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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Modular Optimal Power Flow Method for Integrating New Technologies in Distribution Grids

This work proposes a modular concept to build optimal power flow (OPF) models for distribution networks containing various emerging technologies under diverse ownership structures, to efficiently deal with evolving technology capabilities and information sharing or privacy constraints. This concept will support any typical OPF application (e.g., optimal dispatch of a given asset without violating grid constraints) by coordinating between grid module and technology module without the need to recreate various modeling elements as technology capability changes due to innovation. Moreover, the modularity of the proposed concept enables achieving system level objectives without sharing detailed information on module level objectives and constraints among modules. To achieve this, the proposed work develops a gradient-descent algorithm which builds upon the literature on the state-of-the-art power flow approximation. The proposed concept is demonstrated with two case studies of i) controllable loads and ii) battery energy storage system (BESS) on an actual large-scale distribution grid.

Hanif, Sarmad↗
DOE OSTI2022

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Further, a case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

42 ENGINEERING↗
NASA NTRS1996

User-Centered Indexing for Adaptive Information Access

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Leading and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.

Chen, James R.↗
NASA NTRS1999

Constellation Design of the Mars Network

Mars has become the focus of an unprecedented series of missions spanning many years, involving numerous nations and evolving from robotics to humans.

Constellation Design Communications Navigation↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimizing Cryo-Focused Pyrolysis GC/MS for Tracing Soil Organic Matter Across Diverse Ecosystems

The cycling of organic matter in terrestrial soils and sediments is central to a range of biogeochemical processes that regulate nutrient cycling, crop productivity, trace gas emissions, and contaminant transport. Pyrolysis-gas chromatography/mass spectrometry (py-GC/MS) is a powerful tool for characterizing bulk soil organic matter (SOM) at the molecular level. In this study, we used a cryo-focused py-GC/MS system to analyze soil samples from seven diverse ecosystems: vernal pool, prairie pothole, temperate forest, tropical forest, tundra, wildfire-affected boreal forest, and grassland. We addressed a key bottleneck in molecular-level SOM characterization by developing an automated data analysis pipeline to optimize py-GC/MS and complementary evolved gas analysis/mass spectrometry (EGA/MS) methods, incorporating advanced tools for peak deconvolution, developing a custom compound class library, and implementing fragmentation spectrum-based molecular networking for the first time. This improved workflow was applied to soil samples from all seven ecosystems, including multiple depths and density fractions. Our findings demonstrate that ecosystem type plays a dominant role in shaping compositional differences in SOM. We also identified trends in the source of SOM compounds (e.g., microbial vs plantderived) across soil depth and density fractions, which are critical for understanding persistence and turnover of SOM. Our molecular networking analysis indicated that although many compounds are widespread across ecosystems, others are restricted to specific environments, such as wetlands. This underscores the utility of molecular-level data in elucidating the complexity of SOM composition and the environmental drivers that shape it. Such molecular-level insights can deepen our knowledge of biogeochemical SOM cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Colloidal Clusters and Networks Formed by Oppositely Charged Nanoparticles with Varying Stiffnesses

Colloidal clusters and gels are ubiquitous in science and technology. Particle softness has a strong effect on interparticle interactions; however, our understanding of the role of this factor in the formation of colloidal clusters and gels is only beginning to evolve. Here, we report the results of experimental and simulation studies of the impact of particle softness on the assembly of clusters and networks from mixtures of oppositely charged polymer nanoparticles (NPs). Experiments were performed below or above the polymer glass transition temperature, at which the interaction potential and adhesive forces between the NPs were significantly varied. Hard NPs assembled in fractal clusters that subsequently organized in a kinetically arrested colloidal gel, while soft NPs formed dense precipitating aggregates, due to the NP deformation and the decreased interparticle distance. Importantly, interactions of hard and soft NPs led to the formation of discrete precipitating NP aggregates at a relatively low volume fraction of soft NPs. A phenomenological model was developed for interactions of oppositely charged NPs with varying softnesses. Furthermore, the experimental results were in agreement with molecular dynamics simulations based on the model. This work provides insight on interparticle interactions before, during, and after the formation of hard–hard, hard–soft, and soft–soft contacts and has impact for numerous applications of reversible colloidal gels, including their use as inks for additive manufacturing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1994

Mapping analysis and planning system for the John F. Kennedy Space Center

Environmental management, impact assessment, research and monitoring are multidisciplinary activities which are ideally suited to incorporate a multi-media approach to environmental problem solving. Geographic information systems (GIS), simulation models, neural networks and expert-system software are some of the advancing technologies being used for data management, query, analysis and display. At the 140,000 acre John F. Kennedy Space Center, the Advanced Software Technology group has been supporting development and implementation of a program that integrates these and other rapidly evolving hardware and software capabilities into a comprehensive Mapping, Analysis and Planning System (MAPS) based in a workstation/local are network environment. An expert-system shell is being developed to link the various databases to guide users through the numerous stages of a facility siting and environmental assessment. The expert-system shell approach is appealing for its ease of data access by management-level decision makers while maintaining the involvement of the data specialists. This, as well as increased efficiency and accuracy in data analysis and report preparation, can benefit any organization involved in natural resources management.

Hall, C. R.↗
DOE OSTI2026

Gamma-ray signatures of 𝑟-process radioactivity from the collapse of magnetized white dwarfs

We predict the gamma-ray line emission from 𝑟-process nuclei synthesized in the ejecta of the accretion-induced collapse (AIC) of a magnetized, rapidly rotating white dwarf. Using ejecta from a two-dimensional general-relativistic neutrino-magnetohydrodynamic simulation, further evolved with a radiation-hydrodynamics code coupled to an in situ nuclear reaction network, we construct angle-dependent gamma-ray spectra in the 0.01–10 MeV band via composition-dependent ray tracing through the ejecta. The emission between ∼1 and 10 d is dominated by 132 I (𝑡 1/2 = 2.3 h), continuously replenished by the decay of its parent 132 Te (𝑡 1/2 = 3.2 d), with additional contributions from 131 I, 133 Xe, and 132 Te . At 𝑡 ≳ 20 d, 56 Co (from 56 Ni decay) becomes the primary emitter. The simultaneous presence of 𝑟 process and iron-peak gamma-ray lines is distinctive of AIC ejecta and absent in binary neutron star mergers, where iron-peak nuclei are generally not synthesized. Comparing with the 3⁢𝜎 continuum sensitivities of planned MeV gamma-ray telescopes (COSI, AMEGO-X, e-ASTROGAM, GRAMS, GammaTPC), we find the brightest 𝑟-process lines detectable to ∼10 Mpc by GammaTPC and GRAMS, with the signal approaching their sensitivity threshold at 30 Mpc. As a result, the 𝑟-process spectral features survive time integration over ∼30 d exposures, demonstrating robustness against the long observation times required by gamma-ray detectors.

Nuclear reactions↗
NASA NTRS1998

The Paradox of Filamented Coronal Hole Flow but Uniform High Speed Wind

Plumes and rays in coronal holes are nearly radially aligned density striations that follow the ambient magnetic field. They have long been known, but have gained new interest with growing awareness that coronal hole flow is inherently filamentary. In retrospect, filamentary flow should have been no surprise. This is because,Beta much less than 1 in coronal holes inside approximately 10 Solar radius, allowing the flow to be filamentary down to the smallest scale of photospheric magnetic activity. While the magnetic field itself is locally smooth across any height above ca. 50,000 km, SOHO/MDI has shown that the photospheric magnetic field is a complex array of rapidly evolving small bipoles that are constantly emerging, evolving, and cancelling. The resulting activity is manifested in microflares, concentrated in the magnetic network, that produce Impulsive injections at the footpoints of coronal field lines. The uneven distribution of this activity in space and time is the source of coronal hole filamentation. What is surprising is that the radial flow speed also exhibits filamentary structure. It is not well described as smooth, spherically symmetric, diverging flow, but instead ranges from 300 to over 1000 km/s at 5.5 Solar radius among field-aligned filaments like those seen in plumes and rays [Feldman et al., JGR, Dec. 1997]. This is completely unlike the constant high speed solar wind reported beyond 0.3 AU. Consequently, plumes and filamentary structure must be strongly mixed, and the mixing must be far along by 0.3 AU to be consistent with Helios observations. The paradox is what causes the mixing? Existing models of coronal heating and solar wind acceleration hardly address this issue. One possibility we are investigating is the MHD Kelvin-Helmholtz instability, to which the shear between plumes and interplume corona is expected to become unstable at 5-10 Solar radius. This instability can be simulated and followed far into the nonlinear regime and may lead to Alfvenic fluctuations like those seen at 1 AU.

Suess, Steven T.↗
NASA NTRS2006

The NASA Space Communications Data Networking Architecture

The NASA Space Communications Architecture Working Group (SCAWG) has recently been developing an integrated agency-wide space communications architecture in order to provide the necessary communication and navigation capabilities to support NASA's new Exploration and Science Programs. A critical element of the space communications architecture is the end-to-end Data Networking Architecture, which must provide a wide range of services required for missions ranging from planetary rovers to human spaceflight, and from sub-orbital space to deep space. Requirements for a higher degree of user autonomy and interoperability between a variety of elements must be accommodated within an architecture that necessarily features minimum operational complexity. The architecture must also be scalable and evolvable to meet mission needs for the next 25 years. This paper will describe the recommended NASA Data Networking Architecture, present some of the rationale for the recommendations, and will illustrate an application of the architecture to example NASA missions.

Israel, David J.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31816-DND

A net-centric air space system or architecture is a continuously evolving environment in which people, devices, information, and services are interconnected via a communications network. Inherent in this type of domain is an overwhelming amount of various forms of data that can provide decision makers, pilots, and air traffic controllers with superior awareness concerning events and conditions within it. Assisting a flight crew in gathering, organizing, and disseminating these data expeditiously is imperative and calls for implementation of technologies such as Increasingly Autonomous Systems designed to support future flight operations conducted within a net-centric national air space system.

Vincent E Houston↗
DOE OSTI2025

Hybrid Quantum Networks with Discrete Polarizations and Continuous Quadrature Variables

Final Scientific/Technical Report for DOE Project DE-SC0022069. Quantum communication and computation systems have evolved along two largely independent paradigms: discrete-variable (DV) systems that encode information in qubits such as photon polarizations or photon-number states, and continuous-variable (CV) systems that encode information in optical field quadratures. Each approach offers distinct advantages—DVs provide low error rates and compatibility with single-photon platforms, while CVs support deterministic operations and efficient quantum state manipulation. A fundamental challenge for building a scalable Quantum Internet lies in interfacing these two regimes into a unified hybrid architecture that can coherently distribute and process quantum information across heterogeneous quantum nodes. This project aims to develop and demonstrate a hybrid optical quantum network that seamlessly integrates DV and CV systems. Specifically, we investigate a new class of hybrid entanglement between the discrete polarizations of single photons and the continuous quadrature variables of optical cat states, overcoming incompatibilities in existing DV–CV demonstrations. Using this new entanglement resource, the team investigates a multi-node hybrid quantum local area network (Q-LAN) testbed capable of DV–CV entanglement generation, swapping, and distribution across fiber links.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1992

Channels and valley networks

Attention is given to Martian channels and valley networks, since they have become a principal element of evidence to the effect that the Martian atmosphere evolved from an early volatile-rich state to its present condition. The outflow channels are relatively young, later Hesperian or Amazonian in age. They formed by immense outbursts of fluid from subsurface sources. Complexity in outflow-channel morphology was generated by varying amounts of sediment and ice in the aqueous-fluid flow systems. The overall cataclysmic-flood morphology may thus be locally transitional to morphologies generated by ice and debris flowage. Although local areas of valley networks, such as on Alba Patera, formed coevally with outflow channel activity, regionally extensive networks dominate in the heavily cratered terrains. The morphology of many valleys suggests genesis by ground-water sapping; for some valleys, surface runoff may have been more important.

Baker, Victor R.↗
DOE OSTI2022

Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking

This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.

permeable media↗
DOE OSTI2026

Statistical Analysis of Inter-Area Oscillations in the U.S. Eastern Interconnection: A 2017-2023 Perspective

Recent advancements and the accumulation of high-resolution, long-term phasor measurement unit (PMU) data have provided detailed insights into inter-area oscillations in power grids. This study conducts a comprehensive statistical analysis of inter-area oscillations within the United States Eastern Interconnection from 2017 to 2023. Utilizing data captured by the advanced wide-area Frequency Monitoring Network (FNET/GridEye), this investigation examines the occurrence patterns, dominant frequencies, damping ratios, and excitation mechanisms of these oscillations. Our analysis sheds light on the evolving statistical behaviors of inter-area oscillations, offering updated and critical information for grid operators and planners. The insights gained from this study can be instrumental in enhancing the operational resilience of the power network and guiding strategic developments in grid infrastructure to accommodate future challenges. Additionally, the study discusses emerging challenges associated with the modernization of the power grid, including increased renewable penetration, dynamic load variability, and cyber-physical vulnerabilities that complicate oscillation monitoring and control.

Inter-area oscillations↗
NASA NTRS1992

A mean field neural network for hierarchical module placement

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Unaltuna, M. Kemal↗
NASA NTRS1985

Some key considerations in evolving a computer system and software engineering support environment for the space station program

The space station data management system involves networks of computing resources that must work cooperatively and reliably over an indefinite life span. This program requires a long schedule of modular growth and an even longer period of maintenance and operation. The development and operation of space station computing resources will involve a spectrum of systems and software life cycle activities distributed across a variety of hosts, an integration, verification, and validation host with test bed, and distributed targets. The requirement for the early establishment and use of an apporopriate Computer Systems and Software Engineering Support Environment is identified. This environment will support the Research and Development Productivity challenges presented by the space station computing system.

Mckay, C. W.↗