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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Learning to Fly: The Wright Brothers' Adventure. A Guide for Educators and Students with Activities in Aeronautics

This guide was produced by the NASA Glenn Research Center Office of Educational Programs in Cleveland, OH, and the NASA Aerospace Educational Coordinating Committee. It includes activity modules for students, including the history of the Wright Brothers and their family in Dayton, Ohio and flight experimentation in Kitty Hawk, North Carolina. Student activities such as building models of the Wright Brothers glider and writing press releases of the initial flight are included.

WILBUR WRIGHT↗
NASA NTRS2001

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

cerebellum cerebellar learning dynamic state estim↗
NASA NTRS2009

A History of Space Toxicology Mishaps: Lessons Learned and Risk Management

After several decades of human spaceflight, the community of space-faring nations has accumulated a diverse and sometimes harrowing history of toxicological events that have plagued human space endeavors almost from the very beginning. Lessons have been learned in ground-based test beds and others were discovered the hard way - when human lives were at stake in space. From such lessons one can build a risk-management framework for toxicological events to minimize the probability of a harmful exposure, while recognizing that we cannot foresee all events. Space toxicologists have learned that relatively harmless compounds can be converted by air revitalization systems into compounds that cause serious harm to the crew. Our toxic risk management strategy now includes an assessment of the fate of any compound that might be released into the atmosphere. Propellants are highly toxic compounds, yet we have not always been able to thoroughly isolate the crew from exposure to these toxicants. Leakage of fluids from systems has resulted in hazardous conditions at times, and the behavior of such compounds inside a spacecraft has taught us how to manage potentially harmful escapes should they occur. Potential combustion events are an ever-present threat to the wellbeing of the crew. Such events have been sufficiently common that we have learned that one cannot judge the health threat of a given fire by the magnitude of the event. Management of such risks demands monitoring of combustion products. In the category of unpredictable toxic events, if one assumes that fires are predictable, we can place experience with toxic microbial metabolites, upsets during repair operations, and discharges from filters that have accumulated a substantial load of pollutants in their absorption beds. Management of such events requires a broad-spectrum, real-time analytical capability to discern the identity and concentrations of pollutants if they enter the atmosphere. Adverse events are an integral part of any human activity, and the spacefaring community must learn as much as possible from mistakes and near misses.

James, John T.↗
NASA NTRS2011

FASTSAT-HSV01 Thermal Math Model Correlation

This paper summarizes the thermal math model correlation effort for the Fast Affordable Science and Technology SATellite (FASTSAT-HSV01), which was designed, built and tested by NASA's Marshall Space Flight Center (MSFC) and multiple partners. The satellite launched in November 2010 on a Minotaur IV rocket from the Kodiak Launch Complex in Kodiak, Alaska. It carried three Earth science experiments and two technology demonstrations into a low Earth circular orbit with an inclination of 72deg and an altitude of 650 kilometers. The mission has been successful to date with science experiment activities still taking place daily. The thermal control system on this spacecraft was a passive design relying on thermo-optical properties and six heaters placed on specific components. Flight temperature data is being recorded every minute from the 48 Resistance Temperature Devices (RTDs) onboard the satellite structure and many of its avionics boxes. An effort has been made to correlate the thermal math model to the flight temperature data using Cullimore and Ring's Thermal Desktop and by obtaining Earth and Sun vector data from the Attitude Control System (ACS) team to create an "as-flown" orbit. Several model parameters were studied during this task to understand the spacecraft's sensitivity to these changes. Many "lessons learned" have been noted from this activity that will be directly applicable to future small satellite programs.

McKelvey, Callie↗
NASA NTRS2012

VIIRS On-Orbit Optical Anomaly - Investigation, Analysis, Root Cause Determination and Lessons Learned

A gradual, but persistent, decrease in the optical throughput was detected during the early commissioning phase for the Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imager Radiometer Suite (VIIRS) Near Infrared (NIR) bands. Its initial rate and unknown cause were coincidently coupled with a decrease in sensitivity in the same spectral wavelength of the Solar Diffuser Stability Monitor (SDSM) raising concerns about contamination or the possibility of a system-level satellite problem. An anomaly team was formed to investigate and provide recommendations before commissioning could resume. With few hard facts in hand, there was much speculation about possible causes and consequences of the degradation. Two different causes were determined as will be explained in this paper. This paper will describe the build and test history of VIIRS, why there were no indicators, even with hindsight, of an on-orbit problem, the appearance of the on-orbit anomaly, the initial work attempting to understand and determine the cause, the discovery of the root cause and what Test-As-You-Fly (TAYF) activities, can be done in the future to greatly reduce the likelihood of similar optical anomalies. These TAYF activities are captured in the lessons learned section of this paper.

Iona, Glenn↗
NASA NTRS2011

Reducing NPR 7120.5D to Practice: Transitioning from Design Reviews to the SIR Hardware Review

The Gravity Recovery And Interior Laboratory (GRAIL) mission was the first Jet Propulsion Laboratory (JPL) project initiated under NASA's revised rules for space flight project management, NPR 7120.5D, "NASA Space Flight Program and Project Management Requirements." NASA selected GRAIL through a competitive Announcement of Opportunity process and funded its Phase B Preliminary Design effort. The team's first major milestone was a JPL institutional milestone, the Project Mission System Review (PMSR), which proved an excellent tune-up for the end-of-Phase-B NASA life-cycle review, the Preliminary Design Review (PDR). Building on JPL experience on the Prometheus and Juno projects, the team successfully organized for and conducted these reviews on an aggressive schedule. For the Project Critical Design Review (CDR), lessons learned from the PDR and updated Standing Review Board (SRB) practices from the Agency were factored into the review preparation effort. Additionally, the review was held at the Principal Investigator's institution, the Massachusetts Institute of Technology, rather than at the project management center (JPL), which necessitated additional cross-country coordination steps. The PMSR, PDR, and CDR were design reviews and largely paper-oriented. For the System Integration Review (SIR), the project needed to transition to a hardware review and deal with paper in a very different manner. While many of the practices employed for the design reviews were modified and retained (e.g., review preparation team, gate products management, pre-reviews, SRB coordination), the review agenda, presentation style, and slide templates were significantly changed. A key success factor concerned the handling of project open paper, which was succinctly and effectively communicated to the SRB in presentations.This paper provides a brief overview of the GRAIL mission and its project management challenges, provides a detailed description of project SIR preparation and execution activities, including positive and negative lessons learned and identifies recommendations for future NASA (and non- NASA) project teams.

NPR 7120.5D↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Adaptative Site Management for a 115 Acre Chlorinated Solvent Plume with Two Separate Source Areas at Kennedy Space Center, Florida

Background/Objectives. During Resource Conservation and Recovery Act (RCRA) Facility Investigation (RFI) activities, Geosyntec delineated a chlorinated volatile organic compound (CVOC) plume at the National Aeronautics and Space Administration’s (NASA’s) Vehicle Assembly Building (VAB) area located at KSC, Florida. The RFI activities identified an approximate 115-acre dissolved plume (primarily vinyl chloride) and a trichloroethene (TCE) source area in an active aerospace complex that is surrounded by sensitive wetland/waterbodies. Due to the size of the impacted area, the Corrective Measure Design included a multi-component strategy: (i) address the source area via bioremediation; (ii) protect sensitive wetlands from impacted groundwater discharge via biosparging; and (iii) Long Term Monitoring (LTM) of the remaining dissolved plume. After the Corrective Measures implementation (CMI), NASA and Geosyntec worked with Florida Department of Environmental Protection (FDEP) to implement an adaptive site management for the complex, 115-acre site outside of the traditional RCRA process. The adaptive site management approach relied on performing supplemental assessments and implementing Interim Measures (IMs) to further assess and implement remedies over time while working within site and budget constraints, with an overall goal of achieving enough mass reduction to transition the entire site to LTM and eventually achieve site closure. Approach/Activities. After the biosparge barrier was operational and bioremediation within the source area (referred to as Hot Spot 1) achieved the Corrective Action Objective (CAO), supplemental assessment of the area between Hot Spot 1 and the biosparge barrier was performed. The conceptual site model was updated using the supplemental assessment results and an air sparge system IM was designed to treat an approximate 1.2 acre area (referred to as Hot Spot 2). After installation of the air sparge system, supplemental assessment within the remainder of the 115-acre dissolved plume was performed and a second TCE source area was identified. The TCE source area and associated areas with elevated CVOC concentrations (referred to as Hot Spot 3) were delineated and a bioremediation IM was implemented. Also, the downgradient impacts from Hot Spot 3 were adjacent to a sensitive waterbody, and negotiations with the FDEP allowed the area to be monitored using LTM. Results/Lessons Learned. The performance of supplemental assessment activities and implementation of remedial alternatives as IMs allowed NASA to successfully address groundwater impacts over time, while working within the FDEP regulatory framework. The implementation of the CMI and multiple IMs has achieved the following goals: (i) the biosparge barrier has mitigated the potential discharge of impacted groundwater to an adjacent wetland; (ii) enhanced bioremediation within Hot Spot 1 achieved the CAO within 2 years and transitioned the area into LTM; (iii) operation of an air sparge system within Hot Spot 2 removed TCE as a constituent of concern and contributed to a reduction (approximately 43%) in the impacted groundwater area outside the air sparge treatment area (plume collapse); and (iv) bioremediation within Hot Spot 3 removed approximately 80% of the CVOC mass and contributed to a reduction (approximately 47%) in the impacted groundwater area outside the bioremediation IM treatment area. Overall, the adaptive approach is protecting the sensitive water bodies surrounding the complex site and reducing the area of impacted groundwater, which is moving the entire site towards LTM.

Rebecca C Daprato↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Lessons Learned from the Construction of Upgrades to the NASA Glenn Icing Research Tunnel and Re-activation Testing

Major upgrades were made in 1999 to the 6- by 9-Foot (1.8- by 2.7-m) Icing Research Tunnel (IRT) at the NASA Glenn Research Center. These included replacement of the electronic controls for the variable-speed drive motor, replacement of the heat exchanger, complete replacement and enlargement of the leg of the tunnel containing the new heat-exchanger, the addition of flow-expanding and flow-contracting turning vanes upstream and downstream of the heat exchanger, respectively, and the addition of fan outlet guide vanes (OGV's). This paper presents an overview of the construction and reactivation testing phases of the project. Important lessons learned during the technical and contract management work are documented.

Sheldon, David W.↗
DOE OSTI2026

Effect of Solvent on the Local Structure, Dynamics, and Vibrational Density of States in Sn-BEA Zeolite

Lewis acid zeolites are attractive catalysts for epoxidation and biomass valorization, as they are highly active and selective in the liquid phase and can operate at or near ambient conditions. While a rich experimental literature exists on liquid-phase Lewis acid zeolite catalysis, our understanding of the molecular organization and solvent dynamics in the vicinity of Lewis acid sites with differing metal site speciation remains limited. In this work, we investigate the molecular coordination and diffusion of two common solvents (methanol and water) around the closed and open Sn-BEA zeolite active sites using molecular dynamics simulations with a machine-learned interatomic potential trained on ab initio molecular dynamics trajectories. Molecular dynamics simulations reveal that introducing active sites significantly enhances local order in the first and second solvation shells compared to the pure silica case. For methanol, both closed and open active sites are singly coordinated, while more than two water molecules coordinate the open site. In contrast to methanol, we observed that water molecules dissociate, leading to the formation of additional Sn-OH and silanol groups away from the active site. The diffusion coefficients of water and methanol are functions of the solvent population in the pore. Here, our work provides insights into how active site speciation in Lewis acid zeolites affects solvent coordination, diffusion, and vibrational signature. This information is foundational for catalyst design and optimization of liquid-phase catalytic processes in zeolites. It also demonstrates the suitability of machine-learned interatomic potentials for modeling reactive systems, enabling sufficiently long trajectories for appropriate statistical averaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Behavioral networks as a model for intelligent agents

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs.

Sliwa, Nancy E.↗
NASA NTRS1993

The application of automated operations at the Institutional Processing Center

The JPL Institutional and Mission Computing Division, Communications, Computing and Network Services Section, with its mission contractor, OAO Corporation, have for some time been applying automation to the operation of JPL's Information Processing Center (IPC). Automation does not come in one easy to use package. Automation for a data processing center is made up of many different software and hardware products supported by trained personnel. The IPC automation effort formally began with console automation, and has since spiraled out to include production scheduling, data entry, report distribution, online reporting, failure reporting and resolution, documentation, library storage, and operator and user education, while requiring the interaction of multi-vendor and locally developed software. To begin the process, automation goals are determined. Then a team including operations personnel is formed to research and evaluate available options. By acquiring knowledge of current products and those in development, taking an active role in industry organizations, and learning of other data center's experiences, a forecast can be developed as to what direction technology is moving. With IPC management's approval, an implementation plan is developed and resources identified to test or implement new systems. As an example, IPC's new automated data entry system was researched by Data Entry, Production Control, and Advance Planning personnel. A proposal was then submitted to management for review. A determination to implement the new system was made and elements/personnel involved with the initial planning performed the implementation. The final steps of the implementation were educating data entry personnel in the areas effected and procedural changes necessary to the successful operation of the new system.

Barr, Thomas H.↗
NASA NTRS2008

Reducing NPR 7120.5D to Practice: Preparing for a Life-Cycle Review

In March 2007, NASA issued revised rules for space flight project management, NPR 7120.5D, 'NASA Space Flight Program and Project Management Requirements.' Central to the new rules was the construct of Key Decision Points, maturity gates that the project team must pass in order to continue development. In order that the KDP decision be fully informed, the NPR required, as entrance criteria for the gate, the generation and delivery of specified planning, technical, and cost/schedule documents (gate products) and a life-cycle review, the Preliminary Design Review. Building on JPL experience on the Prometheus and Juno projects, the team successfully organized for and conducted these reviews on an aggressive schedule. Key actions were taken to proactively interact with the SRB, produce high-quality gate products with stakeholder review, generate review presentation materials, and handle a myriad of supporting logistical functions. A review preparation team was established, including a Review Captain and leads for documentation, information systems, and logistics, and their roles, responsibilities and task assignments were identified. Aids were produced, including a detailed review preparation schedule and a comprehensive gate products production table. Institutional support was leveraged early and often. Implementation strategy reflected the needs of a nationally-distributed team, as well as applicable export control and IT security requirements. This paper gives a brief overview of the GRAIL mission and its project management challenges, provides a detailed description of project PMSR and PDR preparation and execution activities, including positive and negative lessons learned, and identifies recommendations for future NASA (and non-NASA) project teams.

NPR 7120.5D↗
NASA NTRS2015

Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.

Knowledge domain↗
NASA NTRS2018

Evolution of KSC EGS Post Space Shuttle - Success Factors and Lessons Learned

The Human Exploration and Operations Mission Directorate (HEOMD) Knowledge Capture & Transfer (KCT) team conducted video interviews with element managers in the Exploration Ground Systems (EGS) Program Office at Kennedy Space Center (KSC). The immediate goal was to capture a point-in-time profile of challenges, solutions, and lessons learned derived from EGS element development activity from the end of the Space Shuttle Program (SSP) to the present time.

Johnson, Patrick↗