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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The compositional structure and the effects of exothermicity in an unpremixed planar jet flame

Results are presented of direct numerical simulations (DNS) of a randomly perturbed compressible, spatially developing planar jet under the influence of a finite rate Arrhenius chemical reaction of the type F + O yields Product + Heat. The objectives of the simulations are to assess the compositional structure of the flame and to determine the influence of reaction exothermicity by means of statistical sampling of the data generated by DNS. It is shown that even with this idealized kinetics model, the simulated results exhibit features in accordance with experimental data. These results indicate that the Damkoehler number is an important parameter in determining the statistical composition of the reacting field, and that the results are insensitive to the mechanism by which this parameter is varied. It is demonstrated that as the intensity of mixing is increased and the effect of finite rate chemistry is more pronounced, the magnitudes of the ensemble mean and rms of the product mass fraction decrease, and those of the reactants mass fraction increase. Also, at higher mixing rates the joint probability density functions of the reactants' mass fractions shift towards higher values within the composition domain indicating a lower reactedness. These trends are consistent with those observed experimentally and are very useful in portraying the statistical structure of non-equilibrium diffusion flames. The DNS generated data are also utilized to examine the applicability of the 'laminar diffusion flamelet model' in predicting the rate of the reactant conversion with finite rate chemistry. This examination indicates that the bounds of the product formation scale reasonably well with those obtained by the flamelet model. However, in the range of the relatively low values of the Damkoehler numbers considered, the scatter of the results is substantially more than that to be completely determined by the closure.

Steinberger, C. J.↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Grain boundary plane distributions in a cold rolled and annealed high purity iron

High purity (99.99%) iron samples were cold rolled with a thickness reduction of 87% followed by a annealing at 620 °C for 15 and 45 min, then the grain boundary plane distributions were characterized by electron backscatter diffraction and a stereology and statistics based five parameter analysis. The results showed that the grain boundary planes were mainly distributed on (1 0 0) and such distribution was enhanced obviously when the holding time of annealing increased from 15 to 45 min during which an extensive grain growth and a dramatic texture evolution took place. Further analysis indicated such distribution was largely due to the rotation of grain boundary planes of the [1 0 0] misoriented grains. Additionally, it was also observed that some random boundaries of [1 0 0] misoriented grains were changing from asymmetrical tilt into (1 0 0)/(− 1 0 0) twist when annealing proceeded from 15 to 45 min. Based on the present work and the results reported by other investigators, it was suggested that the chemistry appeared to be critical in determining the grain boundary plane distribution in iron based materials. - Highlights: •Grain boundary (GB) plane distribution (GBPD) in a high purity (99.99%) was studied. •GB planes are mainly distributed on (1 0 0) in the cold rolled and annealed samples. •Such distribution is largely due to the rotation of GB planes of [100] misoriented grains. •Some [1 0 0] asymmetrical tilt boundaries are evolving into twist ones during annealing.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Simultaneous Improvement of Resolving Power and Signal-to-Noise Ratio Using a Modified Hadamard Transform-Inverse Ion Mobility Spectrometry Technique

In order to improve the resolving power (RP) and signal-to-noise ratio (SNR) of ion mobility spectrometry (IMS) simultaneously, a modified Hadamard transform-inverse ion mobility spectrometry (MHT-IIMS) technique was developed. In this novel technique, a series of isolating codes were appended to each element of the pseudo random binary sequence (PRBS), and then the modified modulation sequence was formed and used to control the ion gate of the inverse IMS (IIMS). Experimental results demonstrate that the MHT-IIMS technique can significantly enhance the resolving power and SNR simultaneously by measuring the spectra of reaction ions. Furthermore, the gas sample CCl{sub 4} and CHCl{sub 3} were measured for evaluating the capability of detecting those samples which have single and multiple product ions. The results show that this novel technique is able to simultaneously improve the resolving power and SNR notablely for the real sample detection without any significant instrumental changes. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Assessing the thermal maturity of black shales using vitrinite reflectance: Insights from Devonian black shales in the eastern United States

Thermal maturity of source rocks indicated by vitrinite reflectance (R o ) is an important and reliable parameter to determine the petroleum potential of sedimentary basins. Originally, R o was used to characterize the degree of coalification of coals and thus works well for coal seams, but it has limitations when used to determine the thermal maturity of black shale successions. A suite of six coal-shale pairs from the Upper Devonian black shale formations in the eastern United States (New Albany Shale, Ohio Shale, Dunkirk Shale, and Rhinestreet Shale) was selected to study the applicability of R o in assessing the thermal maturity of black shale successions. The results show that vitrinite in the studied coal samples is dominated by collotelinite, whereas vitrinite in black shales occurs as small dispersed particles (~5 μm) in the mineral matrix. When comparing the size and morphology of dispersed vitrinite particles in shales and collotelinite fragments in coals, vitrinite in shales and adjacent coals should have the same origin. The measured mean random R o of vitrinite in coals ranges from 0.51–0.68%, and is 0.04–0.11% (average 0.07%) lower than that of dispersed vitrinite particles in enclosing shales. This observation contrasts with previously reported R o suppression in liptinite-rich black shales. A high standard deviation of R o measurements indicates a highly heterogeneous nature of vitrinite in black shales. Three mechanisms can be envisioned to contribute to the higher R o of dispersed vitrinite particles in black shales relative to enclosed coals. First, small vitrinite particles may become more oxidized because small particles are more frequently suspended during transport to the site of deposition and reworking of bottom sediments after deposition than large pieces of driftwood that will turn into enclosed coal lenses during burial diagenesis. Second, misidentification of zooclast (e.g., chitinozoan) fragments as vitrinite due to loss of diagnostic morphology. Third, various macerals in the vitrinite group (e.g., corpogelinite and collotelinite) may have had different original reflectance values. Based on the petrographic characteristics of vitrinite and vitrinite-like particles, caution should be applied when using the R o values of dispersed vitrinite and vitrinite-like particles in black shales as indicators of thermal maturity.

58 GEOSCIENCES↗
DOE OSTI2025

Travelling wave‐based fault detection and location in a real low‐voltage DC microgrid

Abstract This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.

Paruthiyil, Sajay Krishnan [Department of Electric↗
DOE OSTI2016

Intrinsic alignments in redMaPPer clusters – I. Central galaxy alignments and angular segregation of satellites

The shapes of cluster central galaxies are not randomly oriented, but rather exhibit coherent alignments with the shapes of their parent clusters as well as with the surrounding large-scale structures. In this work, we aim to identify the galaxy and cluster quantities that most strongly predict the central galaxy alignment phenomenon among a large parameter space with a sample of 8237 clusters and 94 817 members within 0.1 < z < 0.35, based on the red-sequence Matched-filter Probabilistic Percolation cluster catalogue constructed from the Sloan Digital Sky Survey. We first quantify the alignment between the projected central galaxy shapes and the distribution of member satellites, to understand what central galaxy and cluster properties most strongly correlate with these alignments. Next, we investigate the angular segregation of satellites with respect to their central galaxy major axis directions, to identify the satellite properties that most strongly predict their angular segregation. We find that central galaxies are more aligned with their member galaxy distributions in clusters that are more elongated and have higher richness, and for central galaxies with larger physical size, higher luminosity and centring probability, and redder colour. Satellites with redder colour, higher luminosity, located closer to the central galaxy, and with smaller ellipticity show a stronger angular segregation towards their central galaxy major axes. Lastly, we provide physical explanations for some of the identified correlations, and discuss the connection to theories of central galaxy alignments, the impact of primordial alignments with tidal fields, and the importance of anisotropic accretion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Stochastic quantum Krylov protocol with double-factorized Hamiltonians

Here we propose a class of randomized quantum Krylov diagonalization (rQKD) algorithms capable of solving the eigenstate estimation problem with modest quantum resource requirements. Compared to previous real-time evolution quantum Krylov subspace methods, our approach expresses the time evolution operator e –i$\widehat{H}$$\tau$ as a linear combination of unitaries and subsequently uses a stochastic sampling procedure to reduce circuit depth requirements. While our methodology applies to any Hamiltonian with fast-forwardable subcomponents, we focus on its application to the explicitly double-factorized electronic-structure Hamiltonian. To demonstrate the potential of the proposed rQKD algorithm on near-term quantum devices, we provide numerical benchmarks for a variety of molecular systems with circuit-based state-vector simulators including the effects of sampling noise, achieving ground-state energy errors of less than 1 kcal mol -1 with circuit depths orders of magnitude shallower than those required for low-rank deterministic Trotter-Suzuki decompositions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Nonsuperconducting electronic ground state in pressurized BaFe 2 S 3 and BaFe 2 S 2.5 Se 0.5

In this work, we report a comprehensive study of the spin ladder compound BaFe 2 S 2.5 Se 0.5 using neutron diffraction, inelastic neutron scattering, high pressure synchrotron diffraction, and high pressure transport techniques. We find that BaFe 2 S 2.5 Se 0.5 possesses the same C m c m structure and stripe antiferromagnetic order as does BaFe 2 S 3 , but with a reduced Néel temperature of T N = 98 K compared to 120 K for the undoped system, and a slightly increased ordered moment of 1.40 μ B per iron. Additionally, the low-energy spin excitations in BaFe 2 S 2.5 Se 0.5 are likewise similar to those observed in BaFe 2 S 3 . However, unlike the reports of superconductivity in BaFe 2 S 3 below T c ~ 14 K under pressures of 10 GPa or more, we observe no superconductivity in BaFe 2 S 2.5 Se 0.5 at any pressure up to 19.7 GPa. In contrast, the resistivity exhibits an upturn at low temperature under pressure. Furthermore, we show that additional high-quality samples of BaFe 2 S 3 synthesized for this study likewise fail to become superconducting under pressure, instead displaying a similar upturn in resistivity at low temperature. These results demonstrate that microscopic, sample-specific details play an important role in determining the ultimate electronic ground state in this spin ladder system. We suggest that the upturn in resistivity at low temperature in both BaFe 2 S 3 and BaFe 2 S 2.5 Se 0.5 may result from Anderson localization induced by S vacancies and random Se substitutions, enhanced by the quasi-one-dimensional ladder structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

A study of the effect of random dopant-concentration fluctuations on current in semiconductor superlattices

The influence exerted by random dopant-concentration fluctuations on the current–voltage characteristics of the current flowing through a semiconductor superlattice has been studied. It was shown that the characteristics of the current flowing through the superlattice noticeably vary with the amplitude of fluctuations of nanostructure parameters. It was possible to find for a small sample the probability-density distribution of the integrated absolute values of the difference of currents at various amplitudes of the dopantconcentration fluctuations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Impact of the SSO-ASTRO Margin Guideline on Rates of Re-excision After Lumpectomy for Breast Cancer: A Meta-analysis

Background: One in five patients undergoing initial lumpectomy for invasive breast cancer subsequently undergoes re-excision or mastectomy. A lack of clarity of when to re-excise based on lumpectomy margin width contributes to this high rate of reoperation. We sought to determine the impact of the Society of Surgical Oncology (SSO) and American Society of Radiation Oncologist (ASTRO) margin guideline on reoperation rates after lumpectomy. The guideline recommended omission of routine re-excision in specimens with “no ink on tumor”. Methods: A systematic literature review was performed. For eligible studies, a random-effects model was used for a meta-analysis of lumpectomy re-excision prevalence before and after publication of the SSO-ASTRO margin guideline. Study heterogeneity was measured by the Cochran’s Q test. Results: Five institutional, one population-based, and one national registry study met inclusion requirements. Sample size per study ranged from 237 to 26,102. There was significant interstudy heterogeneity (Q = 19.779; p = 0.003). Pooled re-excision prevalence was 22% (confidence interval [CI] 20–23) before and 14% (CI 12–15) after guideline publication. With the pre-guideline re-excision prevalence used as the reference value, the associated odds ratio for re-excision after the guideline was 0.65 (CI 0.54–0.78; p < 0.0001). Conclusions: The findings of a 35% reduction in the odds of re-excision after the guideline publication and a reduction in re-excision prevalence from 22 to 14% supports the notion that the SSO-ASTRO margin guideline was impactful. These findings are congruent with the projected reductions in re-excision at the time of guideline publication.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Estimating the probability of failure when testing reveals no failures

Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution, and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.

Miller, Keith W.↗
NASA NTRS1993

The compositional structure and the effects of exothermicity in a nonpremixed planar jet flame

Results are presented of direct numerical simulation (DNS) of a randomly perturbed compressible, spatially developing two-dimensional planar jet under the influence of a finite rate chemical reaction of the type F + O yields product. The objectives of the simulations are to assess the compositional structure of the flame and to determine the influence of reaction exothermicity by means of statistical sampling of the DNS generated data. It is shown that even with this idealized kinetics model the simulated results exhibit features in accord with experimental data. These results indicate that the Damkohler number is an important parameter in determining the statistical composition of the reacting field and that the results are not very sensitive to the mechanism by which this parameter is varied. It is demonstrated that as the intensity of mixing is increased and the effect of finite rate chemistry is more pronounced, the magnitudes of the ensemble mean and variance of the product mass fraction decrease and those of the reactants' mass fraction increase. Also, at higher mixing rates the joint probability density functions of the reactants' mass fractions shift towards higher values within the composition domain, indicating a lower reactedness.

Steinberger, C. J.↗
NASA NTRS2003

A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

Globus, Al↗
DOE OSTI2025

Global Evaluation of Process Conditions and Wave Modes in a Rotating Detonation Engine

Rotating Detonation Engines (RDEs) show significant promise for enhancing the efficiency of gas turbine engines while maintaining low 𝑁𝑂𝑥 emissions. This work investigates the predictability of wave modes in a water-cooled RDE under varying operational conditions. Experimental data comprising over 6,700 samples was collected, including parameters such as flow rates, temperatures, pressures, and equivalence ratios. A machine learning approach using the XGBoost library was used to build a multi-class classifier, predicting wave modes based on these inputs. The model achieved a high accuracy of 97%, demonstrating that wave modes are not random but deterministic based on the process conditions. SHAP analysis was used to identify the most influential parameters affecting wave mode prediction. The results show that for the water-cooled NETL RDE, wave mode is determinant and predictable based on the process parameters.

Weber, Justin [NETL] (ORCID:0000000218487035)↗
DOE OSTI2023

Microstructural Investigation of Precipitation Treated A709 Test Samples

This report presents the microstructural characterization using scanning transmission electron microscopy (STEM) of two creep-tested ESR1150-PT specimens, one short-term and one long-term. The results show that during creep testing, the microstructure of the A709 ESR1150-PT material continues to evolve as expected due to the synergistic effect of strain, temperature and time. Key features include dislocation forest and cell structures, precipitates on grain boundaries, precipitates randomly within grains, and precipitates associated with dislocations. In addition, the STEM study of one ESR1100-SA specimen that has been tested to rupture at a relatively low temperature (550°C) for a relatively long term (16226 h) is also presented. The results show that there is a high density of very fine nano precipitates form on dislocations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗