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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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Two figures are shown: Pressure dependence of the shear modulus of Ta for two fundamental curves: the 300 K isotherm and the Hugoniot; and, Scaling of the high pressure strength (Y) of Ta with shear modulus (G). The dashed curve represents a hypothetical G which is fit such that the linear scaling approximation matches the data.
The objective of this project is to develop battery performance models, thermal models and life models for SCE to use in simulation and analysis of grid services involving energy storage. NREL will fit the models to three separate chemistries using test data provided by SCE. The dataset for each chemistry will include electrical, thermal and aging response to different temperatures and cycling conditions, measured under a variety of cell-level and module-level experiments described in SCE test protocol documents. Once tuned to a test dataset, the battery system model software will provide predictions of battery energy and power loss, and thus cycle and calendar life, for any energy storage grid service power profile of interest to SCE. The automated battery life modeling and simulation tool can also be applied to any other grid battery systems as long as necessary input data is available to accelerate battery model development and battery lifetime analysis.
The talk will give an overview of tuning Geant4 model parameters via fits of simulated distributions to the experimental thin target data, and will introduce the concept of "tunes" in Geant4 hadronic models.
This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.
DUNE is a long-baseline neutrino oscillation experiment that will take data in a wideband neutrino beam at Fermilab, starting in the latter half of the 2020s. The experiment is planning to build a very capable near detector to facilitate the high precision extraction of oscillation parameters. Part of the mission of the near detector is to acquire powerful data sets that can be used to constrain the fits used in the oscillation analyses and improve the neutrino interaction model. In this talk, the importance and the potential of a vibrant program of neutrino interaction physics using this detector is described. A few case studies that illustrate the power of the DUNE near detector for studying neutrino interaction physics are described.
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.
Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. Further, we provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.
The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA data on inclusive deep inelastic ep scattering together with jet data as published by the H1 and ZEUS collaborations. A perturbative QCD fit, simultaneously of $\alpha _s(M_Z^2)$ and the PDFs, was performed with the result $\alpha _s(M_Z^2)= 0.1156 \pm 0.0011~\mathrm{(exp)}~ ^{+0.0001}_{-0.0002}~ \mathrm{(model}$ $\mathrm{+ parameterisation)}~ \pm 0.0029~\mathrm{(scale)}$. The PDF sets of HERAPDF2.0Jets NNLO were determined with separate fits using two fixed values of $\alpha _s(M_Z^2)$, $\alpha _s(M_Z^2)$ = 0.1155 and 0.118, since the latter value was already chosen for the published HERAPDF2.0 NNLO analysis based on HERA inclusive DIS data only. The different sets of PDFs are presented, evaluated and compared. The consistency of the PDFs determined with and without the jet data demonstrates the consistency of HERA inclusive and jet-production cross-section data. The inclusion of the jet data reduced the uncertainty on the gluon PDF. Predictions based on the PDFs of HERAPDF2.0Jets NNLO give an excellent description of the jet-production data used as input.
This dataset contains data on daily soil temperature, moisture and flux, and soil carbon stock and root biomass across a soil profile down to 100 cm depth at Blodgett Forest Research Station, CA, USA. These data were generated to determine if modeling of an experimental soil warming of 4C showed increased soil CO2 emissions and changes in bulk soil carbon stocks with depth consistent with field observations, as part of the study: Riley et al. (2025) Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics in Journal of Advances in Modeling Earth Systems. This research was performed within the framework of the TES Belowground Biogeochemistry SFA project, in particular association with a 1 m-deep experimental soil heating experiment at the University of California Blodgett Forest Research Station, California (120 ° 39′40′′W; 38 ° 54′43′′N). Continuous data were collected at the plot level, and bulk soil carbon and root biomass were sampled once a year from each plot from 0-100 cm, in 10 cm intervals. Measurements relevant to the current study include soil temperature and soil volumetric water content measured continuously at multiple depths in the top meter; fine root biomass and SOC stocks measured from annual soil cores. Soil flux was continuously monitored using a LI-8100 Automated CO2 Flux System in conjunction with the LI-8150 Multiplexer (Licor, Nebraska, USA). Soil flux was determined using SoilFluxPro software, with flux values showing an R² fit of less than 0.9 being excluded from the analysis. Data were collected from each paired plot (1-3): one control (C) and one heated (H).
Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.
Recent multi-messenger detection of the binary neutron star merger (GW170817) energized the astrophysical community and encouraged further research for determination of nuclear physics observables. Comprehensive studies of atomic nuclei in the cosmos provide an opportunity for investigating these astrophysical phenomena and acquiring complementary information on stellar nucleosynthesis processes that can be verified using the latest nuclear data. Evaluated Nuclear Data File (ENDF) libraries contain complete collections of reaction cross sections over the energy range relevant to astrophysics, fission yields and decay data. These data collections have been used worldwide in nuclear science, industry and national security applications. There is great interest in exploring the ENDF/B-VIII.0 and TALYS Evaluated Nuclear Data Library (TENDL-2015) for nuclear astrophysics purposes and comparing findings with the Karlsruhe Astrophysical Database of Nucleosynthesis in Stars (KADoNiS). The Maxwellian-averaged cross sections (MACS) and astrophysical reaction rates have been calculated using the ENDF/B-VIII.0 and TENDL-2015 evaluated data sets. The calculated cross sections were combined with the solar system abundances and fitted using the classical model of stellar nucleosynthesis. Astrophysical rapid- and slow-neutron capture, r- and s-process, respectively, abundances were obtained from present data and compared with available values. Further analysis of MACS reveals potential evaluated libraries data deficiencies and a strong need for new measurements. The current results demonstrate a large nuclear astrophysics potential of evaluated libraries and mutually beneficial relations between nuclear industry and research e orts.
We use archival Herschel data to examine the singly ionized carbon ([C II]) content of 14 star-forming dwarf galaxies in the Virgo cluster. We use spectral energy distribution fits to far-infrared, mid-infrared, near-infrared, optical, and ultraviolet data to derive the total infrared continuum (TIR) for these galaxies. We compare the [C II]/TIR ratio for dwarf galaxies in the central part of Virgo to those in the southern part of the cluster and to galaxies with similar TIR luminosities and metallicities in the Herschel Dwarf Galaxy Survey (DGS) sample of field dwarf galaxies to look for signs of [C II] formation independent of star formation. Our analysis indicates that the sample of Virgo dwarfs in the central part of the cluster has significantly higher values of [C II]/TIR than the sample from the southern part of the cluster and the sample from the DGS, while the southern sample is consistent with the DGS. This [C II]/TIR excess implies that a significant fraction of the [C II] in the dwarf galaxies in the cluster center has an origin other than star formation and is likely to be due to environmental processes in the central part of the cluster. We also find a surprisingly strong correlation between [C II]/TIR and the local ram pressure felt by the dwarf galaxies in the cluster. In this respect, we claim that the excess [C II] we see in these galaxies is likely to be due to formation in ram-pressure shocks.
Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.
Density functional theory calculations were used to determine the stability of metal slabs consisting of a Pt surface monolayer and intermetallic supporting layers made from combinations of six transition metal elements (Pt, Fe, Co, Ni, Cu, and Ag), as a model system for Pt skin intermetallic core nanoparticle catalysts. The stability of the slabs is largely determined by strain at the interface of the Pt skin and the subsurface intermetallic, which was described by a lattice matching parameter (r). The surface charge on the Pt skin was found to be correlated with the average electronegativity (EN) of the intermetallic core, so this average EN was used as a descriptor for how the electronic coupling (or ligand effect) affects adsorption energies. A total of 46 slabs were investigated in terms of their stability, from which 10 stable slabs were selected for further studies of adsorbate binding (OOH*, O*, and OH*) that are intermediates in the oxygen reduction reaction (ORR). The correlation between all three adsorption energies and descriptors r and EN was found. Using a linear fit between our descriptors and the calculated adsorption energies, the overpotential for the ORR was obtained as a function of r and EN, from which a volcano plot was produced. The volcano peak was found at r = 0.96 or at EN = 2.025. Interestingly, neither r nor EN was a sufficient single reactivity descriptor as the data points were well off the general trend in both linear fits; this implies that both the strain effect and the ligand effect influence the adsorption energies, although they are partly correlated. The (r, EN) target peak parameters were used to screen over 241 intermetallic combinations of transition metal elements as active ORR activity. In conclusion, this analysis identified 11 intermetallic compounds which can support a Pt skin to have a high predicted ORR activity.
Asteroid 2012 TC4 is a small (∼10 m) near-Earth object that was observed during its Earth close approaches in 2012 and 2017. Earlier analyses of light curves revealed its excited rotation state. We collected all available photometric data from the two apparitions to reconstruct its rotation state and convex shape model. We show that light curves from 2012 and 2017 cannot be fitted with a single set of model parameters; the rotation and precession periods are significantly different for these two data sets, and they must have changed between or during the two apparitions. Nevertheless, we could fit all light curves with a dynamically self-consistent model assuming that the spin states of 2012 TC4 in 2012 and 2017 were different. To interpret our results, we developed a numerical model of its spin evolution in which we included two potentially relevant perturbations: (i) gravitational torque due to the Sun and Earth and (ii) radiation torque, known as the Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect. Despite our model simplicity, we found that the role of gravitational torques is negligible. Instead, we argue that the observed change of its spin state may be plausibly explained as a result of the YORP torque. To strengthen this interpretation, we verify that (i) the internal energy dissipation due to material inelasticity and (ii) an impact with a sufficiently large interplanetary particle are both highly unlikely causes of its observed spin state change. If true, this is the first case where the YORP effect has been detected for a tumbling body.
The bright polarized synchrotron emission, away from the Galactic plane, originates mostly from filamentary structures. We implement a filament finder algorithm which allows the detection of bright elongated structures in polarized intensity maps. We analyse the sky at 23 and 30 GHz as observed respectively by WMAP and Planck. We identify 19 filaments, 13 of which have been previously observed. For each filament, we study the polarization fraction, finding values typically larger than for the areas outside the filaments, excluding the Galactic plane, and a fraction of about 30% is reached in two filaments. We study the polarization spectral indices of the filaments, and find a spectral index consistent with the values found in previous analysis (about -3.1) for more diffuse regions. Decomposing the polarization signals into the E and B families, we find that most of the filaments are detected in P E , but not in P B . We then focus on understanding the statistical properties of the diffuse regions of the synchrotron emission at 23 GHz. Using Minkowski functionals and tensors, we analyse the non-Gaussianity and statistical isotropy of the polarized intensity maps. For a sky coverage corresponding to 80% of the fainter emission, and on scales smaller than 6 degrees (ℓ > 30), the deviations from Gaussianity and isotropy are significantly higher than 3σ. The level of deviation decreases for smaller scales, however, it remains significantly high for the lowest analised scale (~ 1.5°). When 60% sky coverage is analysed, we find that the deviations never exceed 3σ. Finally, we present a simple data-driven model to generate non-Gaussian and anisotropic simulations of the synchrotron polarized emission. The simulations are fitted in order to match the spectral and statistical properties of the faintest 80% sky coverage of the data maps.
This study developed a model for predicting viscosity of alkali-alumino-borosilicate glass melts as functions of composition and temperature. The model is based on a total of 3935 viscosity-temperature data from 574 glasses with viscosity values ranging from 2.53 to 7260 Poise (P) in the temperature range of 900–1260°C. Several different model forms were surveyed, including those based on Arrhenius, Vogel-Fulcher-Tammann, Avramov-Milchev, and Mauro-Yue-Ellison-Gupta-Allen. For each of these models, combinations of the temperature-independent parameters were fitted to composition. It was found that generally fitting more than one temperature-independent parameter as functions of composition resulted in overfitting. The Avramov-Milchev-based model was found to best represent the Hanford low-activity waste glass melt viscosity data based on model fit and validation statistics. A 21-term partial quadratic mixture model was recommended for use. This model predicts melt viscosity with a root-mean square error of .1736 ln(P), which is similar to the error in viscosity measurements from replicate glass analyses of .1383 ln(P). Viscosity was found to be most increased by SiO 2 > Al 2 O 3 > ZrO 2 > SnO 2 and most decreased by Li 2 O > Na 2 O > B 2 O 3 > CaO > K 2 O > MgO, at temperatures from 900 to 1260°C.
Geological interpretations, earthquake source inversions and ground motion modeling, among other applications, require models that jointly resolve crustal and mantle structure. With the second generation of the Collaborative Seismic Earth Model (CSEM2), we present a global multi-resolution tomographic Earth model that serves this purpose. The model evolves through successive regional- and global-scale refinements. While the first generation aggregated regional models, with this study, we ensure consistency between all individual submodels, resulting in a model that accurately explains wave propagation across scales. Recent regional tomographic models were incorporated, comprising continental-scale inversions for Asia and Africa, as well as regional inversions for the Western US, Central Andes, Iran, and Southeast Asia. Across all regional refinements, over 793,000 source-receiver pairs contributed. Moreover, the long-wavelength Earth model (LOWE) introduces large-scale structures outside of pre-existing local refinements. A full-waveform inversion for global anisotropic P-and S-wave speed structure over a total of 194 iterations with a minimum period of 50 s on a large data set of 1 hr of waveform data from 2,423 earthquakes and over 6 million source-receiver pairs ensures that regional updates in the crust and uppermost mantle translate into updates of deeper, global-scale structure. To test the performance of CSEM2, we evaluate waveform fits between observed and synthetic seismograms at 50 s for an independent data set on the global scale, and on the regional scale for lower periods. We accurately simulate waveforms within and across regional refinements, maintaining the original resolution of the submodels embedded in the global framework.