Search NASA⌕ Search

SEARCH · Search NASA

Results for “active learning”

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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Smackdown: Adventures in Simulation Standards and Interoperability

The paucity of existing employer-driven simulation education and the need for workers broadly trained in Modeling & Simulation (M&S) poses a critical need that the simulation community as a whole must address. This paper will describe how this need became an impetus for a new inter-university activity that allows students to learn about simulation by doing it. The event, called Smackdown, was demonstrated for the first time in April at the Spring Simulation Multi-conference. Smackdown is an adventure in international cooperation. Students and faculty took part from the US and Europe supported by IEEE/SISO standards, industry software and National Aeronautics and Space Administration (NASA) content of are supply mission to the Moon. The developers see Smackdown providing all participants with a memorable, interactive, problem-solving experience, which can contribute, importantly to the workforce of the future. This is part of the larger need to increase undergraduate education in simulation and could be a prime candidate for senior design projects.

Elfrey, Priscilla R.↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Rutishauser, David↗
NASA NTRS2013

Background Pressure Profiles for Sonic Boom Vehicle Testing in the NASA Glenn 8- by 6-Foot Supersonic Wind Tunnel

In an effort to identify test facilities that offer sonic boom measurement capabilities, an exploratory test program was initiated using wind tunnels at NASA research centers. The subject of this report is the sonic boom pressure rail data collected in the Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel. The purpose is to summarize the lessons learned based on the test activity, specifically relating to collecting sonic boom data which has a large amount of spatial pressure variation. The wind tunnel background pressure profiles are presented as well as data which demonstrated how both wind tunnel Mach number and model support-strut position affected the wind tunnel background pressure profile. Techniques were developed to mitigate these effects and are presented.

Castner, Raymond↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully Integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Epp, Chirold D.↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Co-Production and Collaborative STEM Engagement

The NASA DEVELOP Program conducts 50-60 projects each year with the goal of bringing the benefits of NASA Earth science to local decision-making challenges. DEVELOP, part of NASA’s Applied Sciences’ Capacity Building Program, connects end users with students, recent graduates, and early and transitioning career professionals through 10-week rapid feasibility studies. These projects are a collaboration between NASA, DEVELOP office host locations (universities and other federal installations), project partners (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations), and the project teams who conduct them. The projects take place under the guidance of science advisors from NASA, academia, and partner organizations, and introduces communities to new applications of NASA Earth observations data with the desired outcome of informed decision-making. This presentation will highlight the DEVELOP model of co-production and programmatic collaboration, lessons learned in partnering, and evaluation activities that look at the impact of the program’s efforts.

Capacity Building↗
NASA NTRSDate not supplied

Best Practices from NASA's Open Science Response to the Satellite Needs Working Group (SNWG) Process

The Satellite Needs Working Group (SNWG) in the U.S. Group on Earth Observations (USGEO) provides dedicated analysis and advice to the Office of Science and Technology Policy (OSTP), and is charged with identifying satellite data needs across the U.S. Government agencies to which the National Aeronautics and Space Administration (NASA) responds with solutions aligning with its missions and goals. The SNWG puts out a biennial survey to the U.S. Government agencies asking a variety of questions aimed at gleaning their current needs for satellite data. NASA’s response to the SNWG since 2016 has been to serve the community at large with open science and open data products derived through the SNWG process. With the next SNWG cycle set to kick off in 2022, the NASA SNWG team has been actively working to incorporate lessons learned from the past three cycles into the NASA-side process and tools. We will discuss the best practices and tools NASA has developed in response to the SNWG survey assessment process. These assist NASA’s decisions on how best to utilize existing, and proposing new, products and services to address the needs of other U.S. agencies.

Cerese Albers↗
DOE OSTI2024

Solar Pathways in Federal Energy Assistance Programs: Expanding Low Income Home Energy Assistance Program (LIHEAP) and Weatherization Assistance Program (WAP)

How can solar best fit within your LIHEAP or WAP activities? Come join NREL and learn about the various pathways and new resources available to help implement solar in low-income programs. Panelists will share results from a multi-year research project, including survey results on LIHEAP and WAP solar adoption across the United States. This session will highlight case studies from early implementers, key lessons learned and resources developed based on stakeholder feedback for interested organizations. Attendees can expect gain a better understanding of the perceived barriers and opportunities to solar implementation, including the importance of partner coordination and complementary funding sources, and the next steps for how to get started. Additionally, attendees will hear from a local implementer of solar in WAP about their program and process.

Colorado↗
NASA NTRS2021

Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks

Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.

Ashwin Ravichandran↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
NASA NTRS2000

The University of Nebraska at Omaha Center for Space Data Use in Teaching and Learning

Within the context of innovative coursework and other educational activities, we are proposing the establishment of a University of Nebraska at Omaha (UNO) Center for the Use of Space Data in Teaching and Learning. This Center will provide an exciting and motivating process for educators at all levels to become involved in professional development and training which engages real life applications of mathematics, science, and technology. The Center will facilitate innovative courses (including online and distance education formats), systematic degree programs, classroom research initiatives, new instructional methods and tools, engaging curriculum materials, and various symposiums. It will involve the active participation of several Departments and Colleges on the UNO campus and be well integrated into the campus environment. It will have a direct impact on pre-service and in-service educators, the K12 (kindergarten through 12th grade) students that they teach, and other college students of various science, mathematics, and technology related disciplines, in which they share coursework. It is our belief that there are many exciting opportunities represented by space data and imagery, as a context for engaging mathematics, science, and technology education. The UNO Center for Space Data Use in Teaching and Learning being proposed in this document will encompass a comprehensive training and dissemination strategy that targets the improvement of K-12 education, through changes in the undergraduate and graduate preparation of teachers in science, mathematics and technology education.

Grandgenett, Neal↗
NASA NTRS2025

Provider of Services for Urban Air Mobility (PSU) Prototype Simulation (X5) Final Report

Urban Air Mobility (UAM) is a new air transportation service concept to carry passengers or cargo in metropolitan areas, leveraged by innovative aircraft and air traffic automation technologies. NASA has conducted a series of simulations, called the X-series simulation, to evaluate the UAM concept of operations and support the development of airspace procedures and services for UAM operations. The simulation called “X5” was conducted in 2023 to test a Provider of Services for UAM (PSU) prototype developed by NASA for UAM flight planning, strategic conflict management support, and data exchange between UAM operators. In this simulation, two strategic conflict management capabilities, Demand-Capacity Balancing and Sequencing and Scheduling, were further investigated. This document describes the UAM system architecture modeled, the X5 simulation environment to be executed (e.g., traffic scenario and UAM airspace construct), and the strategic conflict management processes developed and evaluated in this study. Then, the simulation results are provided using several system performance metrics, such as the number of operations planned and activated, demand-capacity imbalances detected and resolved, and pre-departure delays. Based on these metrics, the test findings and lessons learned from this simulation are discussed. NASA developed a PSU prototype as part of a reference implementation of UAM system architecture and evolved strategic conflict management capabilities for UAM operations from the previous collaborative simulations with industry partners. Below is the summary of the achievements: - Aligned NASA’s UAM reference architecture with the FAA’s UAM ConOps notional architecture - Extended UAM airspace management capabilities to include 1) Demand-Capacity Balancing (DCB) to ensure operators coordinate planned usage of shared vertiports, and 2) Sequencing and Scheduling (S&S) at UAM corridor entry and exit points to help facilitate an orderly flow of traffic - Defined the PSU information exchange APIs and requirements towards informing industry standards - Developed and tested a NASA PSU prototype as reference implementation to validate the requirements and APIs - Developed a prototype service connecting NASA’s PSU and the FAA system for testing future PSU-ATM interface requirements - Tested NASA-developed assumptions for UAM operations such as airspace design, procedures, vehicle performance, and strategic conflict management methods to inform future Cooperative Operating Practices (COPs) development with industry - Evaluated system performance metrics such as number of simultaneous operations and ground delays that can help define system-level requirements. The simulation results showed that the UAM traffic demand could be managed to minimize the needs of tactical separation provision with ground delays assigned by DCB and S&S. These accomplishments and the lessons learned from the PSU Prototype X5 simulation activities will be valuable inputs for the Air Mobility Pathfinders (AMP) project, which is NASA’s new project to create and evaluate a reference architecture for safe, secure, and scalable UAM operations.

Simulation↗
NASA NTRS2007

Integration and Testing Challenges of Small, Multiple Satellite Missions: Experiences From The Space Technology 5 Project

This brief presentation describes the mechanical and electrical integration activities and environmental testing challenges of the Space Technology 5 (ST5) Project. Lessons learned during this process are highlighted, including performing mechanical activities serially to gain efficiency through repetition and performing electrical activities based on the level of subsystem expertise available.

Sauerwein, Timothy A.↗
DOE OSTI2025

Uncovering the True Active Sites in Ni–N–C Catalysts for CO 2 Electroreduction

Understanding and designing active sites in single-atom catalysts (SACs) requires going beyond static models to capture their dynamic evolution under realistic electrochemical conditions. Here, in this work, we develop an integrated theoretical framework that accounts for operational conditions, by combining grand canonical density functional theory (GC-DFT) with machine-learning-accelerated sampling, to uncover structure–activity–stability relationships in Ni–N–C SACs for the CO 2 reduction reaction (CO 2 RR). A library of NiN x C 4–x (x = 0–4) motifs─representing coordination defects likely formed during high-temperature synthesis─was systematically evaluated. Under working conditions, these sites were found to undergo hydrogenation, and NiN 3 C 1_ H 1 was identified as the most probable active site. At reducing potentials, hydrogen adsorbs spontaneously at C–Ni bridge sites rather than Ni top sites, while subsurface hydrogen facilitates bent CO 2 adsorption crucial for activation. High CO 2 RR selectivity toward CO arises from site separation: Ni centers drive CO2RR, while the hydrogen evolution reaction (HER) occurs at the C–Ni bridge or N sites and from thermodynamic suppression of HER at moderate hydrogen coverage. At more negative potentials, a shift in the CO 2 RR rate-determining process (RDP) and Ni out-of-surface displacement induced by coadsorption of H and H 2 O jointly reduce activity and selectivity. Thus, both the high CO2RR selectivity of Ni–N–C catalysts and its reversal with more negative potentials can be rationalized by accounting for hydrogenated surfaces. This highlights the necessity of modeling realistic; in situ conditions. This framework provides generalizable insights into the dynamic behavior of active sites in SACs, offering guidance for the rational design of active and robust catalysts for a wide range of electrochemical reactions.

25 ENERGY STORAGE↗
NASA NTRS2014

ARES Education and Public Outreach

The ARES Directorate education team is charged with translating the work of ARES scientists into content that can be used in formal and informal K-12 education settings and assisting with public outreach. This is accomplished through local efforts and national partnerships. Local efforts include partnerships with universities, school districts, museums, and the Lunar and Planetary Institute (LPI) to share the content and excitement of space science research. Sharing astromaterials and exploration science with the public is an essential part of the Directorate's work. As a small enclave of physical scientists at a NASA Center that otherwise emphasizes human space operations and engineering, the ARES staff is frequently called upon by the JSC Public Affairs and Education offices to provide presentations and interviews. Scientists and staff actively volunteer with the JSC Speaker's Bureau, Digital Learning Network, and National Engineers Week programs as well as at Space Center Houston activities and events. The education team also participates in many JSC educator and student workshops, including the Pre-Service Teacher Institute and the Texas Aerospace Scholars program, with workshop presentations, speakers, and printed materials.

Allen, Jaclyn↗
NASA NTRS2013

Evaluating the High School Lunar Research Projects Program

The Center for Lunar Science and Exploration (CLSE), a collaboration between the Lunar and Planetary Institute and NASA s Johnson Space Center, is one of seven member teams of the NASA Lunar Science Institute (NLSI). In addition to research and exploration activities, the CLSE team is deeply invested in education and outreach. In support of NASA s and NLSI s objective to train the next generation of scientists, CLSE s High School Lunar Research Projects program is a conduit through which high school students can actively participate in lunar science and learn about pathways into scientific careers. The objectives of the program are to enhance 1) student views of the nature of science; 2) student attitudes toward science and science careers; and 3) student knowledge of lunar science. In its first three years, approximately 168 students and 28 teachers from across the United States have participated in the program. Before beginning their research, students undertake Moon 101, a guided-inquiry activity designed to familiarize them with lunar science and exploration. Following Moon 101, and guided by a lunar scientist mentor, teams choose a research topic, ask their own research question, and design their own research approach to direct their investigation. At the conclusion of their research, teams present their results to a panel of lunar scientists. This panel selects four posters to be presented at the annual Lunar Science Forum held at NASA Ames. The top scoring team travels to the forum to present their research in person.

Shaner, A. J.↗
NASA NTRS1991

Capturing flight system test engineering expertise: Lessons learned

Within a few years, JPL will be challenged by the most active mission set in history. Concurrently, flight systems are increasingly more complex. Presently, the knowledge to conduct integration and test of spacecraft and large instruments is held by a few key people, each with many years of experience. JPL is in danger of losing a significant amount of this critical expertise, through retirement, during a period when demand for this expertise is rapidly increasing. The most critical issue at hand is to collect and retain this expertise and develop tools that would ensure the ability to successfully perform the integration and test of future spacecraft and large instruments. The proposed solution was to capture and codity a subset of existing knowledge, and to utilize this captured expertise in knowledge-based systems. First year results and activities planned for the second year of this on-going effort are described. Topics discussed include lessons learned in knowledge acquisition and elicitation techniques, life-cycle paradigms, and rapid prototyping of a knowledge-based advisor (Spacecraft Test Assistant) and a hypermedia browser (Test Engineering Browser). The prototype Spacecraft Test Assistant supports a subset of integration and test activities for flight systems. Browser is a hypermedia tool that allows users easy perusal of spacecraft test topics. A knowledge acquisition tool called ConceptFinder which was developed to search through large volumes of data for related concepts is also described and is modified to semi-automate the process of creating hypertext links.

Woerner, Irene Wong↗
NASA NTRSDate not supplied

Infusion of AI/ML Technology into Operational NASA Data Systems

NASA has been developing a variety of Artificial Intelligence / Machine Learning technologies related to Earth Observations. In most cases, the full value of such a technology is realized when it is infused into an operational system. NASA’s Earth Science Data Systems program has been formulating repeatable methods to execute technology infusion. These efforts include the Advancing Collaborative Connections for Earth System Science (ACCESS) program, a Technology Infusion Playbook, and an assemblage of working groups investigating methods for infusion collaboration, community development, and capacity building. ESDS has also been executing a pathfinder activity to infuse a machine-learning-driven recommender of science keywords for Earth Observation datasets, which is intended to be used for metadata curation in the Earth Observation System Data and Information System.

C Lynnes↗