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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

MoonRIDERS: NASA and Hawaiis Innovative Lunar Surface Flight Experiment for Landing in Late 2017

Recently, NASA Kennedy Space Center, Hawaii's state aerospace agency PISCES, and two Hawaii high schools Iolani and Kealakehe have come together in a unique collaboration called MoonRIDERS. This strategic partnership will allow Hawaii students to participate directly in sending a science experiment to the surface of the moon. The MoonRIDERS project started in the spring of 2014, with each institution responsible for its own project costs and activities. PISCES, given its legislative direction in advancing planetary surface systems, saw this collaboration as an important opportunity to inspire a young generation and encourage STEM (Science, Technology, Engineering, and Mathematics) learning. Under the guidance of PISCES and NASA, the students will be involved hands-on from start to finish in the engineering, testing, and validation of a space technology called the Electrodynamic Dust Shield (EDS). Dust is a critical issue for space exploration, as evidenced by the Apollo lunar missions and Mars rovers and landers. Dust creates a number of problems for humans and hardware, including inhalation, mechanical interference, wear and tear on spacesuits, inhibition of heat transfer on radiators, and reduced efficiency of solar panels. To address this, the EDS is designed to work on a variety of materials, and functions by generatingelectrodynamic fields to clear away the dust. The Google Lunar XPRIZE (GLXP), a space competition "designed to inspire pioneers to do robotic space transport on a budget," serves as a likely method for the MoonRIDERS to get their project to the moon. The EDS would potentially be flown as a hosted payload on a competitor's lander (still to be chosen). This briefing will provide an overview of the technology, the unique partnership, progress update and testing leading to this flight opportunity.

Payload experiment↗
NASA NTRS2020

Required Filtering to Optimize Use of TOS

This presentation was originally given to ATD-2 industry partners at the Surface CDM (Collaborative Decision Making) Team (SCT) & Flow Evaluation Team (FET) meeting at NTX on 2/20/2020. The briefing presented lessons learned from Stormy 19, an overview of how terminal restrictions are handled by the ATD-2 system, and additional Trajectory Option Sets (TOS) route filtering capabilities planned for Stormy 20.

ATD-2↗
DOE OSTI2026

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images

Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method across multiple deep learning–based WSI analysis tasks spanning different tissue types. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Lou, Peiliang↗
NASA NTRS1996

Learning from Inconsistency

This position paper argues that inconsistencies that occur during the development of a software specification offer an excellent way of learning more about the development process. We base this argument on our work on inconsistency management. Much attention has been devoted recently to the need to allow inconsistencies to occur during software development, to facilitate flexible development strategies, especially for collaborative work. Recent work has concentrated on reasoning in the presence of inconsistency, tracing inconsistencies with 'pollution markers' and supporting resolution. We argue here that one of the most important aspects of inconsistency is the learning opportunity it provides. We are therefore concerned with how to capture this learning outcome so that its significance is not lost. We present a small example of how apprentice software engineers learn from their mistakes, and outline how an inconsistency management tool could support this learning. We then argue that the approach can be used more generally as part of continuous process improvement.

Easterbrook, Steve↗
DOE OSTI2022

A High-Throughput Computing Infrastructure to Generate Custom, Open Community Geothermal Datasets

The most significant challenge facing geothermal research, development, and deployment is a lack of comprehensive datasets describing the geological and economical properties of North America. Automated knowledge base construction, the process of designing algorithms to analyze text and images to programmatically build new datasets, is one possible solution to this problem. The xDD library of full-text scientific articles (https://xdd.wisc.edu) is one of the largest collections of open and controlled-access scientific documents available for knowledge base construction in the world, but it has been underutilized by experts in geothermal research. The xDD development team attributed the lack of engagement by software developers and geothermal researchers to two perceived shortcomings of the system. First, the workflow for obtaining data from xDD for local development and testing of data mining applications was unnecessarily abstruse and required significant manual intervention by xDD systems administrators. Second, although xDD already held articles from a broad cross-section of scientific literature with an emphasis on the geosciences, it did not have an explicit set of geothermal research documents that could serve as the nucleus of a geothermal data mining application. To address these issues, the Automated Data Extraction PlaTform (ADEPT) was proposed to extend the data distribution capabilities of the xDD system. The ADEPT extension added the following four key features to xDD: 1) integration of National Geothermal Data System (NGDS) documents into the xDD library to provide an explicitly geothermally-themed collection; 2) improved RESTful (i.e., https-protocol driven) web services for external partners to access xDD data for machine learning application development; 3) a web platform for end-users and xDD administrators to coordinate the development of data mining applications from the initial step of browsing available documents to the final stage of deploying a production-quality machine learning application on high-throughput computing infrastructure; and 4) the development of demonstration data mining applications to illustrate the new workflow to potential collaborators. A total of 21,674 geothermal documents from NGDS were fully ingested into the xDD library and the associated metadata is publicly available through the xDD web services; furthermore, the ADEPT web platform is now publicly accessible and fully live at https://xdd.wisc.edu/adept/.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Lessons Learned for Rapid Decarbonization Of Power Sectors: Key Messages for Energy Ministers

This report covers key lessons learned for the rapid decarbonization of power systems, emphasizing best practices in planning, building, and operating power systems. This report is the result of a collaborative effort among various Clean Energy Ministerial (CEM) workstreams and partner initiatives: 21st Century Power Partnership (21CPP); Carbon Capture, Utilization and Storage (CCUS); Global Power System Transformation Consortium (G-PST), a partner to CEM; Industrial Deep Decarbonization Initiative (IDDI); International Energy Agency Digital Demand-Driven Electricity Networks Initiative (IEA 3DEN), a partner to CEM; International Smart Grid Action Network (ISGAN); Long Term Scenarios for the Energy Transition (LTES); Nuclear Innovation: Clean Energy Future (NICE Future); Super-Efficient Equipment and Appliance Deployment (SEAD). The contents are not intended to be comprehensive of all power sector topics, and there may be overlap between content in each section due to the nature of this first-of-its-kind collaborative effort to deliver unified messaging on power sector decarbonization to energy ministers. This work is intended to complement other work at the Clean Energy Ministerial and offers options for consideration, not specific policy recommendations.

21CPP↗
NASA NTRS1995

Summer Institute in Engineering and Computer Applications: Learning Through Experience

The document describing the Summer Institute project is made up of the following information: Administrative procedures; Seminars/Special Courses/Tours/College fair; Facilities/ Transportation; Staff and Administration; Collaboration; Participant/Project monitoring and evaluation; Fiscal and developmental activities; Job readiness/Job internship development and placement; and Student Follow-up/Tracking. Appendices include presentations, self-evaluations; abstracts and papers developed by the students during their participation in the program.

Langdon, Joan S.↗
NASA NTRS2005

Learning from Failures: Archiving and Designing with Failure and Risk

Identifying and mitigating risks during conceptual design remains an ongoing challenge. This work presents the results of collaborative efforts between The University of Missouri-Rolla and NASA Ames Research Center to examine how an early stage mission design team at NASA addresses risk, and, how a computational support tool can assist these designers in their tasks. Results of our observations are given in addition to a brief example of our implementation of a repository based computational tool that allows users to browse and search through archived failure and risk data as related to either physical artifacts or functionality.

VanWie, Michael↗
DOE OSTI2023

JSPEC: A Program for IBS and Electron Cooling Simulation

JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab to simulate the evolution of the ion beam under the intrabeam scattering effect and/or the electron cooling effect. JSPEC includes various models of the ion beam, the electron beam, and the friction force, aiming to reflect the latest advances in the field and to provide a useful tool to the community. JSPEC has been benchmarked against other cooling simulation codes and experimental data. It has been used to support the cooler design for JLEIC, an earlier JLab design for the Electron-Ion Collider. A Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has also been developed and released. It allows users to run JSPEC simulations in a Python environment and makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs, as well as plentiful Python tools in data visualization, optimization, machine learning, etc. In this report, we introduce the features of JSPEC, with a focus on the latest development, and demonstrate how to use JSPEC and pyJSPEC with sample codes and numerical examples.

Accelerator Physics↗
DOE OSTI2022

pyJSPEC - A Python Module for IBS and Electron Cooling Simulation

The intrabeam scattering is an important collective effect that can deteriorate the property of a high-intensity beam and electron cooling is a method to mitigate the IBS effect. JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab, which simulates the evolution of the ion beam under the IBS and/or the electron cooling effect. The Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has been recently developed and released. It allows the users to run JSPEC simulations in a Python environment. It also makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs as well as plentiful python tools in data visualization, optimization, machine learning, etc. In this paper, we will introduce the features of pyJSPEC and demonstrate how to use it with sample codes and numerical results.

Zhang, H.↗
DOE OSTI2022

Washington State Cyber Incident Response Summit Event Report

On September 7 and 8, 2022, Pacific Northwest National Laboratory and Washington State Adjutant General Major General Bret Daugherty, with support from the Department of Homeland Security Cybersecurity and Infrastructure Security Agency, hosted the Washington State Cyber Incident Response Summit at Camp Murray in Washington State. The invite-only summit convened 40 key decision makers and stakeholders from across the state to discuss strategies and pilot a collaborative approach to improve cyber incident response readiness within Washington. In small working groups, participants shared incident response lessons learned, best practices, and opportunities for improvement within the water and transportation sectors. This report highlights the details of the workshop presentations and discussions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Increasing Reliability and Safety of Hydrogen Components - Reliability Data Collection

Come learn about the new Hydrogen Component Reliability Database (HyCReD) and participate in discussions on hydrogen component reliability data collection, collaboration, and analysis. Funded by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy under the Hydrogen and Fuel Cell Technologies Office, HyCReD is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety reliability for hydrogen facilities by integrating risk reduction methodologies and component reliability data taxonomies that support hydrogen infrastructure failure rate analysis.

component↗
NASA NTRS2017

Model-Based Systems Engineering Pathfinder: Informing the Next Steps

In 2016, the NASA Engineering and Safety Center established a model-based systems engineering (MBSE) Pathfinder. The primary motivations for establishing the MBSE Pathfinder were to advance the Agency's applications of MBSE and capture lessons-learned to inform the next steps. The MBSE Pathfinder had four teams working in parallel for eight months on different topics of interest to NASA. The teams were encouraged to learn, and use creativity and innovation in their system modeling. The results were captured via reports, webinars, and a knowledge capture meeting. The approach taken for the MBSE Pathfinder was very successful in providing a number of lessons-learned for NASA and for other organizations considering MBSE or pathfinder efforts, and in building a very strong and collaborative user community.

Weiland, Karen J.↗
NASA NTRSDate not supplied

Experiences in the Practice of Design of Experiments at NASA

Statistical design of experiments (DOE) has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits DOE techniques by accelerating learning, maximizing knowledge, ensuring strategic resource investment, and informing data-driven decisions. In practice, DOE relies on multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to meet challenging research objectives. This presentation shares experiences in the practice of design of experiments at NASA in aeronautics, space exploration, and atmospheric science.

Peter A. Parker↗
DOE OSTI2025

Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Eleven Countries, an Integrated Spacecraft: the Story of International Collaboration that Built the Orion Spacecraft and Powered the Success of the Artemis I Mission

The quest to return humans to the Moon in the next step towards humanity's exploration of space is more alive than ever. After a great deal of achievements, failures, and lessons learned, the Artemis I mission set o to the Moon on November 16, 2022, with the goal of testing a new rocket, the Space Launch System, and a new spacecraft, Orion: designed, assembled, and tested across two continents, and 11 countries. Behind this mission, decades of experience with the International Space Station, Autonomous Transfer Vehicle operations, and many other program collaborations built the know-how on how to succeed together in the toughest environment | deep space. The Artemis I mission proved to be an incredible success, meeting 161 total mission objectives, including 21 developed during the flight based on outperforming spacecraft. It was also a case-study in international collaboration, given that ESA, NASA, and industry partners Airbus and Lockheed Martin for the first time had to design, build, test, and fly a fully integrated human-rated spacecraft, with most critical functions dependent and interconnected across U.S. and European systems. The U.S.-built Orion Crew Module and Crew Module Adapter and European-built European Service Module (ESM) shared critical interfaces and commodities, from propulsion, avionics, active/passive thermal, electrical power generation, storage and distribution to the software that managed it all. In this paper, we will describe relevant aspects of the integrated spacecraft design, providing context for the challenges that the team faced in all phases required to get Orion ready to fly, and provide a direct account of how the joint team formed, trained, and supported the operations of the successful Artemis I mission. We will also explore the evolution of the partnerships, given that these allow a multi-national e ort to sustain the program production, share costs, leverage a broader base of engineering expertise, and build more diverse capabilities over the long haul to support the Artemis goals and objectives. Lastly, we will cover critical lessons learned and how the Orion Program has implemented these in preparation of the next Artemis missions to repeat the success of Artemis I. The purpose of this paper is to document knowledge we gained and lessons we learned through the development of an integrated Orion spacecraft, since it is imperative we build on this now, at the dawn of the Artemis Program, an international endeavor to push human space exploration.

Deep Space Exploration↗
NASA NTRS2021

Lessons Learned in Remote Participant Concurrent Engineering Concept Development Studies

Team-X was born from a need to perform rapid space mission design for principal investigator-led competed proposals in the mid-1990s. Throughout the last 25 years, Team-X at the Jet Propulsion Laboratory has expanded its application of the collaborative, concurrent study approach into every facet necessary to win competed proposals. The COVID-19 pandemic of 2020 created an immediate and new constraint on these studies – the need to conduct them with remote participants, both on the client side requesting the study, but also on the provider side producing the study. This paper provides lessons learned from dozens of design studies run by Team-X at the Jet Propulsion Laboratory during the COVID-19 pandemic. These lessons span all aspects of the information infrastructure: the people, processes, procedures, methods, tools, and “facilities”. These lessons learned will have applicability post-pandemic, as they have shown how best to incorporate participants, both on the provider side, and on the client side, who cannot travel or otherwise be co-located during collaborative, concurrent study sessions.

Nash, Alfred↗
DOE OSTI2023

Model selection and signal extraction using Gaussian Process regression

We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗