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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Results from 13-cm absorptivity and H2SO4 abundance profiles from the Season 10 (1986) Pioneer Venus Orbiter radio occultation experiment

Results are reported from 13-cm radio-occultation absorptivity measurements of H2SO4 in the northern-hemisphere atmosphere of Venus, obtained by the Pioneer Venus Orbiter on 23 orbits during late 1986 and early 1987. The theoretical basis of the occultation measurements is explained; the error-analysis procedures are outlined; and the data are presented in tables and graphs. The abundance and distribution of gaseous H2SO4 in the equatorial zone (11-25 deg N) are found to be significantly different from those at latitudes above 36 deg N, and evidence for a reduction in H2SO4 abundance since the 1979 measurements is detected.

Jenkins, Jon M.↗
NASA NTRS2004

Plan-graph Based Heuristics for Conformant Probabilistic Planning

In this paper, we introduce plan-graph based heuristics to solve a variation of the conformant probabilistic planning (CPP) problem. In many real-world problems, it is the case that the sensors are unreliable or take too many resources to provide knowledge about the environment. These domains are better modeled as conformant planning problems. POMDP based techniques are currently the most successful approach for solving CPP but have the limitation of state- space explosion. Recent advances in deterministic and conformant planning have shown that plan-graphs can be used to enhance the performance significantly. We show that this enhancement can also be translated to CPP. We describe our process for developing the plan-graph heuristics and estimating the probability of a partial plan. We compare the performance of our planner PVHPOP when used with different heuristics. We also perform a comparison with a POMDP solver to show over a order of magnitude improvement in performance.

Ramakrishnan, Salesh↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Degree-preserving graph dynamics: a versatile process to construct random networks

Real-world networks evolve over time via the addition or removal of vertices and edges. In current network evolution models, vertex degree varies or grows arbitrarily. A recently introduced degree-preserving network growth (DPG) family of models preserves vertex degree, resulting in structures significantly different from and more diverse than previous models. Despite its degree preserving property, the DPG model is able to replicate the output of several well-known real-world network growth models. Simulations showed that many real-world networks can also be constructed from small seed graphs via the DPG process. Here, we start the development of a rigorous mathematical theory underlying the DPG family of network growth models. We prove that the degree sequence of the output of some of the well-known, real-world network growth models can be reconstructed via the DPG process, using proper parametrization. We also show that the general problem of deciding whether a simple graph can be obtained via the DPG process from a small seed (DPG feasibility) is, however, NP-complete. In conclusion, it is an intriguing open problem to uncover whether there is a structural reason behind the DPG-constructability of real-world networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study

To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.

25 ENERGY STORAGE↗
DOE OSTI2023

Improving materials property predictions for graph neural networks with minimal feature engineering *

Graph neural networks (GNNs) have been employed in materials research to predict physical and functional properties, and have achieved superior performance in several application domains over prior machine learning approaches. Recent studies incorporate features of increasing complexity such as Gaussian radial functions, plane wave functions, and angular terms to augment the neural network models, with the expectation that these features are critical for achieving a high performance. Here, we propose a GNN that adopts edge convolution where hidden edge features evolve during training and extensive attention mechanisms, and operates on simple graphs with atoms as nodes and distances between them as edges. As a result, the same model can be used for very different tasks as no other domain-specific features are used. With a model that uses no feature engineering, we achieve performance comparable with state-of-the-art models with elaborate features for formation energy and band gap prediction with standard benchmarks; we achieve even better performance when the dataset size increases. Although some domain-specific datasets still require hand-crafted features to achieve state-of-the-art results, our selected architecture choices greatly reduce the need for elaborate feature engineering and still maintain predictive power in comparison.

42 ENGINEERING↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

In situ extraction and analysis of volatile elements and molecules from carbonaceous chondrites

A laser microprobe mass spectrometer was used to measure volatiles released, on a scale of 30-50 microns, from freshly broken, sawed, and weathered surfaces in fragments of the Allende, Murchison, Coolidge, Felix, and Orgueil carbonaceous chondrites. Samples were heated to about 120 C under a vacuum of 200 ntorr and illuminated with the focused beam of a Q-switched Nd:glass laser of variable energy output (0.1-1.0 J); the gases released were analyzed using a computer-controlled mass-selective detector. The results are presented in tables and graphs and discussed in detail, with particular attention to aqueous alteration; weathering; thermal metamorphism; the distribution of sulfur-bearing phases; and differences in the amounts of volatiles in matrix, inclusions, and chondrules.

Hartmetz, C. P.↗
NASA NTRS2003

Real-Time Inhibitor Recession Measurements in Two Space Shuttle Reusable Solid Rocket Motors

Real-time internal motor insulation char line recession measurements have been evaluated for two full-scale static tests of the Space Shuttle Reusable Solid Rocket Motor (RSRM). These char line recession measurements were recorded on the forward facing propellant grain inhibitors to better understand the thermal performance of these inhibitors. The RSRM propellant grain inhibitors are designed to erode away during motor operation, thus making it difficult to use post-fire observations to determine inhibitor thermal performance. Therefore, this new internal motor instrumentation is invaluable in establishing an accurate understanding of inhibitor recession versus motor operation time. The data for the first test was presented at the 37th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit (AIAA 2001-3280) in July 2001. Since that time, a second full scale static test has delivered additional real-time data on inhibitor thermal performance. The evaluation of this data is presented in this paper. The second static test, in contrast to the first test, used a slightly different arrangement of instrumentation in the inhibitors. This instrumentation has yielded a better understanding of the inhibitor time dependent inboard tip recession. Graphs of inhibitor recession profiles with time are presented. Inhibitor thermal ablation models have been created from theoretical principals. The model predictions compare favorably with data from both tests. This verified modeling effort is important to support new inhibitor designs for a five segment Space Shuttle solid rocket motor. The internal instrumentation project on RSRM static tests is providing unique opportunities for other real-time internal motor measurements that could not otherwise be directly quantified.

McWhorter, B. B.↗
NASA NTRS2002

Graph Partitioning for Parallel Applications in Heterogeneous Grid Environments

The problem of partitioning irregular graphs and meshes for parallel computations on homogeneous systems has been extensively studied. However, these partitioning schemes fail when the target system architecture exhibits heterogeneity in resource characteristics. With the emergence of technologies such as the Grid, it is imperative to study the partitioning problem taking into consideration the differing capabilities of such distributed heterogeneous systems. In our model, the heterogeneous system consists of processors with varying processing power and an underlying non-uniform communication network. We present in this paper a novel multilevel partitioning scheme for irregular graphs and meshes, that takes into account issues pertinent to Grid computing environments. Our partitioning algorithm, called MiniMax, generates and maps partitions onto a heterogeneous system with the objective of minimizing the maximum execution time of the parallel distributed application. For experimental performance study, we have considered both a realistic mesh problem from NASA as well as synthetic workloads. Simulation results demonstrate that MiniMax generates high quality partitions for various classes of applications targeted for parallel execution in a distributed heterogeneous environment.

Bisws, Rupak↗
NASA NTRS2003

Radial Clearance Found To Play a Key Role in the Performance of Compliant Foil Air Bearings

Compliant foil air bearings are at the forefront of the Oil-Free turbomachinery revolution, which supports gas turbine engines with hydrodynamic bearings that use air instead of oil as the working fluid. These types of bearings have been around for almost 50 years and have found a home in several commercial applications, such as in air cycle machines, turbocompressors, and microturbines, but are now being aggressively pursued for use in small and midrange aircraft gas turbine engines. Benefits include higher operating speeds and temperatures, lower maintenance costs, and greater reliability. The Oil-Free Turbomachinery team at the NASA Glenn Research Center is working to foster the transition of Oil-Free technology into gas turbine engines by performing in-house experiments on foil air bearings in order to gain a greater insight into their complex operating principles. A research program recently undertaken at Glenn focused on the concept of radial clearance and its influence on bearing performance. The tests were conducted on foil bearings with different radial clearances. As defined for a foil bearing, radial clearance is a measure of the small amount of shaft radial motion that is present from play that exists in the elastic support structure, such as between the top and bump foils and the bump foils and bearing shell (see the drawing). With an insufficient amount of radial clearance, the bearing imparts a high preload on the shaft, which when excessive, can reduce the loadcarrying capability of the bearing. On the other hand, systems using foil bearings with excessive radial clearance may experience rotordynamic instabilities because of low bearing preload. Therefore, without a more thorough understanding of radial clearance, it is difficult to accurately predict the performance of a given bearing design. The test program demonstrated that there is a direct correlation between radial clearance and the performance of foil air bearings. As shown in the graph, an optimum radial clearance exists that will maximize the amount of load that the bearing is capable of supporting. With respect to this optimum, two different performance regimes were observed that are a function of the amount of radial clearance. Tests showed that bearings with radial clearances below the optimum in regime I were susceptible to sudden seizure, a failure mode indicative of thermal runaway caused by high preload. The high preload is in response to an insufficient amount of radial clearance available to accommodate the thermal growth of the bearing and shaft. However, radial clearances greater than the optimum in regime II resulted in low bearing preloads that did not cause any heat-related problems, and the failure mode was due to fluid-film breakdown. In fact, bearings operating with radial clearances twice as much as the optimum suffered a decrease in the maximum load capacity of only about 20 percent. Therefore, special attention has to be given to the range of operating conditions expected in the bearing/shaft system since changes in temperature, centrifugal, and hydrodynamic effects can all affect radial clearance. This enhanced understanding of foil air bearing behavior will greatly aid our efforts to transition Oil-Free technology to future aircraft engines.

Radil, Kevin C.↗
NASA NTRS2012

Characterizing Orbital Debris and Spacecrafts Through a Multi-Analytical Approach

Defining the risks present to both crewed and robotic spacecrafts is part of NASA s mission, and is critical to keep these resources out of harms way. Characterizing orbital debris is an essential part of this mission. We present a proof-of-concept study that employs multiple techniques to demonstrate the efficacy of each approach. The targets of this study are IDCSPs (Initial Defense Communications Satellite Program). 35 of these satellites were launched by the US in the mid-1960s and were the first US communications satellites in the GEO regime. They were emplaced in slightly sub-synchronous orbits. These targets were chosen for this proof-of-concept study for the simplicity of their observable exterior surfaces. The satellites are 26-sided polygons (86cm in diameter), initially spin-stabilized and covered on all sides in solar panels. Data presented here include: (a) visible broadband photometry (Johnson B and Cousins R bands) taken with the University of Michigan s 0.6-m aperture Curtis-Schmidt telescope MODEST (for Michigan Orbital DEbris Survey Telescope) in Chile in November, 2011, (b) laboratory broadband photometry (Johnson BV Cousins RI) of solar cells, obtained using the Optical Measurements Center (OMC) at NASA/JSC (see Cowardin et al., this meeting for more details), (c) visible-band spectra taken using the Magellan 6.5m Baade Telescope at Las Campanas Observatory in Chile in March, 2012 (see also Seitzer et al., this meeting), and (d) visible-band laboratory spectra of solar cells using a Field Spectrometer. Color-color plots using broadband photometry (e.g. B-R vs. R-I) demonstrate that different material types fall into distinct areas on the plots (Cowardin, AMOS 2010). Spectra will be binned in wavelength to compare with photometry results and plotted on the same graph for comparison. This allows us to compare lab data with telescopic data, and photometric results with spectroscopic results. In addition, the spectral response of solar cells in the visible wavelength regime varies from relatively flat (modern black solar cells with uniform albedo as a function of wavelength) to older solar cells whose reflectivity is sharply peaked in the blue (similar to the IDCSP solar cells). With a target like IDCSPs, the material type is known a priori. Therefore, this study will also be used to determine whether laboratory spectra of pre-launch (pristine) solar cells differ from the telescopic spectra of IDCSPs that have been exposed to the harsh environment of space for ~45 years to investigate whether space weathering effects are evident.

Lederer, S. M.↗
DOE OSTI2025

Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data

Abstract Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable even for cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search.

Biochemistry & Molecular Biology↗
DOE OSTI2023

A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks

Hyperbolic neural networks have recently gained significant attention due to their promising results on several graph problems including node classification and link prediction. The primary reason for this success is the effectiveness of hyperbolic space in capturing the inherent hierarchy of graph datasets. However, they are limited in terms of generalization, scalability, and have inferior performance when applied to non-hierarchical datasets. In this paper, we take a completely different perspective for modeling hyperbolic networks and answer the following question: is an Euclidean model able to approximate a function or behavior in the hyperbolic space? Extending the universal approximation theory developed for Euclidean models, We draw an analogy from the hyperbolic components to the Euclidean counterparts and conclude that, in order to capture hierarchical features, it is possible to generalize hyperbolic models to be a special case of Euclidean models with the proposed Pseudo-Poincaré technique. We applied our non-linear hyperbolic normalization to the current state-of-the-art homogeneous and multi-relational graph networks and demonstrate significant improvements in performance compared to both Euclidean and hyperbolic counterparts. The primary impact of this work lies in its ability to capture hierarchical features in the Euclidean space, and thus, can replace hyperbolic networks without any loss in performance metrics while simultaneously leveraging the power of Euclidean networks such as interpretability and efficient execution of various model components.

Khatir, Mehrdad↗
DOE OSTI2020

Parallel algorithms for finding connected components using linear algebra

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. Here, we demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Defect diffusion graph neural networks (d2gnn)

SAND2025-01004O Defect Diffusion Graph Neural Networks (d2gnn) is a software tool that assists in the discovery of new materials for high-temperature, clean-energy applications. It uses advanced graph neural networks to model the relationship between material structures and their defect properties. The application helps predict how materials will behave under different conditions and accelerates the development of innovative materials. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Witman, Matthew [Sandia National Lab. (SNL-CA), Li↗