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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Computer network environment planning and analysis

The GSFC Computer Network Environment provides a broadband RF cable between campus buildings and ethernet spines in buildings for the interlinking of Local Area Networks (LANs). This system provides terminal and computer linkage among host and user systems thereby providing E-mail services, file exchange capability, and certain distributed computing opportunities. The Environment is designed to be transparent and supports multiple protocols. Networking at Goddard has a short history and has been under coordinated control of a Network Steering Committee for slightly more than two years; network growth has been rapid with more than 1500 nodes currently addressed and greater expansion expected. A new RF cable system with a different topology is being installed during summer 1989; consideration of a fiber optics system for the future will begin soon. Summmer study was directed toward Network Steering Committee operation and planning plus consideration of Center Network Environment analysis and modeling. Biweekly Steering Committee meetings were attended to learn the background of the network and the concerns of those managing it. Suggestions for historical data gathering have been made to support future planning and modeling. Data Systems Dynamic Simulator, a simulation package developed at NASA and maintained at GSFC was studied as a possible modeling tool for the network environment. A modeling concept based on a hierarchical model was hypothesized for further development. Such a model would allow input of newly updated parameters and would provide an estimation of the behavior of the network.

Dalphin, John F.↗
NASA NTRS2002

Design and architecture of the Mars relay network planning and analysis framework

In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

Mars relay network planning analysis↗
NASA NTRSDate not supplied

Determining the Most Influencing Medical Conditions in MEDPRAT’s SIN Directed Graph

INTRODUCTION: The Susceptibility Inference Network (SIN) is a network of medical conditions, part of the Medical Extensible Probabilistic Risk Assessment Tool (MEDPRAT) developed by NASA to assess human health and medical risk to space exploration missions. The SIN is subject matter expert informed and acts as a prototype that provides relationships and dependencies between events modeled by MEDPRAT. Each vertex in the SIN has a weight which evaluates the severity of having the condition regardless of the progression from or to that condition. In this presentation, we consider two statistics to measure that stand alone risk: Quality Time Lost (QTL) and Loss of Crew Life (LOCL). Our goal is to identify the medical conditions that contribute the most to crew members QTL and LOCL risks due to progression of conditions in the network. We investigate how different computation parameters result in different condition rankings and address the choice of parameters that allows appropriate interventions to ensure space mission success. METHODS: The Katz score, one of many centrality measures created for ranking purposes in network analysis, takes into account all possible walks through the network, penalizing each additional step in a walk by a factor α called the Katz parameter. The literature does not provide specific values for the choice of α. We derive an analytical relationship between α and the maximum path length which has influence on the Katz score and ranking. Based on the probability of progression of each condition in the SIN, we identify that maximum path length of interest and calculate α that is then used in the Katz formula to rank the conditions in the SIN. RESULTS AND CONCLUSION: The effective probabilities of the SIN matrix generally fall below 10−6, which is below the level of the least influencing condition in the set. This corresponds to the probability of at most six consecutive progressions of a condition. Consequently, we calculate the Katz Parameter α and get 0.32. We rank the medical conditions and find that Acute Radiation Symptom is the condition the most prone to contribute to quality time loss due to progression.

risk analysis↗
DOE OSTI2024

Machine Learning Application for CCUS and Fracture Analysis

This is an invited guest speaker's presentation. The present covers three use cases by applying machine learning techniques. The use cases include fracture analysis for CCUS: IBDP study, multiple level of fracture network analysis and tool: HFTS1 study, Frac-Hit with Middleland Basin datasets from collaborations with Company A.

Liu, Guoxiang↗
DOE OSTI2026

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗
DOE OSTI2026

High yield production of 3-hydroxypropionic acid using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Biotechnology↗
NASA NTRS1987

SPAN: Ocean science

The Space Physics Analysis Network (SPAN) is a multi-mission, correlative data comparison network which links space and Earth science research and data analysis computers. It provides a common working environment for sharing computer resources, sharing computer peripherals, solving proprietary problems, and providing the potential for significant time and cost savings for correlative data analysis. This is one of a series of discipline-specific SPAN documents which are intended to complement the SPAN primer and SPAN Management documents. Their purpose is to provide the discipline scientists with a comprehensive set of documents to assist in the use of SPAN for discipline specific scientific research.

Thomas, Valerie L.↗
NASA NTRSDate not supplied

NEUROSPF: A Tool For the Symbolic Analysis of Neural Networks

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model’s parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them.

neural networks↗
NASA NTRS1987

Analysis of a Four-Reflector S/X-Band Antenna

Physical optics accounts for near field, cross polarization, and higher-order modes. Report presents physical-optics analysis of four-reflector, 64-m antennas of Deep Space Network. Analysis thorough and detailed. Report has instructional value as example for designers of large microwave dishes with subreflectors and involving reflector surfaces with hyperboloidal, paraboloidal, ellipsoidal, and more complex shapes.

Cha, Alan G.↗
NASA NTRS2005

Systemic Analysis Approaches for Air Transportation

Air transportation system designers have had only limited success using traditional operations research and parametric modeling approaches in their analyses of innovations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be used with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed. However, air transportation has proven itself an extensive, complex system whose behavior is difficult to describe, no less predict. There is a wide range of system analysis techniques available, but some are more appropriate for certain applications than others. Specifically in the area of complex system analysis, the literature suggests that both agent-based models and network analysis techniques may be useful. This paper discusses the theoretical basis for each approach in these applications, and explores their historic and potential further use for air transportation analysis.

Conway, Sheila↗
NASA NTRS2023

Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca

A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning

Wildfire modeling↗
DOE OSTI2026

A tissue‐resolved, network‐based transcriptomic framework for abiotic stress responses in sorghum

Developing climate‐resilient crops requires a detailed understanding of stress‐induced gene expression dynamics, as maladaptive responses can compromise their productivity and survival. Sorghum, a globally important cereal with exceptional tolerance to multiple abiotic stresses, provides a powerful system for investigating these dynamics. However, how stress type, tissue specificity, and temporal progression jointly shape transcriptomic responses in crops remains poorly understood. Here, we present a comparative, time‐resolved transcriptomic atlas of sorghum responses to drought, heat, and salinity stress across shoot and root tissues. Integrative analyses revealed that tissue specificity is the dominant determinant of abiotic stress‐induced gene reprogramming across all three stresses. Building on these global comparisons, we focused on heat stress, as it elicited the most coherent and pronounced transcriptional and regulatory responses, enabling deeper network‐level interrogation. Co‐expression network analysis identified tissue‐specific modules enriched for phytohormone‐responsive genes, while gene regulatory network (GRN) mapping and cistrome analyses uncovered transcription factors (TFs) controlling key hub genes within these modules. Together, this study provides a foundational transcriptomic and network‐based resource for dissecting the regulatory architecture of abiotic stress responses in sorghum and offers prioritized candidates for future functional validation and engineering of climate‐resilient crops.

abiotic stress↗
NASA NTRSDate not supplied

Machine Learning for the Validation of Expert-Elicited Causal Risk Diagrams

Exposure to spaceflight poses risk to human health in complex ways. To help manage this risk, the Human Systems Risk Board (HSRB) at the National Aeronautics and Space Administration (NASA) maintains a set of causal diagrams that attempt to explain how spaceflight hazards generate health risks and lead to adverse outcomes both in-mission, immediately post-mission, and over the long term. These causal risk diagrams are formulated as directed acyclic graphs (DAGs) and can function as knowledge graphs of connected risks and outcomes. These DAGs have proven useful for communication, and, through network analysis, have allowed for the identification of structurally important factors in the risk network. However, the utility these DAGs provide is directly proportional to their verisimilitude, making assessment of this trait using empirical data – whether from actual human spaceflight or various spaceflight analogue exposures and model organisms – a high priority. In this research we explore the use of machine learning algorithms to learn DAG structure from empirical data as a means of evaluating human-elicited DAG structures. To do so, we test several different graph structure-learning algorithms on data concerning changes in the bones of rats and mice after exposure to either spaceflight or a spaceflight analogue. We explore potential methods for indexing the similarity between each algorithm’s output DAG with all the others and with that of the expert-elicited DAG. We discuss next steps in this ongoing line of research and open science initiatives underway to complete them.

directed acyclic graphs↗
NASA NTRS1986

Near-real-time data transmission during the ICE - Comet Giacobini-Zinner encounter

The data links established between the U.S. and Europe during the September 1985 International Cometary Explorer/Comet Giacobini-Zinner encounter are summarized. The Space Physics Analysis Network (SPAN), which is a link between U.S. universities, research institutes and NASA centers, was responsible for the rapid dissemination and analysis of the data obtained from the encounter. The network was then linked across the Atlantic to support investigators involved in a European experiment on board the spacecraft. It is concluded that SPAN provided ESA personnel with a unique opportunity to experience near-real-time data acquisition. The data transfer was performed successfully, and the experience gained proved useful in assessing ESA's needs for future participation in scientific international networking.

Sanderson, T. R.↗
NASA NTRSDate not supplied

Concept-based Analysis of Neural Networks via Vision-Language Models

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. We describe a logical specification language Con spec designed to facilitate writing specifications in terms of these concepts. To define and formally check Con spec specifications, we build a map between the internal representations of a given vision model and a VLM, leading to an efficient verification procedure of natural-language properties for vision models. We demonstrate our techniques on a ResNet-based classifier trained on the RIVAL-10 dataset using CLIP as the multimodal model.

Large Vision Language Models↗