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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
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

Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. Here this tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1987

Wolf-Rayet stars

The properties and evolutionary status of WR stars are examined, reviewing the results of recent observational and theoretical investigations. Topics discussed include spectral types and line strengths, magnitudes and colors, intrinsic variability, IR and radio observations, X-ray observations, the Galactic distribution of WR stars, WR stars in other galaxies, and WR binaries. Consideration is given to the inferred masses, composition, and stellar winds of WR stars; model atmospheres; WR stars and the Galactic environment; and WR stars as a phase of stellar evolution. Diagrams, graphs, and tables of numerical data are provided.

Abbott, David C.↗
NASA NTRS1983

Radar investigation of asteroids

For 80 Sappho, 356 Liguria, 694 Ekard, and 2340 Hathor, data were taken simultaneously in the same sense of circular polarization as transmitted (SC) as well as in the opposite (OC) sense. Graphs show the average OC and SC radar echo power spectra soothed to a resolution of EFB Hz and plotted against Doppler frequency. Radar observations of the peculiar object 2201 Oljato reveal an unusual set of echo power spectra. The albedo and polarization ratio remain fairly constant but the bandwidths range from approximately 0.8 Hz to 1.4 Hz and the spectral shapes vary dramatically. Echo characteristics within any one date's approximately 2.5-hr observation period do not fluctuate very much. Laboratory measurements of the radar frequency electrical properties of particulate metal-plus-silicate mixtures can be combined with radar albedo estimates to constrain the bulk density and metal weight, fraction in a hypothetical asteroid regolith having the same particle size distribution as lab samples.

Ostro, S. J.↗
DOE OSTI2024

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ensuring thermodynamic consistency with invertible coarse-graining

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models. Designing “good” coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real-space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We illustrate that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Network analysis of memristive device circuits: dynamics, stability and correlations

Abstract Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanoscale conductivity to providing insight into the information-processing of neurons. The resistance in a memristive device depends on the history of the applied bias and thus displays a type of memory. The interplay of this memory with the dynamic properties of the network can give rise to new behavior, offering many fascinating theoretical challenges. But methods to analyze general memristive circuits are not well described in the literature. In this paper we develop a general circuit analysis for networks that combine memristive devices alongside resistors, capacitors and inductors and under various types of control. We derive equations of motion for the memory parameters of these circuits and describe the conditions for which a network should display properties characteristic of a resonator system. For the case of a purely memresistive network, we derive Lyapunov functions, which can be used to study the stability of the network dynamics. Surprisingly, analysis of the Lyapunov functions show that these circuits do not always have a stable equilibrium in the case of nonlinear resistance and window functions. The Lyapunov function allows us to study circuit invariances, wherein different circuits give rise to similar equations of motion, which manifest through a gauge freedom and node permutations. Finally, we identify the relation between the graph Laplacian and the operators governing the dynamics of memristor networks operators, and we use these tools to study the correlations between distant memristive devices through the effective resistance.

97 MATHEMATICS AND COMPUTING↗
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.↗
DOE OSTI2025

AI-powered exploration of molecular vibrations, phonons, and spectroscopy

The vibrational dynamics of molecules and solids play a critical role in defining material properties, particularly their thermal behaviors. However, theoretical calculations of these dynamics are often computationally intensive, while experimental approaches can be technically complex and resource-demanding. Recent advancements in data-driven artificial intelligence (AI) methodologies have substantially enhanced the efficiency of these studies. This review explores the latest progress in AI-driven methods for investigating atomic vibrations, emphasizing their role in accelerating computations and enabling rapid predictions of lattice dynamics, phonon behaviors, molecular dynamics, and vibrational spectra. Key developments are discussed, including advancements in databases, structural representations, machine-learning interatomic potentials, graph neural networks, and other emerging approaches. Compared to traditional techniques, AI methods exhibit transformative potential, dramatically improving the efficiency and scope of research in materials science. The review concludes by highlighting the promising future of AI-driven innovations in the study of atomic vibrations.

Han, Bowen [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2018

Red Emitting Monoazo Disperse Dyes with Phenyl(1H-benzoimidazol-5-yl) Methanone as Inbuilt Photostabilizing Unit: Synthesis, Spectroscopic, Dyeing and DFT Studies

Synthesis of three novel phenyl(1H-benzoimidazol-5-yl)methanone based fluorescent monoazo disperse dyes and their characterization by spectroscopic methods ({sup 1}H NMR, {sup 13}C NMR, IR and MS) are presented. Insertion of phenyl(1H-benzoimidazol-5-yl)methanone moiety bring about induced fluorescence properties and enhanced photostability as compared to the previously reported analogues (CI Solvent Yellow 14, 4-diethylamino-2-hydroxy-1-diazobenzene and 7-(diethylamino)-4-hydroxy-3-(phenyldiazenyl)-2H-chromen-2-one). Synthesized phenyl(1H-benzoimidazol-5-yl)methanone based dyes exhibited red-shifted absorption maxima (497–516 nm), high molar extinction coefficients and are emitting in the far-red region (565–627 nm). Moreover, naphthalene-comprising dyes showed negative solvatochromism while N,N-diethylamine comprising dyes showed positive solvatochromism and are in good agreement with solvent polarity graphs and the computed energy levels of highest occupied and lowest unoccupied molecular orbitals. Synthesised dyes have better photostability (light fastness) and sublimation fastness on dyed polyester and nylon compared to reported analogues. DFT calculated energies, electrophilicity index and Frontier Molecular Orbitals (FMO’s) enabled to evaluate the stabilities of azo and hydrazone forms of the dyes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity

Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.

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

Picture gallery - A structured presentation of OAO-2 photometric data supported by OAO-2 spectrophotometric data and UBV, ANS and TD1 observations

Graphs are presented for the stellar fluxes of 531 stars in the 5500-1330 A wavelength range, which have been divided into 52 categories on the basis of spectral types. The merging of medium band interference filter photometry, UBV photometry, ANS photometry and TD1 fluxes, as well as the ordering of the objects, should prove helpful in studies of interstellar reddening, luminosity effects, bandwidth effects, and comparisons with model stellar atmospheres. The agreement between the various UV photometric systems for early-type stars is generally better than 0.10 mag. A list of stars whose photometric properties indicate stellar or interstellar anomalies is also provided.

Koornneef, J.↗
DOE OSTI2021

Loop amplitudes monodromy relations and color-kinematics duality

Color-kinematics duality is a remarkable conjectured property of gauge theory which, together with double copy, is at the heart of a wealth of new developments in scattering amplitudes. So far, its validity has been verified in most cases only empirically, with limited ab initio understanding beyond tree-level. In this paper we provide initial steps in a first-principle understanding of color-kinematics duality and double-copy at loop level, through a detailed analysis of the field-theory limit of the monodromy relations of string theory at one loop. In this limit, we dissect the type of Feynman graphs generated and the relations they obey. We find that graphs with contact-terms are unavoidable and are generated in the field theory limit of “bulk” contours which do not have a standard physical interpretation in string perturbation theory. We show how they are related to ambiguities in the definition of the loop momentum and that their role is precisely to cancel those ambiguities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗