demonstrate collaboration and basic capabilities. The pilot phase will focus on biomass relevant airborne and field campaign data. Two NASA teams are supporting the development of the MAAP. The MAAP engineering team is responsible for the development, maintenance and operations of the MAAP system while the MAAP data team ensures the ongoing quality of the data, metadata and other information provided in the MAAP. The MAAP data team also supports the ingest and archive of identified data to the MAAP platform. This poster describes the use case development process for the pilot MAAP and the data curated in support of those use cases. Additionally, this presentation will outline the pilot MAAP data ingest process and metadata curation effort along with efforts to ensure interoperability between ESA and NASA data and metadata.

Bugbee, Kaylin↗

NASA NTRSDate not supplied

Towards a flexible framework for community-wide ensemble forecasting tailored for major space environment impacts

To build continuously improving space weather predictive capabilities based on science and enable assessments and rapid implementations of advances in research into source-to-impact modelling systems we need: to assemble parts of the puzzle by solving problems focused on specific physical domains; to identify essential space environment quantities (ESEQs) passed between domains and linked to impacts; evaluate modeling capabilities for each ESEQ; connect all validated solutions from space weather origins on the sun to impacts on humans and critical infrastructure; to design displays for ensemble predictions tailored for major space weather user groups; to build a collaborative environment for efficient sharing of information and capabilities (models/data/expertise) and collaborative development. The presentation will overview existing community-wide space weather forecasting frameworks and research-to-operations pipelines and discuss opportunities to build a collaborative plug-and-play platform for interconnecting predictive capabilities developed under different space weather programs.

space weather↗

NASA NTRSDate not supplied

Towards A Flexible Framework for Community-Wide Ensemble Forecasting Tailored for Major Space Environment Impacts

space weather↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

DOE OSTI2025

Thoroughly testing and integrating hundreds of Pull Requests per month: ROOT’s new Cost-efficient and Feature Rich GitHub-based CI

ROOT is an open source framework, freely available on GitHub, at the heart of data acquisition, processing and analysis of HE(N)P experiments, and beyond. It is developed collaboratively: contributions are not authored only by ROOT team members, but also by the user community at large: developers and scientists from universities, labs as well as the private sector. More than 1500 GitHub Pull Requests are merged on average per year. It is in this context that code integration acquires a primary role. The review of code contributions isn’t enough: not only they need to be thoroughly reviewed, they also need to be thoroughly tested through a powerful CI infrastructure on several different platforms to comply with the high code quality standards of the project. Since the end of 2023, ROOT moved its continuous integration system from Jenkins to GitHub Actions. In this contribution, we characterise the transition to the GitHub CI, focussing on our strategy, its implementation and the lessons learned, as well as the advantages the new system offers with respect to the previous one. Particular emphasis will be given to the evaluation of the cost-benefit ratio for Jenkins and GitHub Actions for the ROOT project. We also describe how we manage to run in less than one hour thousands of unit, integration, functional and end-to-end tests on different flavours of Windows, four versions of macOS, as well as about ten of the most used Linux distributions, taking advantage of the CERN computing infrastructure.

Piparo, Danilo [CERN]↗

DOE OSTI2025

FNAL-NIU collaboration on magnet development

We are presenting an overview of our (MTD) collaboration with NIU on superconducting magnet development. Through multiple endeavors over the last years we succeeded building a strong collaboration and attracted additional support for it. One of the key aspects of the collaboration is that it heavily benefits NIU students who are offered excellent opportunities to work with world leading experts in the field, make a mark for themselves and possibly start a carrier in an area they start to appreciate and like.

Stoynev, Stoyan [Fermilab]↗

DOE OSTI2024

WETO Software Stack Best Practices

Wind energy researchers typically share one key characteristic: a passion for increasing wind energy in the global energy mix. The U.S. Department of Energy (DOE) supports this mission in a number of ways including allocating funding directly to various aspects of wind energy research through the Office of Energy Efficiency and Renewable Energy (EERE) via the Wind Energy Technologies Office (WETO). While the traditional output of research is academic publication, software development efforts are increasingly a major focus. Software tools in the research environment allow researchers to describe an idea and quickly increase the scope and scale as they study it further. As a product of research, these tools represent a direct pipeline from researcher to industry practitioners since they are the implementation of ideas described in academic publications. Given this vital role in wind energy research and commercial development, the broad research software portfolio supported by WETO must maintain a minimum level of quality to support the wind energy field in the growing transition to renewable energy. This report outlines a series o f best practices to be adopted by all WETO-supported software projects, as well as expectations that the communities interacting with these projects should have of the developers and tools themselves. Wind energy research software has a unique standing in the field of scientific software. The stakeholders are varied with a subset being: (1) DOE EERE leadership, (2) DOE WETO leadership and program managers, (3) National lab leadership, (4) Associated project principle investigators, (5) Research software engineers, (6) Wind energy researchers in academia (including graduate students, post docs, and national lab staff), (7) Industry researchers and practitioners, (8) Commercial software developers, and (9) The general public interested in wind energy. These software are typically the end-user of other generic software libraries, so the funding cycles are often tied to applied research rather than the development of the software itself. Since the developers are also wind energy researchers, these tools are typically designed in a way that closely resembles the application in which they're used. Additionally, the expertise and incentives for the developers have a high variability, and often neither are aligned with software engineering or computer science. Given the unique environment in which wind energy research software is produced and consumed, it is critical for model owners to understand the context of their software. A framework for developing this understanding is to answer the following questions of a given software project: What is it's purpose? What is its role in the field of wind energy? What is the profile of the expected users? For how long will it be relevant? What is the expected impact? These questions allow model owners to identify the appropriate methods for the design, development, and long term maintenance of their software. Additionally, the answer provide context for future planners to understand why particular decisions were made and discern the consequences of changing course. The information is aggregated from experience within WETO-supported software development groups as well as external organizations and efforts to define the craft of research software engineering. These best practices aim to make the collaborative development process efficient and effective while improving the model understanding across stakeholders. Additionally, the general adoption of a common framework for software quality ensures that the end users of WETO software can trust these tools and accurately understand the risks to workflow integration.

17 WIND ENERGY↗

NASA NTRS2007

An Orion/Ares I Launch and Ascent Simulation: One Segment of the Distributed Space Exploration Simulation (DSES)

This paper describes the architecture and implementation of a distributed launch and ascent simulation of NASA's Orion spacecraft and Ares I launch vehicle. This simulation is one segment of the Distributed Space Exploration Simulation (DSES) Project. The DSES project is a research and development collaboration between NASA centers which investigates technologies and processes for distributed simulation of complex space systems in support of NASA's Exploration Initiative. DSES is developing an integrated end-to-end simulation capability to support NASA development and deployment of new exploration spacecraft and missions. This paper describes the first in a collection of simulation capabilities that DSES will support.

Chung, Victoria I.↗

NASA NTRS2020

Georgia Energy III Project Summary - Identifying Habitat and Solar Site Conflict in Georgia by Developing an Environmental Sensitivity Public Mapping Too

The rapid expansion of the solar industry across the state of Georgia has a detrimental effect on the habitats of keystone and threatened species, such as the gopher tortoise (Gopherus polyphemus) and the American black bear (Ursus americanus). NASA DEVELOP collaborated with the Georgia Chapter of The Nature Conservancy (TNC) and the Georgia Department of Natural Resources to continue the research from two previous NASA DEVELOP projects in 2017. The team worked to assess the conflict between solar suitability and environmentally sensitive areas with the Land-Use Conflict Identification Strategy (LUCIS) and to determine how conflict has changed since the 2017 analysis. The project utilized Terra/Aqua Clouds and the Earth's Radiant Energy System (CERES) satellite data and other ancillary datasets to conduct and compare a general statewide LUCIS analysis from 2017 to 2019 and to complete an in-depth LUCIS analysis of Georgia’s fastest-growing solar counties—Taylor, Twiggs, Decatur, and Brooks. The analysis indicated that between 2017 and 2019, the entire state saw high conflict areas increase by 38%. Project partners will use these findings to target areas for promotion of conservation policy and education efforts. The team also provided the TNC with a publicly available web application, called the Environmental Sensitivity Mapping Tool (ESMT), that can be updated as new data are released. The ESMT will be used to educate interest groups, such as solar developers and conservationists, to help them recognize and mitigate the negative effects of solar development on the environment.

DEVELOP Project Summary↗

NASA NTRS2012

Collaborative Business Models for Exploration: - The Expansion of Public-Private Partnerships to Enable Exploration and Improve the Quality of Life on Earth

In May of 2007, The Space Life Sciences Strategy was published, launching a series of efforts aimed at driving human health and performance innovations that both meet space flight needs and benefit life on Earth. These efforts, led by the Space Life Science Directorate (SLSD) at the NASA Johnson Space Center, led to the development and implementation of the NASA Human Health and Performance Center (NHHPC) in October 2010. The NHHPC now has over 100 members including seven NASA centers; other federal agencies; some of the International Space Station partners; industry; academia and non-profits. The NHHPC seeks to share best practices, develop collaborative projects and experiment with open collaboration techniques such as crowdsourcing. Using this approach, the NHHPC collaborative projects are anticipated to be at the earliest possible stage of development utilizing the many possible public-private partnerships in this center. Two workshops have been successfully conducted in 2011 (January and October) with a third workshop planned for the spring of 2012. The challenges of space flight are similar in many respects to providing health care and environmental monitoring in challenging settings on the earth. These challenges to technology development include the need for low power consumption, low weight, in-situ analysis, operator independence (i.e., minimal training), robustness, and limited resupply or maintenance. When similar technology challenges are identified (such as the need to provide and monitor a safe water supply or develop a portable medical diagnostic device for remote use), opportunities arise for public-private partnerships to engage in co-creation of novel approaches for space exploration and health and environmental applications on earth. This approach can enable the use of shared resources to reduce costs, engage other organizations and the public in participatory exploration (solving real-world problems), and provide technologies with multiple uses for space exploration and life on earth. Several examples will be provided that demonstrate the application of a technology to solve a space exploration need and to provide a positive impact to the quality of life on earth.

Davis, Jeffrey R.↗