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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Towards a theory of automated elliptic mesh generation

The theory of elliptic mesh generation is reviewed and the fundamental problem of constructing computational space is discussed. It is argued that the construction of computational space is an NP-Complete problem and therefore requires a nonstandard approach for its solution. This leads to the development of graph-theoretic, combinatorial optimization and integer programming algorithms. Methods for the construction of two dimensional computational space are presented.

Cordova, J. Q.↗
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 OSTI2025

Computational insights into hydrogen adsorption energies on medium-entropy oxides

High entropy oxides (HEOs) have emerged as promising catalysts for several important chemical transformations including alkane activation. Hydrogen adsorption energy (HAE) has been used as a key descriptor for many reactions including methane C–H activation and hydrogen evolution reactions. Hence, understanding the relationship between HAEs and the surface chemistry of HEO surfaces could lay the foundation for meaningful correlations among methane C–H activation, HAE, and the complex, local environment of HEO surfaces. Here, we used a medium-entropy oxide as a prototypical system – Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O with a rock-salt structure – to interrogate these relationships. We sampled 2000 different surfaces of its (100) plane and calculated the HAEs at randomly chosen surface O sites using density functional theory (DFT). Our analysis of the 2000 data points reveals that the HAEs at the surface O sites are significantly influenced by the local environment around the adsorption sites, particularly the nature of the metal atom directly below the surface O site where H adsorbs. After comparing several popular graph-neural-network-based machine learning models, we found that the DimeNet++ model performed best achieving satisfactory accuracy in predicting HAEs for both Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O and slightly varied compositions. Our work underscores the promise of such models and the need for further refinement to address the complexity of HEOs.

Song, Haohong [Vanderbilt Univ., Nashville, TN (Un↗
NASA NTRS1997

Sequential Test Strategies for Multiple Fault Isolation

In this paper, we consider the problem of constructing near optimal test sequencing algorithms for diagnosing multiple faults in redundant (fault-tolerant) systems. The computational complexity of solving the optimal multiple-fault isolation problem is super-exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and Lagrangian relaxation, we present several static and dynamic (on-line or interactive) test sequencing algorithms for the multiple fault isolation problem that provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a static diagnostic directed graph (digraph), instead of a static diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. Computational results based on real-world systems indicate that the size of a static multiple fault strategy is strictly related to the structure of the system, and that the use of an on-line multiple fault strategy can diagnose faults in systems with as many as 10,000 failure sources.

Shakeri, M.↗
DOE OSTI2023

Can a deep-learning model make fast predictions of vacancy formation in diverse materials?

The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.

2D materials↗
NASA NTRS1985

The relations between the linear and bilinear atmospheric tidal fields in geometric height and in log-pressure coordinates

Theoretical models of planetary-atmosphere tidal fields are examined analytically, comparing models based on geometric-height coordinates with those employing log-pressure coordinates. The relationship between the linearized meteorological variables in the two systems is explored for classical tidal theory and its reduction to the Laplace tidal equation, and it is shown that identical horizontal and vertical equations are obtained. Also considered are the tidal zonal-mean bilinear flux convergences and their Eliassen-Palm formulations. Numerical results for problems involving the earth and Mars atmospheres are presented in graphs and discussed.

Zurek, Richard W.↗
NASA NTRS1977

Superconductivity in sputtered CuMo6S8

The superconducting transition temperature and the superconducting upper critical fields for sputtered films of copper-containing ternary molybdenum chalcogenides (chevrel phases) were determined. The theory presented by Werthamer, Helfand, and Hohenberg (WHH) (1966) was used to make computer-generated plots of the superconducting upper critical field values versus the superconducting transition temperature values for various values of the Maki paramagnetic pair-breaking parameter and the spin-orbit scattering parameter. The interpretation of the graph suggests that the experimental data for at least one sample are above the maximum WHH theoretical limit in the lower temperature range. Possible explanations of this situation are discussed. In other calculations, a scaling law was found suitable for determining pinning force densities; this data was used to calculate critical current values. The relation between resistivity and temperature was studied.

Alterovitz, S.↗
DOE OSTI2022

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗
NASA NTRS1984

Nonlocal effects on the convective properties of the electrostatic current-driven ion-cyclotron instability

The convective behavior of the current-driven ion-cyclotron instability (CDICI) in the presence of nonlocal magnetic-shear and current-channel-width effects is investigated theoretically using the analytical approach of Bakshi et al. (1983). The results are presented in graphs and discussed. Three different CDICI regimes defined by the ratio of the channel width to the shear length are obtained: a purely nonlocal regime with reduced temporal growth rate and group velocity in the z direction going to zero (ratios greater than about 0.1); a regime corresponding to the results of local theory (ratios less than 0.01); and a regime characterized by decreasing temporal growth rate and by z and y group velocities which become negative when the channel width becomes less than the mean ion Larmor radius (ratios 0.001 or less).

Ganguli, G.↗
DOE OSTI2021

Fishnet four-point integrals: integrable representations and thermodynamic limits

In this work, we consider four-point integrals arising in the planar limit of the conformal “fishnet” theory in four dimensions. They define a two-parameter family of higher-loop Feynman integrals, which extend the series of ladder integrals and were argued, based on integrability and analyticity, to admit matrix-model-like integral and determinantal representations. In this paper, we prove the equivalence of all these representations using exact summation and integration techniques. We then analyze the large-order behaviour, corresponding to the thermodynamic limit of a large fishnet graph. The saddle-point equations are found to match known two-cut singular equations arising in matrix models, enabling us to obtain a concise parametric expression for the free-energy density in terms of complete elliptic integrals. Interestingly, the latter depends non-trivially on the fishnet aspect ratio and differs from a scaling formula due to Zamolodchikov for large periodic fishnets, suggesting a strong sensitivity to the boundary conditions. We also find an intriguing connection between the saddle-point equation and the equation describing the Frolov-Tseytlin spinning string in AdS 3 × S 1 , in a generalized scaling combining the thermodynamic and short-distance limits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Gapped lineon and fracton models on graphs

We introduce a $\mathbb{Z}_N$ stabilizer code that can be defined on any spatial lattice of the form $\Gamma\times C_{L_z}$, where $\Gamma$ is a general graph. We also present the low-energy limit of this stabilizer code as a Euclidean lattice action, which we refer to as the anisotropic $\mathbb{Z}_N$ Laplacian model. It is gapped, robust (i.e., stable under small deformations), and has lineons. Its ground state degeneracy (GSD) is expressed in terms of a "mod $N$-reduction" of the Jacobian group of the graph $\Gamma$. In the special case when space is an $L\times L\times L_z$ cubic lattice, the logarithm of the GSD depends on $L$ in an erratic way and grows no faster than $O(L)$. Furthermore, we also discuss another gapped model, the $\mathbb{Z}_N$ Laplacian model, which can be defined on any graph. It has fractons and a similarly strange GSD.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2022

On the Current State of Sheaf Theoretic Networking

Advancing the Delay Tolerant Networking (DTN) effort has proven to be a unique and difficult challenge. In the journey to implementing space networking, NASA has considered several strategies. From direct translations of Internet Protocol (IP) to satellite networks to pre-planned routing structures in Contact Graph Routing (CGR), each potential solution for DTN has come with its fair share of setbacks and challenges. Frequently, these challenges can be traced back to specific assumptions made in the development of each protocol that do not carry over from ground networks to space networks. To address these fundamental assumptions, a more general and foundational networking theory is required. In this paper, we survey a novel mathematical foundation for networking using the theory of cellular sheaves. Sheaves form a mathematical tool for modeling local phenomenon that leads to global effects. Sheaves have been introduced a number of times, and our goal here is to summarize the applications and point towards important future work that must be done to build a stronger, cohesive theory for networking.

Sheaves↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

Planetary science: A lunar perspective

An interpretative synthesis of current knowledge on the moon and the terrestrial planets is presented, emphasizing the impact of recent lunar research (using Apollo data and samples) on theories of planetary morphology and evolution. Chapters are included on the exploration of the solar system; geology and stratigraphy; meteorite impacts, craters, and multiring basins; planetary surfaces; planetary crusts; basaltic volcanism; planetary interiors; the chemical composition of the planets; the origin and evolution of the moon and planets; and the significance of lunar and planetary exploration. Photographs, drawings, graphs, tables of quantitative data, and a glossary are provided.

Taylor, S. R.↗
NASA NTRS1974

On the theory of H2 rotational excitation

A number of mechanisms for exciting higher-J states in the ground vibrational level are considered. Attention is given to collisions with H atoms and electrons. Other mechanisms involve a cascading down from upper vibrational levels, following absorption and reemission of photons in the Lyman and Werner bands, or a cascading down from upper vibrational levels, following formation of hydrogen molecules in excited vibrational and rotational levels. Theoretical population densities of rotational levels are shown in a graph.

Spitzer, L., Jr.↗
NASA NTRS1985

A remark about pointed bubbles

The polymer expansion is a formal algebraic identity between a partition function and logarithm in statistical physics problems. The expansion gives a systematic method to control the free energy or to establish exponential tree-graph decay of connected correlations. Here, the convergence properties of the polymer expansion are analyzed in connection with three practical examples, including: intersecting bonds in chemical polymer chains; a connected closed hypersurface built from the (d-1)-faces of the d-dimensional unit cubes; and the set of Feynamn diagrams in the perturbation series of the Euclidean field theory partition function Z. The example of connected polymer chains is generalized to apply to other lattice models, including n-state Ising models at high temperature; short range lattice gases at high temperature; and weak coupling lattice field and gauge theories.

Garabedian, P. R.↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗