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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Velocity distributions of ion beams in the plasma sheet boundary layer

The value of measured ion-beam velocity distributions as diagnostic tools to characterize the propagation and acceleration of particle beams in the plasma-sheet boundary layer is assessed. Models based on adiabatic deformation of a flowing Maxwellian, acceleration by field-aligned potentials, and current-sheet acceleration are fitted to observational data, and the results are presented in extensive diagrams and graphs. The data are found to be consistent with models involving field-aligned or cross-tail potential drops, but not with models based solely on the adiabatic deformation of initially isotropic distributions.

Eastman, T. E.↗
DOE OSTI2020

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Causal Approach to Model Validation and Calibration

This poster presents a novel method for validation and verification that focuses on identifying causal relationships between data elements, moving beyond traditional statistical and machine learning approaches. These methods employ causal discovery techniques to reveal the underlying mechanisms of data generation. The research utilizes structural causal models and directed acyclic graphs to depict causal relationships. This approach assists in achieving alignment between simulation models and reality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗
DOE OSTI2026

FAIR Data Meets FAIR Software

Modern scientific research is increasingly defined by the interplay between data, software, and the workflows that connect them. Yet while the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become foundational for scientific data stewardship, the same level of structure and expectation has only recently begun to extend to research software. This talk covers why and how FAIR principles are being applied to data and software to support data reuse. It outlines the gaps in current sharing norms, the growing federal emphasis on persistent identifiers and public access, and the opportunities created when datasets, computational workflows, code, and models are linked through rich, standardized metadata. Practical implementation pathways for the EIC and JLab communities are described, including datacards for structured dataset documentation and provenance-aware workflows. By aligning data lifecycle management with FAIR-aligned software practices, the scientific community can advance toward autonomous knowledge graphs, generative workflows, and high-quality, AI-ready scientific datasets.

McSpadden, Diana [Thomas Jefferson National Accele↗
NASA NTRS1983

Establishing homologies in protein sequences

Computer-based statistical techniques used to determine homologies between proteins occurring in different species are reviewed. The technique is based on comparison of two protein sequences, either by relating all segments of a given length in one sequence to all segments of the second or by finding the best alignment of the two sequences. Approaches discussed include selection using printed tabulations, identification of very similar sequences, and computer searches of a database. The use of the SEARCH, RELATE, and ALIGN programs (Dayhoff, 1979) is explained; sample data are presented in graphs, diagrams, and tables and the construction of scoring matrices is considered.

Dayhoff, M. O.↗
NASA NTRS1989

Airplane-Acceleration Display For Low-Gravity Research

Bar-graph display facilitates precise control of trajectories. Overall display system includes accelerometer block, power-supply-and-interface box, thrust-axis display, and pitch-and-lateral-axes display. Accelerometer block includes three servoaccelerometers orthogonally mounted on adjustable triaxial base allowing fine alignment of accelerometers with respect to airplane. Accelerometer block and power-supply-and-interface box located near center of gravity of airplane in lugguage area. LED bar-graph displays take up less panel space and easier to align with axes represented. No moving parts and not subject to parallax. With help of display, pilot adheres to predetermined fraction of g, with minimal lateral acceleration.

Millis, Marc G.↗
NASA NTRS1976

Ground state properties of solid and liquid spin-aligned atomic hydrogen

Calculations of the ground state energy in the solid phase were performed with the aid of a variational approach. The Morse potential form of the atomic triple potential computed by Kolos and Wolniewicz (1965) was employed for the calculations. The ground state energies of both the liquid and solid phases of spin-aligned atomic hydrogen around the volume of the transition are presented in a graph.

Danilowicz, R. L.↗
NASA NTRS2011

NASA Tech Briefs, December 2011

Topics covered include: 1) SNE Industrial Fieldbus Interface; 2) Composite Thermal Switch; 3) XMOS XC-2 Development Board for Mechanical Control and Data Collection; 4) Receiver Gain Modulation Circuit; 5) NEXUS Scalable and Distributed Next-Generation Avionics Bus for Space Missions; 6) Digital Interface Board to Control Phase and Amplitude of Four Channels; 7) CoNNeCT Baseband Processor Module; 8) Cryogenic 160-GHz MMIC Heterodyne Receiver Module; 9) Ka-Band, Multi-Gigabit-Per-Second Transceiver; 10) All-Solid-State 2.45-to-2.78-THz Source; 11) Onboard Interferometric SAR Processor for the Ka-Band Radar Interferometer (KaRIn); 12) Space Environments Testbed; 13) High-Performance 3D Articulated Robot Display; 14) Athena; 15) In Situ Surface Characterization; 16) Ndarts; 17) Cryo-Etched Black Silicon for Use as Optical Black; 18) Advanced CO2 Removal and Reduction System; 19) Correcting Thermal Deformations in an Active Composite Reflector; 20) Umbilical Deployment Device; 21) Space Mirror Alignment System; 22) Thermionic Power Cell To Harness Heat Energies for Geothermal Applications; 23) Graph Theory Roots of Spatial Operators for Kinematics and Dynamics; 24) Spacesuit Soft Upper Torso Sizing Systems; 25) Radiation Protection Using Single-Wall Carbon Nanotube Derivatives; 26) PMA-PhyloChip DNA Microarray to Elucidate Viable Microbial Community Structure; 27) Lidar Luminance Quantizer; 28) Distributed Capacitive Sensor for Sample Mass Measurement; 29) Base Flow Model Validation; 30) Minimum Landing Error Powered-Descent Guidance for Planetary Missions; 31) Framework for Integrating Science Data Processing Algorithms Into Process Control Systems; 32) Time Synchronization and Distribution Mechanisms for Space Networks; 33) Local Estimators for Spacecraft Formation Flying; 34) Software-Defined Radio for Space-to-Space Communications; 35) Reflective Occultation Mask for Evaluation of Occulter Designs for Planet Finding; and 36) Molecular Adsorber Coating

Source record↗
DOE OSTI2023

An Efficient High-Order Solver for Diffusion Equations with Strong Anisotropy on Non-Anisotropy-Aligned Meshes

This paper concerns numerical solution of the diffusion equation with strong anisotropy on meshes not aligned with the anisotropic vector field. In order to resolve the numerical pollution for simulations on a non-anisotropy-aligned mesh and reduce the associated high computational cost we propose an effective preconditioner, extending our previous work. Similar to the anisotropy-aligned mesh case, we apply the auxiliary space preconditioning framework to design a preconditioner where a continuous finite element space is used as the auxiliary space for the discontinuous finite element space. The key component is an effective line smoother that can mitigate the high-frequency errors perpendicular to the magnetic field. We design a graph-based approach to find such a line smoother that is approximately perpendicular to the vector fields when the mesh does not align with the anisotropy. Finally, numerical experiments for several benchmark problems are presented, demonstrating the effectiveness and robustness of the proposed preconditioner when applied to Krylov iterative methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Deciding Termination for Ancestor Match- Bounded String Rewriting Systems

Termination of a string rewriting system can be characterized by termination on suitable recursively defined languages. This kind of termination criteria has been criticized for its lack of automation. In an earlier paper we have shown how to construct an automated termination criterion if the recursion is aligned with the rewrite relation. We have demonstrated the technique with Dershowitz's forward closure criterion. In this paper we show that a different approach is suitable when the recursion is aligned with the inverse of the rewrite relation. We apply this idea to Kurth's ancestor graphs and obtain ancestor match-bounded string rewriting systems. Termination is shown to be decidable for this class. The resulting method improves upon those based on match-boundedness or inverse match-boundedness.

Geser, Alfons↗
DOE OSTI2022

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Zhou, Doudou↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
NASA NTRS2004

"We Burn to Learn" About Fuel-Air Mixing Within Aircraft Powerplants

I am working with my branch s advanced diagnostics team to investigate fuel-air mixing in jet-fueled gas turbine combustors and jet-fuel reformers. Our data acquisition begins with bench-top experiments which will help with calibration of equipment for facility testing. While conducting the bench-top experiments I learned to align laser and optical equipment to collect data, to use the data acquisition software, and to process the data into graphs and images. which jet he1 is to be reformed into hydrogen. Testing will commence shortly, after which we will obtain and analyze data and meet a critical milestone for the end of September. I am also designing the layout for a Schlieren system that will be used during that time frame. A Schlieren instrument records changes in the refractive index distribution of transparent media like air flows. The refractive index distribution can then be related to density, temperature, or pressure distributions within the flow. I am working on a scheme to quantify this information and add to the knowledge of the fuel-air mixing process.

Robinson, Heidi N.↗