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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Recent progress in the development and understanding of SUPG methods with special reference to the compressible Euler and Navier-Stokes equations

The current status of streamline-upwind/Petrov-Galerkin (SUPG) methods for the analysis of flow problems is surveyed in an analytical review. Problem areas addressed include classical Galerkin, upwind, artificial-diffusion, SUPG, discontinuous Galerkin, space-time FEM, and discontinuity-capturing approaches to the scalar advection-diffusion equation; incompressible flows; advective-diffusive systems; and the compressible Euler and Navier-Stokes equations. Graphs and diagrams are provided, and the good stability properties of state-of-the-art SUPG methods are pointed out.

Hughes, Thomas J. R.↗
NASA NTRS2014

Rate-Compatible Protograph LDPC Codes

Digital communication coding methods resulting in rate-compatible low density parity-check (LDPC) codes built from protographs. Described digital coding methods start with a desired code rate and a selection of the numbers of variable nodes and check nodes to be used in the protograph. Constraints are set to satisfy a linear minimum distance growth property for the protograph. All possible edges in the graph are searched for the minimum iterative decoding threshold and the protograph with the lowest iterative decoding threshold is selected. Protographs designed in this manner are used in decode and forward relay channels.

Nguyen, Thuy V.↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2019

Benchmark Analysis on Magnetic and Photoluminescence Properties of Selective Metal Ions Doped ZnS Nanoparticles

The creation of diluted magnetic semiconductors (DMSs) at lower dimensions that exhibit room temperature ferromagnetism (RTFM) has been given immense significance for the fabrication of a new class of spintronic devices through utilizing spin degrees of freedom besides charge nature of electrons. In this view, nanocrystals of ZnS doped with 4% concentration of Fe, Co, Ni, Cr, Mn, Sr, Cu, and Ce have been synthesized at room temperature (RT) by chemical co-precipitation method. The samples were examined by various characterization techniques like energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and photoluminescence (PL). EDS spectra revealed the existence of parent elements in the prepared samples. XRD characterization disclosed hexagonal structures of nanocrystals, and no secondary phases were observed in all the samples except for Cu and Ni doped ZnS. FTIR data confirmed the proper substitution of dopants in the host lattice as all the graphs have almost same stretchings. The magnetic nature of each sample was evaluated from M-H graphs of VSM, and it corroborated the transition of magnetic properties due to doping. Pristine ZnS showed diamagnetic nature, while Fe and Sr doped ZnS evinced strong ferromagnetic and anti-ferromagnetic properties, respectively. Photoluminescence studies on Ce, Sr and Mn doped ZnS samples revealed strong emission peaks with enhanced luminescence properties compared to bare ZnS. Cu, Cr and Mn doped ZnS exhibited a red shift in emission wavelengths, whereas Co doped ZnS showed emission peak shifted towards the blue region as compared to the host lattice.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Multilevel graph embedding

The goal of the present paper is the design of embeddings of a general sparse graph into a set of points in $\mathbb{R}^d$ for appropriate d ≥ 2. The embeddings that we are looking at here aim to keep vertices that are grouped in communities together and keep the rest apart. To achieve this property, we utilize coarsening that respects possible community structures of the given graph. We employ a hierarchical multilevel coarsening approach that identifies communities (strongly connected groups of vertices) at every level. The multilevel strategy allows any given (presumably expensive) graph embedding algorithm to be made into a more scalable (and faster) algorithm. We demonstrate the presented approach on a number of given embedding algorithms and large-scale graphs and achieve speed-up over the methods in a recent paper.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Effects of Various Wavelength Ranges of Vacuum Ultraviolet Radiation on Teflon FEP Film Investigated

Teflon Fluorinated Ethylene Propylene (FTP) films (DuPont) have been widely used for spacecraft thermal control and have been observed to become embrittled and cracked upon exposure to the space environment. This degradation has been attributed to a synergistic combination of radiation and thermal effects. A research study was undertaken at the NASA Glenn Research Center to examine the effects of different wavelength ranges of vacuum ultraviolet (VUV) radiation on the degradation of the mechanical properties of FEP. This will contribute to an overall understanding of space radiation effects on Teflon FEP, and will provide information necessary to determine appropriate techniques for using laboratory tests to estimate space VUV degradation. Research was conducted using inhouse facilities at Glenn and was carried out, in part, through a grant with the Cleveland State University. Samples of Teflon FEP film of 50.8 microns thickness were exposed to radiation from a VUV lamp from beneath different cover windows to provide different exposure wavelength ranges: MgF2 (115 to 400 nm), crystalline quartz (140 to 400 nm), and fused silica (FS, 155 to 400 nm). Following exposure, FEP film specimens were tensile tested to determine the ultimate tensile strength and elongation at failure as a function of the exposure duration for each wavelength range. The graphs show the effect of ultraviolet exposure on the mechanical properties of the FEP samples.

Dever, Joyce A.↗
DOE OSTI2026

Cluster bootstrap for cosmological correlators

We show that cosmological wavefunction coefficients associated with n-site chain and loop graphs for a cubic scalar theory in de Sitter spacetime have symbol alphabets given by subsets of A 2n−2 and B 2n−1 cluster variables, respectively, and satisfy the associated cluster adjacency properties. The key step in proving this is identifying a precise connection between graph “tubings” that appear in the kinematic flow equation and polygon “triangulations” that encode the combinatorics of cluster compatibility. Our results imply that cosmological wavefunction coefficients in a general power-law FRW cosmology satisfy cluster adjacency to all orders in the ϵ expansion around the de Sitter limit. We use this information as bootstrap input to show that de Sitter symbols for n ≤ 4 are uniquely determined by simple physical constraints.

differential and algebraic geometry↗
DOE OSTI2017

Graph theory data for topological quantum chemistry

Topological phases of noninteracting particles are distinguished by the global properties of their band structure and eigenfunctions in momentum space. On the other hand, group theory as conventionally applied to solid-state physics focuses only on properties that are local (at high-symmetry points, lines, and planes) in the Brillouin zone. To bridge this gap, we have previously [Bradlyn et al., Nature (London) 547, 298 (2017)] mapped the problem of constructing global band structures out of local data to a graph construction problem. In this paper, we provide the explicit data and formulate the necessary algorithms to produce all topologically distinct graphs. Furthermore, we show how to apply these algorithms to certain “elementary” band structures highlighted in the aforementioned reference, and thus we identified and tabulated all orbital types and lattices that can give rise to topologically disconnected band structures. Finally, we show how to use the newly developed bandrep program on the Bilbao Crystallographic Server to access the results of our computation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Bounding entanglement entropy with Clifford double cosets

Following on our previous work studying the orbits of quantum states under Clifford circuits via reachability graphs, we introduce contracted graphs whose vertices represent classes of quantum states with the same entropy vector. These contracted graphs represent the double cosets of the Clifford group, where the left cosets are built from the stabilizer subgroup of the starting state and the right cosets are built from the entropy-preserving operators. We study contracted graphs for stabilizer states, as well as 𝑊 states and Dicke states, discussing how the diameter of a state's contracted graph constrains the entropic diversity of its two-qubit Clifford orbit. We derive an upper bound on the number of entropy vectors that can be generated using any 𝑛-qubit Clifford circuit, for any quantum state. Here, we speculate on the holographic implications for the relative proximity of gravitational duals of states within the same Clifford orbit. Although we concentrate on how entropy evolves under the Clifford group, our double-coset formalism, and thus the contracted graph picture, is extendable to generic gate sets and generic state properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1980

Testing of the permanent magnet material Mn-Al-C for potential use in propulsion motors for electric vehicles

The development of Mn-Al-C permanent magnets is reviewed. The general properties of the material are discussed and put into perspective relative to alnicos and ferrites. The commercial material now available is described by the manufacturer's data. The traction motor designer's demands of a permanent magnet for potential use in electric vehicle drives are reviewed. From this, a list of the needed specific information is extracted. A plan for experimental work is made which would generate this information, or verify data supplied by the producer. The results of these measurements are presented in the form of tables and graphs. The tests determined magnetic design data and some mechanical strength properties. Easy axis hysteresis and demagnetization curves, recoil loops and other minor loop fields were measured over a temperature range from -50 C to +150 C. Hysteresis loops were also measured for three orthogonal directions (the easy and 2 hard axes of magnetization).

Abdelnour, Z. A.↗
NASA NTRS2020

Model Rotor Hover Performance at Low Reynolds Number

Hover performance data from four key experiments has been analyzed in detail to shed some light on model rotor hover performance at low Reynolds number. Each experiment used the simplest blade geometry. The blades were constant chord and untwisted. Three experiments used blades with the NACA 0012 airfoil from root to tip. The NACA 0015 was used in the earliest test. The four experiments provide data spanning a Reynolds number range of 136,500 to 548,700. The specific objective of this report is to ask and answer two questions: 1. Does blade aspect ratio influence hover performance or is rotor solidity the fundamental rotor geometry parameter for practical engineering purposes? ANSWER: Rotor solidity is the fundamental rotor geometry parameter for practical engineering purposes. Any effect of blade aspect ratio appears to be such a secondary variable that its effect lies within the range of experimental error. 2. Is Reynolds number a significant factor in scaling up hover performance to full-scale rotor performance? The answer is twofold. ANSWER: (a) Reynolds number effects on the increase of power with thrust do not appear to be a significant factor for practical engineering purposes, and (b) Reynolds number effects on minimum profile power at or very near zero rotor thrust could not be clearly established primarily because the low torque levels could not be accurately measured with the test equipment used. This has led to significant data scatter. A number of other observations can be made based on the analysis provided herein. For instance: 1. The test matrices used in the four key references contained far too few data points. A collective pitch variation of four or five data points is insufficient to establish experimental accuracy and data repeatability. 2. A common property of the power-versus-thrust (raised to the 3/2 exponent) graphs was that this curve was linear below the onset of blade stall. 3. The blade-to-blade interference at or near zero thrust may, in fact, be creating a turbulent flow field such that the effective Reynolds number at a blade element is considerably greater than what theories using two-dimensional (2D) airfoil properties at a blade element would calculate. 4. Definitive experiments answering the two key questions have yet to be made.

Model Rotor Hover Performance↗
DOE OSTI2017

High-Performance Data Analytics Beyond the Relational and Graph Data Models with GEMS

Graphs represent an increasingly popular data model for data-analytics, since they can naturally represent relationships and interactions between entities. Relational databases and their pure table-based data model are not well suitable to store and process sparse data. Consequently, graph databases have gained interest in the last few years and the Resource Description Framework (RDF) became the standard data model for graph data. Nevertheless, while RDF is well suited to analyze the relationships between the entities, it is not efficient in representing their attributes and properties. In this work we propose the adoption of a new hybrid data model, based on attributed graphs, that aims at overcoming the limitations of the pure relational and graph data models. We present how we have re-designed the GEMS data-analytics framework to fully take advantage of the proposed hybrid data model. To improve analysts productivity, in addition to a C++ API for applications development, we adopt GraQL as input query language. We validate our approach implementing a set of queries on net-flow data and we compare our framework performance against Neo4j. Experimental results show significant performance improvement over Neo4j, up to several orders of magnitude when increasing the size of the input data.

Data Analytics↗
DOE OSTI2024

Probabilistic Context Neighborhood model for lattices

Here we present the Probabilistic Context Neighborhood model designed for two-dimensional lattices as a variation of a Markov random field assuming discrete values. In this model, the neighborhood structure has a fixed geometry but a variable order, depending on the neighbors’ values. Our model extends the Probabilistic Context Tree model, originally applicable to one-dimensional space. It retains advantageous properties, such as representing the dependence neighborhood structure as a graph in a tree format, facilitating an understanding of model complexity. Furthermore, we adapt the algorithm used to estimate the Probabilistic Context Tree to estimate the parameters of the proposed model. We illustrate the accuracy of our estimation methodology through simulation studies. Additionally, we apply the Probabilistic Context Neighborhood model to spatial real-world data, showcasing its practical utility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting Large‐Scale Systematic Missing Pipe Attributes in Water Distribution Networks

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Poff, Jason W. [Oregon State Univ., Corvallis, OR ↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗