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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimization on Manifolds via Graph Gaussian Processes

This paper integrates manifold learning techniques within a Gaussian process upper confidence bound algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective. Query points are sequentially chosen using the posterior distribution of the surrogate model given all previous queries. We establish regret bounds in terms of the number of queries and the size of the point cloud. Several numerical examples complement the theory and illustrate the performance of our method.

Bayesian optimization↗
DOE OSTI2024

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning

Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

Visible light scatter measurements of the Advanced X-ray Astronomical Facility /AXAF/ mirror samples

NASA is studying the properties of mirror surfaces for X-ray telescopes, the data of which will be used to develop the telescope system for the Advanced X-ray Astronomical Facility. Visible light scatter measurements, using a computer controlled scanner, are made of various mirror samples to determine surface roughness. Total diffuse scatter is calculated using numerical integration techniques and used to estimate the rms surface roughness. The data measurements are then compared with X-ray scatter measurements of the same samples. A summary of the data generated is presented, along with graphs showing changes in scatter on samples before and after cleaning. Results show that very smooth surfaces can be polished on the common substrate materials (from 2 to 10 Angstroms), and nickel appears to give the lowest visible light scatter.

Griner, D. B.↗
NASA NTRS1991

Compiling and editing agricultural strata boundaries with remotely sensed imagery and map attribute data using graphics workstations

The USDA presently uses labor-intensive photographic interpretation procedures to delineate large geographical areas into manageable size sampling units for the estimation of domestic crop and livestock production. Computer software to automate the boundary delineation procedure, called the computer-assisted stratification and sampling (CASS) system, was developed using a Hewlett Packard color-graphics workstation. The CASS procedures display Thematic Mapper (TM) satellite digital imagery on a graphics display workstation as the backdrop for the onscreen delineation of sampling units. USGS Digital Line Graph (DLG) data for roads and waterways are displayed over the TM imagery to aid in identifying potential sample unit boundaries. Initial analysis conducted with three Missouri counties indicated that CASS was six times faster than the manual techniques in delineating sampling units.

Cheng, Thomas D.↗
DOE OSTI2019

Magnetic Properties of Ru-Doped Nd{sub 0.67}Sr{sub 0.33}Mn{sub 1−x}Ru{sub x}O{sub 3} (0 ≤ x ≤ 0.10) Manganites

The structural and magnetic properties of polycrystalline Nd{sub 0.67}Sr{sub 0.33}Mn{sub 1−x}Ru{sub x}O{sub 3} (x = 0.00 and 0.10) samples have been studied with the substitution of Ru at Mn site. The samples have orthorhombic crystal structure determined by room-temperature X-ray diffraction and Rietvield analysis. On Ru doping at Mn site, a change in its symmetry space group is observed. The increase in Ru content significantly affects the Curie temperature (T{sub c}). The double-exchange mechanism is suppressed with Ru doping affecting the Mn{sup 3+}–O{sup 2−}–Mn{sup 4+} ion network. Paramagnetic regime of samples in magnetization-temperature graph is fitted and analyzed with Curie-Weiss law. The magnetization versus applied magnetic field measurements show that the samples exhibit ferromagnetic behavior at lower temperatures and paramagnetic behavior at room temperature with negligible hysteresis loop and low coercive field.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Simulator for enhanced ATAMM multiprocessing

A simulator is introduced which follows the rules of the enhanced Algorithm to Architecture Mapping Model (ATAMM) multiprocessing strategy. ATAMM is a method for embedding an algorithm into a multiprocessing system in addition to providing system performance prediction capabilities. A discussion of the inner workings of the Architecture Design and Assessment System (ADAS) hosted simulator is introduced to illustrate how the simulator follows the ATAMM rule set. A sample 5-node algorithm graph is given as an example to illustrate: (1) entering of a graph into the simulator and how to run the graph using a graph entry tool; (2) how to analyze the data; and (3) a comparison of the simulation results with the predicted results from ATAMM.

Andrews, Asa M.↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1977

Geochemical studies of the White Breccia Boulders at North Ray Crater, Descartes region of the lunar highlands

The samples of the White Breccia Boulders obtained during the Apollo 16 mission and investigated in the reported study include an anorthositic breccia (67415), a dark matrix breccia (67435), a light matrix breccia (67455), and a large clast of dark matrix breccia (67475) taken from the 67455 boulder. The chemical analyses of bulk samples of the samples are listed in a table. A graph shows the lithophile trace element abundances. Another graph indicates the variation of Sm with Al2O3 content for samples from the White Breccia Boulders. The North Ray Crater breccias are found to be in general slightly more aluminous than breccias from the other stations at the Apollo 16 site. Analyses of eight Apollo 16 breccias cited in the literature range from 25% to 35% Al2O3. However, the North Ray Crater breccias are more clearly distinct from the other Apollo 16 breccias in their contents of lithophile trace elements.

Lindstrom, M. M.↗
NASA NTRS1987

A flux-limited sample of Galactic carbon stars

Published observational data (including IRAS observations) for a flux-limited sample of 215 Galactic carbon stars (CSs) selected from the 2-micron sky survey of Neugebauer and Leighton (1969) are compiled in extensive tables and graphs and analyzed statistically. The sample is found to penetrate a volume of radius 1.5 kpc, and the local CS space density and surface density are calculated as log rho0 (per cu kpc) = 2.0 + or - 0.4 and log N (per sq kpc) = 1.6 + or - 0.2, respectively. The total Galactic mass-return rate from these CSs is estimated as 0.013 solar mass/yr, implying a time scale of 0.1-1 Myr for the CS evolutionary phase and a mass of 1.2-1.6 solar mass for the (probably F-type) main-seqence progenitors of CSs.

Claussen, M. J.↗
NASA NTRS1995

Learning In networks

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Buntine, Wray L.↗
NASA NTRS1985

Laboratory simulation of meteoritic noble gases. II - Sorption of xenon on carbon: Etching and heating experiments

The release of trapped Xe from amorphous-C phases of meteorites is simulated experimentally by HNO3 etching of carbon-black and pyrolyzed polyvinylidene chloride samples exposed to Xe-127 for 0.5-240 h at 100-1000 C and then degassed for 9 h or more at the same temperatures, as reported by Wacker et al. (1985). The results are presented in tables and graphs and characterized in detail. Samples exposed at 100-200 C are found to lose most of their Xe after etching to a depth of only about 20 pm, while those exposed at 800-1000 C exhibit a second more tightly bound component extending to a depth of 3 nm, indicative of diffusion of Xe during exposure and resembling planetary Xe. The higher noble-gas concentrations measured in meteorites are attributed to rate-controlled Xe uptake over a long period in the solar nebula.

Zadnik, M. G.↗
NASA NTRS1981

Some statistical problems inherent in measuring precipitation

A formula permitting calculation of the mean-square error of the mean value of a random variable due to periodic sampling is derived and applied to estimating the sampling error for satellite observation of the mean rainfall during the GARP Atlantic Tropical Experiment (GATE). The effects of both spatial resolution and frequency of observation on the sampling error are summarized in graphs. It is found that four observations per day are sufficient to determine the monthly mean rainfall over an area of 2.5 deg square (280km x 280km) to within a standard deviation of 5 percent of its mean value; two samples per day would yield an error with a standard deviation slightly less than 10 percent of the mean. A satellite instrument with less frequent sampling may produce significantly greater error in the estimate of monthly mean rainfall.

Flueck, J. A.↗
DOE OSTI2022

Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

graph synthesis↗
NASA NTRS2004

Effects of Heat Flux, Oxygen Concentration and Glass Fiber Volume Fraction on Pyrolysate Mass Flux from Composite Solids

Experimental work on the effects of heat flux, oxygen concentration and glass fiber volume fraction on pyrolysate mass flux from samples of polypropylene/glass fiber composite (PP/G) is underway. The research is conducted as part of a larger project to develop a test methodology for flammability of materials, particularly composites, in the microgravity and variable oxygen concentration environment of spacecraft and space structures. Samples of PP/G sized at 30x30x10 mm are flush mounted in a flow tunnel, which provides a flow of oxidizer over the surface of the samples at a fixed value of 1 m/s and oxygen concentrations varying between 18 and 30%. Each sample is exposed to a constant external radiant heat flux at a given value, which varies between tests from 10 to 24 kW/m2. Continuous sample mass loss and surface temperature measurements are recorded for each test. Some tests are conducted with an igniter and some are not. In the former case, the research goal is to quantify the critical mass flux at ignition for the various environmental and material conditions described above. The later case generates a wider range of mass flux rates than those seen prior to ignition, providing an opportunity to examine the protective effects of blowing on oxidative pyrolysis and heating of the surface. Graphs of surface temperature and sample mass loss vs. time for samples of 30% PPG at oxygen concentrations of 18 and 21% are presented in the figures below. These figures give a clear indication of the lower pyrolysis rate and extended time to ignition that accompany a lower oxygen concentration. Analysis of the mass flux rate at the time of ignition gives good repeatability but requires further work to provide a clear indication of mass flux trends accompanying changes in environmental and material properties.

Rich, D. B.↗
NASA NTRS2004

Effects of Heat Flux, Oxygen Concentration and Glass Fiber Volume Fraction on Pyrolysate Mass Flux from Composite Solids

Experimental work on the effects of heat flux, oxygen concentration and glass fiber volume fraction on pyrolysate mass flux from samples of polypropylene/glass fiber composite (PP/G) is underway. The research is conducted as part of a larger project to develop a test methodology for flammability of materials, particularly composites, in the microgravity and variable oxygen concentration environment of spacecraft and space structures. Samples of PP/G sized at 30 x 30 x 10 mm are flush mounted in a flow tunnel, which provides a flow of oxidizer over the surface of the samples at a fixed value of 1 m/s and oxygen concentrations varying between 18 and 30%. Each sample is exposed to a constant external radiant heat flux at a given value, which varies between tests from 10 to 24 kW/sq m. Continuous sample mass loss and surface temperature measurements are recorded for each test. Some tests are conducted with an igniter and some are not. In the former case, the research goal is to quantify the critical mass flux at ignition for the various environmental and material conditions described above. The later case generates a wider range of mass flux rates than those seen prior to ignition, providing an opportunity to examine the protective effects of blowing on oxidative pyrolysis and heating of the surface. Graphs of surface temperature and sample mass loss vs. time for samples of 30% PPG at oxygen concentrations of 18 and 21% are presented in the figures below. These figures give a clear indication of the lower pyrolysis rate and extended time to ignition that accompany a lower oxygen concentration. Analysis of the mass flux rate at the time of ignition gives good repeatability but requires further work to provide a clear indication of mass flux trends accompanying changes in environmental and material properties.

Rich, D. B.↗
DOE OSTI2023

Multiscale graph neural network autoencoders for interpretable scientific machine learning

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. Further, this reduction procedure essentially amounts to flowfieldconditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sampling frequency thresholds for the quantum advantage of the quantum approximate optimization algorithm

We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p required for a quantum device to outperform classical algorithms. There is potential for quantum advantage on hundreds of qubits and moderate depth with a sampling frequency of 10 kHz. We observe, however, that classical heuristic solvers are capable of producing high-quality approximate solutions in linear time complexity. In order to match this quality for large graph sizes N, a quantum device must support depth p > 11. Additionally, multi-shot QAOA is not efficient on large graphs, indicating that QAOA p ≤ 11 does not scale with N. These results limit achieving quantum advantage for QAOA MaxCut on 3-regular graphs. Other problems, such as different graphs, weighted MaxCut, and 3-SAT, may be better suited for achieving quantum advantage on near-term quantum devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗