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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
DOE OSTI2016

In-depth investigation on quantitative characterization of pyrolysis oil by 31 P NMR

The characterization of different heteroatom functional groups by employing 31 P NMR has been developed for almost 30 years. In this study, an in-depth investigation of this commonly used method has been accomplished for the analysis of pyrolysis oil. Several commonly used internal standards for 31 P NMR have been examined by in situ monitoring. The results indicated that endo-N-hydroxy-5-norbornene-2,3-dicarboximide (NHND) is not stable after a long period of storage or experiment (>12 hours), but both cyclohexanol and triphenylphosphine oxide (TPPO) can be used as internal standards if a long experiment or storage is required. The pyrolysis oil has also been investigated by both short time (16 hours) in situ monitoring and long time (14 days) ex situ monitoring. The results showed that aliphatic OH, carboxylic acids and water contents are not very stable after 2 hours, and thus a short time of preparation, storage, and experiment need to be considered to ensure a precise quantitative measurement. The decomposition products are still unclear, but some preliminary investigations for different acids, (e.g. formic acid) have been accomplished. The results indicated that the aromatic carboxylic acids (benzoic acid and vanillic acid) are more stable than formic acid and acetic acid. Interestingly, the formic acid will even decompose to some other compounds at the very beginning of the in situ monitoring test. Further characterization found that water is one of the major products for the decomposition of formic acid in the 31 P NMR solution. Finally, as far as we know, this is the first report on such time-dependent changes when using 31 P NMR to analyze the pyrolysis oil, and these results show that proper application of this method is essential to achieve reliable quantitative data.

09 BIOMASS FUELS↗
DOE OSTI2015

Two-temperature transport coefficients of SF{sub 6}–N{sub 2} plasma

Sulfur hexafluoride (SF{sub 6}) is widely adopted in electric power industry, especially in high-voltage circuit breakers and gas-insulated switchgear. However, the use of SF{sub 6} is limited by its high liquidation temperature and high global warming potential. Recently, research shows SF{sub 6}–N{sub 2} mixture, which shows environmental friendliness and good electrical properties, may be a feasible substitute for pure SF{sub 6}. This paper is devoted to the calculation of and transport coefficients of SF{sub 6}–N{sub 2} mixture under both LTE (local thermodynamic equilibrium) and non-LTE condition. The two–temperature mass action law was used to determine the composition. The transport coefficients were calculated by classical Chapman–Enskog method simplified by Devoto. The thermophysical properties are presented for electron temperatures of 300–40 000 K, ratios of electron to heavy species temperature of 1–10 and N{sub 2} mole fraction of 0%–100% at atmospheric pressure. The ionization processes under both LTE and non-LTE have been discussed. The results show that deviations from local thermodynamic equilibrium significantly affect the properties of SF{sub 6}–N{sub 2} plasma, especially before the plasma is fully ionized. The different influence of N{sub 2} on properties for SF{sub 6}–N{sub 2} plasma in and out of LTE has been found. The results will serve as reliable reference data for computational simulation of the behavior of SF{sub 6}–N{sub 2} plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Thermodynamic properties and transport coefficients of air thermal plasmas mixed with ablated vapors of Cu and polytetrafluoroethylene

Because the fault arc in aircraft electrical system often causes a fire, it is particularly important to analyze its energy and transfer for aircraft safety. The calculation of arc energy requires the basic parameters of the arc. This paper is mainly devoted to the calculations of equilibrium composition, thermodynamic properties (density, molar weight, enthalpy, and specific heat at constant pressure) and transport coefficients (thermal conductivity, electrical conductivity, and viscosity) of plasmas produced by a mixture of air, Cu, and polytetrafluoroethylene under the condition of local thermodynamic equilibrium. The equilibrium composition is determined by solving a system of equations around the number densities of each species. The thermodynamic properties are obtained according to the standard thermodynamic relationships. The transport coefficients are calculated using the Chapman-Enskog approximations. Results are presented in the temperature range from 3000 to 30 000 K for pressures of 0.08 and 0.1 MPa, respectively. The results are more accurate and are reliable reference data for theoretical analysis and computational simulation of the behavior of fault arc.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Equation of state and transport properties of warm dense helium via quantum molecular dynamics simulations

The equation of state, self-diffusion, and viscosity coefficients of helium have been investigated by quantum molecular dynamics (QMD) simulations in the warm dense matter regime. Our simulations are validated through the comparison with the reliable experimental data. The calculated principal and reshock Hugoniots of liquid helium are in good agreement with the gas-gun data. On this basis, we revisit the issue for helium, i.e., the possibility of the instabilities predicted by chemical models at around 2000 GPa and 10 g/cm{sup 3} along the pressure isotherms of 6309, 15 849, and 31 623 K. Our calculations show no indications of instability in this pressure-temperature region, which reconfirm the predictions of previous QMD simulations. The self-diffusion and viscosity coefficients of warm dense helium have been systematically investigated by the QMD simulations. We carefully test the finite-size effects and convergences of statistics, and obtain numerically converged self-diffusion and viscosity coefficients by using the Kubo-Green formulas. The present results have been used to evaluate the existing one component plasma models. Finally, the validation of the Stokes-Einstein relationship for helium in the warm dense regime is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

How strongly do hydrogen and water molecules stick to carbon nanomaterials?

The interaction strength of molecular hydrogen and water to carbon nanomaterials is relevant to, among many applications, hydrogen storage, water treatment, and water flow. Yet, accurate interaction energies for hydrogen and water with carbon nanotubes (CNTs) remain scarce despite the importance of having reliable benchmark data to inform experiments and to validate computational models. Here, benchmark fixed-node diffusion Monte Carlo (DMC) interaction energies are provided for hydrogen and water monomers inside and outside a typical zigzag CNT. The DMC interaction energies provide valuable insight into molecular interactions with CNTs in general and are also expected to be particularly relevant to gas uptake studies on CNTs. In addition, a selection of density functional theory (DFT) exchange-correlation (xc) functionals and force field potentials that ought to be suitable for these systems is compared. An unexpected variation is found in the performance of DFT van der Waals (vdW) models in particular. An analysis of the peculiar discrepancy between different vdW models indicates that medium-range correlation (at circa 3 to 5 Å) plays a key role inside CNTs and is poorly predicted by some vdW models. Using accurate reference information, this work reveals which xc functionals and force fields perform well for molecules interacting with CNTs. The results will be valuable to future work on these and related systems that involve molecules interacting with low-dimensional systems.

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

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Finite-temperature vibrational full configuration interaction

Thermodynamic functions of an ideal molecular gas due to its anharmonic vibrations are evaluated in a wide range of temperature (T) by the vibrational full-configuration-interaction (FCI) method using a quartic force field and a finite number (N) of harmonic-oscillator basis functions along each normal mode. The thermodynamic functions considered are the grand potential (Ω), internal energy (U), and entropy (S). They are compared with those obtained from the Bose–Einstein theory with or without truncation of the harmonic-oscillator basis functions after quantum number N–1. The comparison reveals that the finite-basis-set errors in Ω and U are, respectively, k B Tln(k B T/Nℏω) and k B T per mode in the high-T limit, obscuring anharmonic effects when k B T > ℏω, where ω is the lowest mode frequency. Here, the benchmark data for several low-order perturbation corrections to Ω, U, and S are also obtained as the numerical derivatives of their FCI values with respect to dimensionless perturbation strength, and the domain of T and N in which these data are reliable (for the N → ∞ limits) is discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

Herschel dust emission as a probe of starless cores mass: MCLD 123.5+24.9 of the polaris flare

We present newly processed archival Herschel images of molecular cloud MCLD 123.5+24.9 in the Polaris Flare. This cloud contains five starless cores. Using the spectral synthesis code Cloudy, we explore uncertainties in the derivation of column densities, and hence masses of molecular cores from Herschel data. We first consider several detailed grain models that predict far-infrared grain opacities. Opacities predicted by the models differ by more than a factor of two, leading to uncertainties in derived column densities by the same factor. Then we consider uncertainties associated with the modified blackbody fitting process used by observers to estimate column densities. For high column density clouds (N(H) ≫ 1 × 10{sup 22} cm{sup −2}), this fitting technique can underestimate column densities by about a factor of three. Finally, we consider the virial stability of the five starless cores in MCLD 123.5+24.9. All of these cores appear to have strongly sub-virial masses, assuming, as we argue, that {sup 13}CO line data provide reliable estimates of velocity dispersions. Evidently, they are not self-gravitating, so it is no surprise that they are starless.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Measurement of the 169 Tm(n,3n) 167 Tm cross section and the associated branching ratios in the decay of 167 Tm [Measurement of the 169 Tm(n,3n) 167 Tm cross section and the branching ratios in the decay of 167 Tm]

The cross section for the 169 Tm(n,3n) 167 Tm reaction was measured from 17 to 22 MeV using quasimonoenergetic neutrons produced by the 2 H(d,n) 3 He reaction. This energy range was studied to resolve the discrepancy between previous (n,3n) cross-section measurements. In addition, the absolute γ-ray branching ratios following the electron-capture decay of 167 Tm were measured. Furthermore, these results provide more reliable nuclear data for an important diagnostic that is used at the National Ignition Facility to estimate the yield of reaction-in-flight neutrons produced via the inertial-confinement-fusion plasma in deuterium-tritium capsules.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Structure of Ne 20 states in resonance O 16 + α elastic scattering

Background: The nuclear structure of the cluster bands in 20 Ne presents a challenge for different theoretical approaches. It is especially difficult to explain the broad 0 + , 2 + states at 9 MeV excitation energy. Simultaneously, it is important to obtain more reliable experimental data for these levels in order to quantitatively assess the theoretical framework. Purpose: To obtain new data on 20 Ne α- cluster structure. Method: Thick target inverse kinematics technique was used to study the 16 O+α resonance elastic scattering and the data were analyzed using an R matrix approach. The 20 Ne spectrum, the cluster and nucleon spectroscopic factors were calculated using cluster-nucleon configuration interaction model (CNCIM). Results: We determined the parameters of the broad resonances in 20 Ne: 0 + level at 8.77 ± 0.150 MeV with a width of 750 $^{+500}_{-220}$ keV; 2 + level at 8.75 ± 0.100 MeV with the width of 695 ± 120 keV; the width of 9.48 MeV level of 65 ± 20 keV and showed that 9.19 MeV, 2 + level (if exists) should have width ≤ 10 keV. The detailed comparison of the theoretical CNCIM predictions with the experimental data on cluster states was made. Conclusions: Our experimental results by the TTIK method generally confirm the adopted data on α-cluster levels in 20 Ne. The CNCIM gives a good description of the 20 Ne positive parity states up to an excitation energy of ~7 MeV, predicting reasonably well the excitation energy of the states and their cluster and single particle properties. At higher excitations, the qualitative disagreement with the experimentally observed structure is evident, especially for broad resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Detecting hidden layers from spreading dynamics on complex networks

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. Furthermore, we conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Liquid Structure of Shock-Compressed Hydrocarbons at Megabar Pressures

We present here results for the ionic structure in hydrocarbons (polystyrene, polyethylene) that were shock compressed to pressures of up to 190 GPa, inducing rapid melting of the samples. The structure of the resulting liquid is then probed using in situ diffraction by an x-ray free electron laser beam, demonstrating the capability to obtain reliable diffraction data in a single shot, even for low-Z samples without long range order. The data agree well with ab initio simulations, validating the ability of such approaches to model mixed samples in states where complex interparticle bonds remain, and showing that simpler models are not necessarily valid. While the results clearly exclude the possibility of complete carbon-hydrogen demixing at the conditions probed, they also, in contrast to previous predictions, indicate that diffraction is not always a sufficient diagnostic for this phenomenon.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The crystal structures of Fe-bearing MgCO 3 sp 2 - and sp 3 -carbonates at 98 GPa from single-crystal X-ray diffraction using synchrotron radiation

The crystal structure of MgCO 3 -II has long been discussed in the literature where DFT-based model calculations predict a pressure-induced transition of the carbon atom from the sp 2 to the sp 3 type of bonding. We have now determined the crystal structure of iron-bearing MgCO 3 -II based on single-crystal X-ray diffraction measurements using synchrotron radiation. We laser-heated a synthetic (Mg 0.85 Fe 0.15 )CO 3 single crystal at 2500 K and 98 GPa and observed the formation of a monoclinic phase with composition (Mg 2.53 Fe 0.47 )C 3 O 9 in the space group C 2/ m that contains tetrahedrally coordinated carbon, where CO 4 4- tetrahedra are linked by corner-sharing oxygen atoms to form three-membered C 3 O 9 6- ring anions. The crystal structure of (Mg 0.85 Fe 0.15 )CO 3 (magnesium iron carbonate) at 98 GPa and 300 K is reported here as well. In comparison with previous structure-prediction calculations and powder X-ray diffraction data, our structural data provide reliable information from experiments regarding atomic positions, bond lengths, and bond angles.

Chariton, Stella (ORCID:0000000155220498)↗
DOE OSTI2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.

Subasi, Omer↗
DOE OSTI2017

MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection

Future HPC systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs are rather harmful and dangerous in that they cannot be detected by hardware. We propose an online machine-learning based silent data corruption detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In particular, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms in our study, and enable the detector to automatically select the best-fit algorithms at runtime to adapt to the data dynamics. Our learning framework exhibits low memory overhead (less than 1%), since it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into the training data. Experiments based on real-world scientific applications/benchmarks show that our framework can get the detection sensitivity (i.e., recall) up to 99% while the false positive rate is limited to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-art spatial technique.

Subasi, Omer↗
DOE OSTI2026

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗