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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Space station Simulation Computer System (SCS) study for NASA/MSFC. Volume 6: Study issues report

The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex (PTC) at the Marshall Space Flight Center (MSFC). The PTC will train the space station payload specialists and mission specialists to operate the wide variety of experiments that will be on-board the Freedom Space Station. This simulation Computer System (SCS) study issues report summarizes the analysis and study done as task 1-identify and analyze the CSC study issues- of the SCS study contract.This work was performed over the first three months of the SCS study which began in August of 1988. First issues were identified from all sources. These included the NASA SOW, the TRW proposal, and working groups which focused the experience of NASA and the contractor team performing the study-TRW, Essex, and Grumman. The final list is organized into training related issues, and SCS associated development issues. To begin the analysis of the issues, a list of all the functions for which the SCS could be used was created, i.e., when the computer is turned on, what will it be doing. Analysis was continued by creating an operational functions matrix of SCS users vs. SCS functions to insure all the functions considered were valid, and to aid in identification of users as the analysis progressed. The functions will form the basis for the requirements, which are currently being developed under task 3 of the SCS study.

Source record↗
NASA NTRS1989

Global models: Robot sensing, control, and sensory-motor skills

Robotics research has begun to address the modeling and implementation of a wide variety of unstructured tasks. Examples include automated navigation, platform servicing, custom fabrication and repair, deployment and recovery, and science exploration. Such tasks are poorly described at onset; the workspace layout is partially unfamiliar, and the task control sequence is only qualitatively characterized. The robot must model the workspace, plan detailed physical actions from qualitative goals, and adapt its instantaneous control regimes to unpredicted events. Developing robust representations and computational approaches for these sensing, planning, and control functions is a major challenge. The underlying domain constraints are very general, and seem to offer little guidance for well-bounded approximation of object shape and motion, manipulation postures and trajectories, and the like. This generalized modeling problem is discussed, with an emphasis on the role of sensing. It is also discussed that unstructured tasks often have, in fact, a high degree of underlying physical symmetry, and such implicit knowledge should be drawn on to model task performance strategies in a methodological fashion. A group-theoretic decomposition of the workspace organization, task goals, and their admissible interactions are proposed. This group-mechanical approach to task representation helps to clarify the functional interplay of perception and control, in essence, describing what perception is specifically for, versus how it is generically modeled. One also gains insight how perception might logically evolve in response to needs of more complex motor skills. It is discussed why, of the many solutions that are often mathematically admissible to a given sensory motor-coordination problem, one may be preferred over others.

Schenker, Paul S.↗
NASA NTRS1996

National Wind Tunnel Complex (NWTC)

The National Wind Tunnel Complex (NWTC) Final Report summarizes the work carried out by a unique Government/Industry partnership during the period of June 1994 through May 1996. The objective of this partnership was to plan, design, build and activate 'world class' wind tunnel facilities for the development of future-generation commercial and military aircraft. The basis of this effort was a set of performance goals defined by the National Facilities Study (NFS) Task Group on Aeronautical Research and Development Facilities which established two critical measures of improved wind tunnel performance; namely, higher Reynolds number capability and greater productivity. Initial activities focused upon two high-performance tunnels (low-speed and transonic). This effort was later descoped to a single multipurpose tunnel. Beginning in June 1994, the NWTC Project Office defined specific performance requirements, planned site evaluation activities, performed a series of technical/cost trade studies, and completed preliminary engineering to support a proposed conceptual design. Due to budget uncertainties within the Federal government, the NWTC project office was directed to conduct an orderly closure following the Systems Design Review in March 1996. This report provides a top-level status of the project at that time. Additional details of all work performed have been archived and are available for future reference.

Source record↗
NASA NTRS2004

Preliminary Analysis of ISS Maintenance History and Implications for Supportability of Future Missions

The International Space Station (ISS) enables the study of supportability issues associated with long-duration human spaceflight. The ISS is a large, complex spacecraft that must be maintained by its crew. In contrast to the Space Shuttle Orbiter vehicle, but similar to spacecraft that will be component elements of future missions beyond low-Earth orbit, ISS does not return to the ground for servicing and provisioning of spares is severely constrained by transportation limits. Although significant technical support is provided by ground personnel, all hands-on maintenance tasks are performed by the crew. It is expected that future missions to distant destinations will be further limited by lack of resupply opportunities and will, eventually, become largely independent of ground support. ISS provides an opportunity to begin learning lessons that will enable future missions to be successful. Data accumulated over the first several years of ISS operations have been analyzed to gain a better understanding of maintenance-related workload. This analysis addresses both preventive and corrective maintenance and includes all U.S segment core systems. Systems and tasks that are major contributors to workload are identified. As further experience accrues, lessons will be learned that will influence future system designs so that they require less maintenance and, when maintenance is required, it can be performed more efficiently. By heeding the lessons of ISS it will be possible to identify system designs that should be more robust and point towards advances in both technology and design that will offer the greatest return on investment.

Watson, Kevin J.↗
NASA NTRSDate not supplied

Planning and Monitoring for Swarm Search and Service Missions

To transition from control theory to real applications, it is important to study missions such as Swarm Search and Service (SSS) where vehicles are not only required to search an area, but also service all jobs that they find. In SSS missions each type of job requires a group of vehicles to break-off from the swarm for a given amount of time to successfully service it. The required number of vehicles and the service rate are unique to each job type. Once a job has been completed the vehicles are able to return to the swarm for use elsewhere. If not enough vehicles are present in the swarm at the time that the job is identified, that job is dropped without being serviced. Human operators as tasked with effectively planning and managing these complex missions. This work presents a user study that seeks to test the efficacy and ease-of-use of a prediction model known as the Hybrid Model as an aid in planning and monitoring tasks. Results show that the novel computational model aid allows operators to more effectively choose the necessary swarm size to handle expected mission workload, as well as, maintain sufficient situation awareness to evaluate the performance of the swarm during missions.

swarm search and service↗
NASA NTRS2021

Planning and Monitoring Multi-Job Type Swarm Search and Service Missions

To transition from control theory to real applications, it is important to study missions such as Swarm Search and Service (SSS) where vehicles are not only required to search an area, but also service all jobs that they find. In SSS missions, each type of job requires a group of vehicles to break off from the swarm for a given amount of time to service it. The required number of vehicles and the service rate are unique to each job type. Once a job has been completed, the vehicles are able to return to the swarm for use elsewhere. If not, enough vehicles are present in the swarm at the time that the job is identified, that job is dropped without being serviced. In SSS missions that occur in open environments, the arrival rate of jobs varies dynamically as vehicles move in and out of the swarm to service jobs. Human operators are tasked with effectively planning and managing these complex missions. This paper presents a user study that seeks to test the efficacy and ease-of-use of a prediction model known as the Hybrid Model as an aid in planning and monitoring tasks. Results show that the novel computational model aid allows operators to more effectively choose the necessary swarm size to handle expected mission workload, as well as, maintain sufficient situation awareness to evaluate the performance of the swarm during missions.

swarm search and service↗
NASA NTRSDate not supplied

Durable Anti-Reflective and Anti-Fog Coatings Produced by Aerosol Impact Driven Assembly

The Exploration Extravehicular Mobility Unit’s (xEMU) helmet is a complex assembly designed to accomplish several tasks to protect and enable astronauts. In addition to maintaining a suitable environment for the wearer, it must also allow for appropriate mobility and provide a wide and undistorted view of the surroundings. A critical component of the helmet’s optical system is the anti-fog coating. While previous versions of the anti-fog coating have provided suitable anti-fog performance, they have been difficult to apply, lacked mechanical or chemical durability, or resulted in unanticipated failures (e.g., outgassing of eye-irritating materials during use). This work describes the use of a new coating technology, aerosol impact-driven assembly (AIDA), to develop a next-generation permanent anti-fog coating for the xEMU helmet. AIDA’s unique ability to tune both the refractive index and surface roughness of films was used to deposit a thin, transparent (>85% transmittance of visible light), and hydrophilic (contact angle <10°) anti-fog coating. The coating’s abrasion resistance and chemical resistance was evaluated and it was found that the coating maintained both its anti-fog and anti-reflective functionally. Finally, the scalability of the process was demonstrated by successfully coating the polycarbonate blanks used to form the helmet’s bubble.

Spacesuit↗
DOE OSTI2024

Molecular dynamics simulations of uranyl and plutonyl cations in a task-specific ionic liquid

Ionic liquids (ILs) are a unique class of solvents with potential applications in advanced separation technologies relevant to the nuclear industry. ILs are salts with low melting points and a wide range of tunable physical properties, such as viscosity, hydrophobiciy, conductivity, and liquidus range. ILs have negligible vapor pressure, are often non-flammable, and can have high thermal stability and a wide electrochemical window, making them attractive for use in separations processes relevant to the nuclear industry. Metal salts generally have a low solubility in ILs; however, by incorporating new functional groups into the IL cation or anion that promote complexation with the metal, the solubility can be greatly increased. One such task-specific ionic liquid (TSIL) is 1-carboxy-N, N, N-trimethylglycine bis(trifluoromethylsulfonyl)imide ([Hbet][Tf 2 N]). Water, which is detrimental for electrochemical separations, is a common impurity in ILs and can coordinate with actinyl cations, particularly in ILs containing only weakly coordinating components. Understanding the behavior of actinides in TSIL/water mixtures on a molecular level is vital for designing improved separations processes. Classical molecular dynamics simulations of uranyl(VI) and plutonyl(VI) in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][Tf 2 N]) with deprotonated Hbet (betaine) and water have been performed to understand the coordination and dynamics of the actinyl cations. We find that betaine is a much stronger ligand than water and prefers to coordinate the metal in a bidentate manner. Potential of mean force simulations yield a relative free energy for betaine coordination of approximately -120 to -90 kJ/mol in mixtures with water. As the amount of betaine coordinated to the actinide increases, the diffusion coefficient of the actinyl cation decreases. Moreover, the betaine ligand is able to bridge between two metal centers, resulting in dimeric complexes with actinide–actinide distances of ~5 Å. Potential of mean force simulations show that these structures are stable, with relative free energies of up to -40 kJ/mol. The crystal structure for [(UO 2 ) 2 (bet) 6 (H 2 O) 2 ][Tf 2 N] 4 shows that the betaine bridges between two uranium atoms to form dimeric complexes similar to those found in our simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
NASA NTRS1996

Immobile Robots: AI in the New Millennium

A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.

Williams, Brian C.↗
NASA NTRS2019

Inversion of Multiangular Polarimetric Measurements Over Open and Coastal Ocean Waters: A Joint Retrieval Algorithm for Aerosol and Water-Leaving Radiance Properties

Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.

Gao, Meng↗
NASA NTRS1980

The event-related brain potential as an index of attention allocation in complex displays

The advantages of employing the event-related brain potential (ERP) in the assessment of allocation of attention in dynamic environments are discussed. Three experiments are presented in which the P300 component of the ERP is demonstrated to be a useful index of subjects' locus of attention. The first two experiments were concerned with the allocation of attention during discrete and continuous visual monitoring tasks. The results indicated that a P300 was elicited only by stimuli to which the subject had to attend in order to perform successfully the task. The third experiment was conducted to assess the sensitivity of P300 to the manner in which attention is allocated to different aspects of a display during the performance of a 3-dimensional target acquisition task. The amplitude of the P300 was found to reflect differences between two levels of workload, as well as the task relevance of the stimuli. The results of the experiments are discussed in terms of their utility in the evaluation of the design of man-machine systems as well as in the study of the allocation of attention in operational environments.

Wickens, C. D.↗
NASA NTRS1989

Hierarchical Ada robot programming system (HARPS)- A complete and working telerobot control system based on the NASREM model

HARPS is a telerobot control system that can perform some simple but useful tasks. This capability is demonstrated by performing the ORU exchange demonstration. HARPS is based on NASREM (NASA Standard Reference Model). All software is developed in Ada, and the project incorporates a number of different CASE (computer-aided software engineering) tools. NASREM was found to be a valid and useful model for building a telerobot control system. Its hierarchical and distributed structure creates a natural and logical flow for implementing large complex robust control systems. The ability of Ada to create and enforce abstraction enhanced the implementation of such control systems.

Leake, Stephen↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
NASA NTRS2000

Computer Modeling of Real-Time Dynamic Lighting

Space Station tasks involve procedures that are very complex and highly dependent on the availability of visual information. In many situations, cameras are used as tools to help overcome the visual and physical restrictions associated with space flight. However, these cameras are effected by the dynamic lighting conditions of space. Training for these is conditions is necessary. The current project builds on the findings of an earlier NRA funded project, which revealed improved performance by humans when trained with computer graphics and lighting effects such as shadows and glare.

Maida, James C.↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS1988

TES: A modular systems approach to expert system development for real-time space applications

A major goal of the Space Station era is to reduce reliance on support from ground based experts. The development of software programs using expert systems technology is one means of reaching this goal without requiring crew members to become intimately familiar with the many complex spacecraft subsystems. Development of an expert systems program requires a validation of the software with actual flight hardware. By combining accurate hardware and software modelling techniques with a modular systems approach to expert systems development, the validation of these software programs can be successfully completed with minimum risk and effort. The TIMES Expert System (TES) is an application that monitors and evaluates real time data to perform fault detection and fault isolation tasks as they would otherwise be carried out by a knowledgeable designer. The development process and primary features of TES, a modular systems approach, and the lessons learned are discussed.

Cacace, Ralph↗
NASA NTRS1995

CaseMIDAS - A reactive planning architecture for the man-machine integration design and analysis system

MIDAS is a set of tools which allow a designer to specify the physical and functional characteristics of a complex system such as an aircraft cockpit, and analyze the system with regard to human performance. MIDAS allows for a number of static analyses such as military standard reach and fit analysis, display legibility analysis, and vision polars. It also supports dynamic simulation of mission segments with 3d visualization. MIDAS development has incorporated several models of human planning behavior. The CaseMIDAS effort has been to provide a simplified and unified approach to modeling task selection behavior. Except for highly practiced, routine procedures, a human operator exhibits a cognitive effort while determining what step to take next in the accomplishment of mission tasks. Current versions of MIDAS do not model this effort in a consistent and inclusive manner. CaseMIDAS also attempts to address this issue. The CaseMIDAS project has yielded an easy to use software module for case creation and execution which is integrated with existing MIDAS simulation components.

Pease, R. Adam↗