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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Evidence for the Thermal Sunyaev Zeldovich Effect Associated with Quasar Feedback

Using a radio-quiet subsample of the Sloan Digital Sky Survey spectroscopic quasar catalogue, spanning redshifts 0.5-3.5, we derive the mean millimetre and far-infrared quasar spectral energy distributions (SEDs) via a stacking analysis of Atacama Cosmology Telescope and Herschel-Spectral and Photometric Imaging REceiver data. We constrain the form of the far-infrared emission and find 3 sigma-4 sigma evidence for the thermal Sunyaev-Zel'dovich (SZ) effect, characteristic of a hot ionized gas component with thermal energy (6.2 plus or minus 1.7) × 10 (exp 60) erg. This amount of thermal energy is greater than expected assuming only hot gas in virial equilibrium with the dark matter haloes of (1-5) × 10(exp 12) h(exp −1) solar mass that these systems are expected to occupy, though the highest quasar mass estimates found in the literature could explain a large fraction of this energy. Our measurements are consistent with quasars depositing up to (14.5 +/- 3.3)tau (sub 8)(exp -1) per cent of their radiative energy into their circumgalactic environment if their typical period of quasar activity is tau(sub 8) x 108 yr. For high quasar host masses, approximately 10(exp 13) h(exp −1) solar mass, this percentage will be reduced. Furthermore, the uncertainty on this percentage is only statistical and additional systematic uncertainties enter at the 40 per cent level. The SEDs are dust dominated in all bands and we consider various models for dust emission. While sufficiently complex dust models can obviate the SZ effect, the SZ interpretation remains favoured at the 3 sigma-4 sigma level for most models.

Sunyaev-Zeldovich (SZ)↗
NASA NTRS1996

A 3-level Model for Schumann-Runge O2 Laser-Induced Fluorescence

A three level model has been developed for the analysis of Schumann-Runge band (B(sup 3)Sigma(sup -)(sub u ) <- X(sup 3)Sigma(sup -)(sub g)) laser-induced fluorescence of molecular oxygen, O2. Such a model is required due to the severe lower state depletion which can occur when transitions having relatively large absorption cross-sections are excited. Such transitions are often utilized via ArF* or KrF* excimer or dye-laser excitation in high temperature environments. The rapid predissociation of the upper state prevents substantial repopulation of the lower state by collisional processes, and the lower state may be largely depleted, even at laser fluences as low as 10-100 mJ/sq cm. The resulting LIF signal in such cases no longer varies linearly with laser pulse energy, and the extent of the sublinear behavior varies with the particular rovibrational transition of interest. Relating the measured signal to the lower state population, then, necessitates the use of exceedingly low laser fluences. These low fluences in turn lead to the need to compromise spatial resolution in order to generate sufficient signal.

Diskin, Glenn S.↗
DOE OSTI2025

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Salih, Fathya Y. M. [University of Notre Dame, IN ↗
NASA NTRS1989

Millimeter wave transmission studies of YBa2Cu3O7-delta thin films in the 26.5 to 40.0 GHz frequency range

Millimeter wave transmission measurements through YBa2Cu3O(7-delta) thin films on MgO, ZrO2 and LaAlO3 substrates, are reported. The films (approx. 1 micron) were deposited by sequential evaporation and laser ablation techniques. Transition temperatures T sub c, ranging from 89.7 K for the Laser Ablated film on LaAlO3 to approximately 72 K for the sequentially evaporated film on MgO, were obtained. The values of the real and imaginary parts of the complex conductivity, sigma 1 and sigma 2, are obtained from the transmission data, assuming a two fluid model. The BCS approach is used to calculate values for an effective energy gap from the obtained values of sigma sub 1. A range of gap values from 2 DELTA o/K sub B T sub c = 4.19 to 4.35 was obtained. The magnetic penetration depth is evaluated from the deduced values of sigma 2. These results are discussed together with the frequency dependence of the normalized transmission amplitude, P/P sub c, below and above T sub c.

Miranda, F. A.↗
NASA NTRS2016

Production of Peroxy Nitrates in Boreal Biomass Burning Plumes over Canada During the BORTAS Campaign

The observations collected during the BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) campaign in summer 2011 over Canada are analysed to study the impact of forest fire emissions on the formation of ozone (O3 and total peroxy nitrates (sigma)PNs, (sigma)ROONO2. The suite of measurements on board the BAe-146 aircraft, deployed in this campaign, allows us to calculate the production of O3 and of (sigma)PNs, a long-lived NOx reservoir whose concentration is supposed to be impacted by biomass burning emissions.In fire plumes, profiles of carbon monoxide (CO), which is a well-established tracer of pyrogenic emission, show concentration enhancements that are in strong correspondence with a significant increase of concentrations of (sigma)PNs, where as minimal increase of the concentrations of O3 and NO2 is observed. The (sigma)PN and O3 productions have been calculated using the rate constants of the first- and second-order react Pions of volatile organic compound (VOC) oxidation. The (sigma)PN and O3 productions have also been quantified by 0-D model simulation based on the Master Chemical Mechanism. Both methods show that in fire plumes the average production of (sigma)PNs and O3 are greater than in the background plumes, but the increase of (sigma)PN production is more pronounced than the O3 production. The average (sigma)PN production in fire plumes is from 7 to 12 times greater than in the background, whereas the average O3 production in fire plumes is from 2 to 5 times greater than in the background. These results suggest that, at least for boreal forest fires and for the measurements recorded during the BORTAS campaign,fire emissions impact both the oxidized NOy and O3;but (1)(sigma)PN production is amplified significantly more thanO3 production and (2) in the forest fire plumes the ratio between the O3 production and the (sigma)PN production is lower than the ratio evaluated in the background air masses, thus confirming that the role played by the (sigma)PNs produced during biomass burning is significant in the O3 budget. The implication of these observations is that fire emissions in some cases, for example boreal forest fires and in the conditions reported here, may influence more long-lived precursors of O3than short-lived pollutants, which in turn can be transported and eventually diluted in a wide area.

biomass burning↗
DOE OSTI2024

Dynamics of inspiraling dark energy

Abstract We investigate the dynamics of a multifield dark energy model, which arises from certain rapid-turning and inspiraling trajectories in field space. We find the speed of sound $$c_s$$ c s of the dark energy perturbations around the background and show that $$c_s$$ c s is monotonically decreasing with time. Furthermore, it has a positive-definite lower bound that implies a certain clustering scale. We also extend the previously known background solution for dark energy to an exact solution that includes matter. This allows us to address the implications of our model for two cosmological tensions. More precisely, we argue that the $$\sigma _8$$ σ 8 tension can be alleviated generically, while reducing the Hubble tension requires certain constraints on the parameter space of the model. Notably, a necessary condition for alleviating the Hubble tension is that the transition from matter domination to the dark energy epoch begins earlier than in $$\Lambda $$ Λ CDM.

Anguelova, Lilia↗
NASA NTRS1994

Large-scale structure after COBE: Peculiar velocities and correlations of cold dark matter halos

Large N-body simulations on parallel supercomputers allow one to simultaneously investigate large-scale structure and the formation of galactic halos with unprecedented resolution. Our study shows that the masses as well as the spatial distribution of halos on scales of tens of megaparsecs in a cold dark matter (CDM) universe with the spectrum normalized to the anisotropies detected by Cosmic Background Explorer (COBE) is compatible with the observations. We also show that the average value of the relative pairwise velocity dispersion sigma(sub v) - used as a principal argument against COBE-normalized CDM models-is significantly lower for halos than for individual particles. When the observational methods of extracting sigma(sub v) are applied to the redshift catalogs obtained from the numerical experiments, estimates differ significantly between different observation-sized samples and overlap observational estimates obtained following the same procedure.

Zurek, Wojciech H.↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
NASA NTRS1991

A search for the 2.223 MeV neutron capture gamma-ray line from the directions of Cygnus and the Galactic center

Free neutrons produced by nuclear reactions in high-temperature plasmas may undergo proton capture in ambient material, giving rise to gamma-ray emisison in a line at 2.223 MeV. Plausible settings for this process include accretion disks around black holes, material accreting onto neutron stars, and the binary companions of TeV gamma-ray sources. Data accumulated by the Solar Maximum Mission Gamma Ray Spectrometer (GRS) between 1980 and 1989 have been searched for evidence of this line, during periods when the black hole candidates Cygnus X-1 and the Galactic center were in transit across the GRS aperture. During these periods the neutron-star X-ray binaries Cygnus X-3 and Scorpius X-1 were also in the respective GRS fields of view. A 3-sigma upper limit of 0.0001/sq cm s has been placed on the steady emission in the 2.223 MeV line from the Galactic center and Scorpius X-1. Upper limits in the range 0.00012-0.00022 gamma/sq cm s have been set for Cygnus X-1 according to different models of the origin of the emission. The 3-sigma upper limit to the phase-averaged steady emission from Cygnus X-3 was found to be 0.00012 gamma/sq cm s.

Harris, Michael J.↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Decentralized control of Markovian decision processes: Existence Sigma-admissable policies

The problem of formulating and analyzing Markov decision models having decentralized information and decision patterns is examined. Included are basic examples as well as the mathematical preliminaries needed to understand Markov decision models and, further, to superimpose decentralized decision structures on them. The notion of a variance admissible policy for the model is introduced and it is proved that there exist (possibly nondeterministic) optional policies from the class of variance admissible policies. Directions for further research are explored.

Greenland, A.↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS2013

Soil Moisture Estimate Under Forest Using a Semi-Empirical Model at P-Band

Here we present the result of a semi-empirical inversion model for soil moisture retrieval using the three backscattering coefficients: sigma(sub HH), sigma(sub VV) and sigma(sub HV). In this paper we focus on the soil moisture estimate and use the biomass as an ancillary parameter estimated automatically from the algorithm and used as a validation parameter, We will first remind the model analytical formulation. Then we will sow some results obtained with real SAR data and compare them to ground estimates.

soil moisture↗
NASA NTRS1986

Evaluation of a nonlinear parameter extraction mathematical model including the term C(subm(sub delta e squared))

Shuttle flight test data were used to determine values for the short-period parameters. The best identified, as judged by its estimated standard deviation, was the elevon effectiveness parameter C (sub m (sub sigma e squared)). However, the scatter about the preflight prediction of C (sub m (sub sigma e squared)) was large. Other investigators have suggested that adding nonlinear terms to the mathematical model used to identify C (sub m (sub sigma e)) could reduce the scatter. The results of this investigation show that C (sub m (sub sigma e squared)) is the only identifiable nonlinear parameter applicable and that the changes in C (sub m (sub sigma e)) values when C (sub m (sub sigma e squared)) is included are in the order of ten percent for the data estimated.

Suit, W. T.↗
DOE OSTI2025

CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

Two crucial properties influencing the performance of high-temperature alloys are coefficient of thermal expansion (CTE) and phase constitution. It is desirable to have alloys with low CTE, which reduces CTE mismatch with the surface oxide and the likelihood of oxide spallation. Reducing the amount of brittle intermetallic phases such as Sigma (σ) enhances alloy ductility and processability. Here, we propose a multi-objective Bayesian Optimization (BO) model to simultaneously minimize CTE (at an operational temperature of 1150 °C) and T σ (temperature when the Sigma phase completely dissolves in the metal matrix), properties which are obtained from high-throughput CALculation of PHAse Diagrams (CALPHAD). The model successfully identifies several alloys with CTE ≤ 2 × 10 –5 /K and T σ ≤ 500 °C by exploring just 7% of the nickel–chromium–cobalt–aluminum–iron (Ni–Cr–Co–Al–Fe) composition space. Such multi-objective alloy design frameworks can be used to inform additive manufacturing experiments and accelerate alloy discovery for high-temperature energy applications.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Evidence for variability of the hard X-ray feature in the Hercules X-1 energy spectrum

The hard X-ray spectrum of HER X-1 was measured for the first time with a high resolution (1.4 keV FWHM) germanium spectrometer. The observation was performed near the peak of the on-state in the 35 day cycle and the 1.24 pulsations were observed between the energies of 20 keV and 70 keV. The feature corresponds to an excess of 7.5 sigma over the low energy continuum. Smooth continuum models are poor fits to the entire energy range (chance probabilities of 2 percent or less). The best fit energies are 35 keV for an absorption line and 39 keV for an emission line. These are significantly lower energies than those derived from previous experiments. A direct comparison of our data with the results of the MPI/AIT group shows statistically significant variations which strongly suggest variability in the source.

Tueller, J.↗
NASA NTRS1981

Simulation of the planetary boundary layer with the UCLA general circulation model

A planetary boundary layer (PBL) model is presented which employs a mixed layer entrainment formulation to describe the mass exchange between the mixed layer with the upper, laminar atmosphere. A modified coordinate system couples the mixed layer model with large scale and sub-grid scale processes of a general circulation model. The vertical coordinate is configured as a sigma coordinate with the lower boundary, the top of the PBL, and the prescribed pressure level near the tropopause expressed as coordinate surfaces. The entrainment mass flux is parameterized by assuming the dissipation rate of turbulent kinetic energy to be proportional to the positive part of the generation by convection or mechanical production. The results of a simulation of July are presented for the entire globe.

Suarez, M. J.↗
NASA NTRS1984

Evidence for variability of the hard X-ray feature in the Hercules X-1 energy spectrum

The hard X-ray spectrum of HER X-1 was measured for the first time with a high resolution (1.4 keV FWHM) germanium spectrometer. The observation was performed near the peak of the on-state in the 35 day cycle and the 1.24 pulsations were observed between the energies of 20 keV and 70 keV. The feature corresponds to an excess of 7.5 sigma over the low energy continuum. Smooth continuum models are poor fits to the entire energy range (chance probabilities of 2 percent or less). The best fit energies are 35 keV for an absorption line and 39 keV for an emission line. These are significantly lower energies than those derived from previous experiments. A direct comparison of the data with the results of the MPI/AIT group shows statistically significant variations which strongly suggest variability in the source. Previously announced in STAR as N83-37036

Tueller, J.↗