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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRSDate not supplied

High dimensional similarity search with quantum assisted variational autoencoder

Recent progress in quantum algorithms and hardware is indicator of the potential importance of quantum computing in the next future. However, finding suitable application areas remains an active area of research. Quantum machine learning [1] is touted as a potential approach to demonstrate quantum advantage within both the gate-model [2,3] and the adiabatic [4,5] schemes. For instance, the Quantum-assisted Variational Autoencoder (QVAE) [6] has been proposed as a quantum enhancement to the discrete VAE [7]. We extend on previous work and study the real-world applicability of a QVAE, specifically, for similarity search in large-scale high dimensional datasets. While similarity search algorithms are available for low dimensional datasets, scaling to billion-scale datasets with thousands of dimensions is non-trivial. We show how the latent-space representation of a QVAE can be used to construct a space-efficient search index. We back up our claims by experimental results which show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we show real-world speedups compared to linear search and demonstrate memory efficient scaling to large-scale datasets.

Nicholas D Gao↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
NASA NTRSDate not supplied

The Trash Compaction Processing System (TCPS) Technology Demonstration and Risk Reduction Updates FY23-FY24

The Next STEP Phase BTrash Compaction Processing System (TCPS) is being developed for a technology demonstration on the International Space Station(ISS) to process common spacecraft consumables trash such as food packaging and clothing to reduce trash storage volume, recover water, safen(e.g. reduce biological activity), and shape the trash for storage, jettison, reuse (e.g.,radiation shielding) and/or recycling. Sierra Space is developing the flight demonstration hardware and National Aeronautics and Space Administration(NASA) continues to conduct risk reduction activities to exploreoperational scenarios, setting the stage for successful on-orbit tests. After the flight demonstration on ISS, the TCPS becomes available to be infused into NASA short-and long-term missions.This paper will discuss the rationale for the updated requirement definitions outlined in the 2023 International Conference on Environmental System TCPS paper and the ongoing risk reduction activities. In addition, drawing from lessons learned in the development of the TCPS, a discussion of other compression technologies will be introduced for various mission types.

Trash Compaction Processing System↗
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
NASA NTRSDate not supplied

The Trash Compaction Processing System (TCPS) Technology Demonstration and Risk Reduction Updates FY23-FY24 Presentation

The Next STEP Phase BTrash Compaction Processing System (TCPS) is being developed for a technology demonstration on the International Space Station(ISS) to process common spacecraft consumables trash such as food packaging and clothing to reduce trash storage volume, recover water, safen(e.g. reduce biological activity), and shape the trash for storage, jettison, reuse (e.g.,radiation shielding) and/or recycling. Sierra Space is developing the flight demonstration hardware and National Aeronautics and Space Administration(NASA) continues to conduct risk reduction activities to exploreoperational scenarios, setting the stage for successful on-orbit tests. After the flight demonstration on ISS, the TCPS becomes available to be infused into NASA short-and long-term missions.This paper will discuss the rationale for the updated requirement definitions outlined in the 2023 International Conference on Environmental System TCPS paper and the ongoing risk reduction activities. In addition, drawing from lessons learned in the development of the TCPS, a discussion of other compression technologies will be introduced for various mission types.

Trash Compaction Processing System↗
DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗
NASA NTRS2012

Launch Pad Closeout Operations for the Mars Science Laboratory's Heat Rejection System

The Mars Science Laboratory (MSL) rover was launched on an Atlas V on November 26, 2011. Preparations were carried out prior to launch in order to closeout the spacecraft's complex heat rejection system (HRS), which consists of two mechanically pumped CFC-11 fluid loops. The first HRS loop, onboard the Curiosity rover, was fully integrated, filled with CFC-11, and successfully operated prior to launch pad operations; however, the second thermal loop, called the cruise HRS loop, required final mechanical and thermal integration activities to occur while on the launch pad in order to accommodate the last minute installation of the rover's Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) power source. In order to prevent overheating of propellant tanks and critical avionics equipment buried deep within the spacecraft's aeroshell, the MMRTG needed to be pre-cooled using a separate non-flight mechanically pumped fluid loop prior to and during the final closeout and subsequent startup of the flight loop. This paper outlines the various steps that took place to safely install the MMRTG while carefully transitioning from the pre-cooling operation to the final startup and operation of the flight cruise HRS loop. Temperature data of the launch pad thermal transition from the ground support loop activity to the final flight loop operation is presented. Some background development of the ground support loop and lessons learned are also discussed. This successful launch pad integration activity required a close-knit coordination between NASA KSC, JPL, the Department of Energy, Idaho National Labs, Pratt and Whitney Rocketdyne Inc., Teledyne Technologies Inc., ULA, and Advanced Thermal Sciences Corp.

thermal↗
DOE OSTI2024

Graphene-supported single atom catalysts for high performance lithium-oxygen batteries

The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithiumoxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. Here we then used machine learning (ML) to develop an intrinsic descriptor, phi, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N 4 active site on graphene surface. We established a linear relationship between phi and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Cartesian equivariant representations for learning and understanding molecular orbitals

Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.

chemical reactions↗
DOE OSTI2020

Stability of heterogeneous single-atom catalysts: a scaling law mapping thermodynamics to kinetics

Heterogeneous single-atom catalysts (SACs) hold the promise of combining high catalytic performance with maximum utilization of often precious metals. We extend the current thermodynamic view of SAC stability in terms of the binding energy (E bind ) of single-metal atoms on a support to a kinetic (transport) one by considering the activation barrier for metal atom diffusion. A rapid computational screening approach allows predicting diffusion barriers for metal–support pairs based on E bind of a metal atom to the support and the cohesive energy of the bulk metal (E c ). Metal–support combinations relevant to contemporary catalysis are explored by density functional theory. Assisted by machine-learning methods, we find that the diffusion activation barrier correlates with (E bind ) 2 /E c in the physical descriptor space. This diffusion scaling-law provides a simple model for screening thermodynamics to kinetics of metal adatom on a support.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Achieving Improved Reliability with Failure Analysis

Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs, PCBAs and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA, fishbone, FTA), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Will You Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM etc.) -Destructive analysis (DPA, Decap, FIB etc.) -Materials characterization (XRF, EDS, TMA/DSC etc.) Who Will Benefit: Reliability engineers, failure analysis engineers, engineering managers, design engineers, component engineers, quality assurance functions and, personnel involved with reliability activities within their company.

non-destructive techniques↗