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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spin cutoff factor and level density for Ni 59 from an analysis of compound nuclear reactions

The spin cutoff parameter for 59 Ni has been studied from different types of experimental data, including neutron angular distributions from the 56 Fe(α,n) 59 Ni reaction, spin of discrete levels, the level density from the proton evaporation spectrum of the 54 Fe( 6 Li,p) 59 Ni reaction and neutron resonance spacing. Experimental data points were compared with calculations using models widely used in literature. It was found that the available empirical models overestimate data points in the energy region below the neutron separation energy, while microscopic calculations which take into account pairing correlations within Hartree-Fock +Bardeen-Cooper-Schrieffer approach are consistent with data. As a result, it confirmed earlier findings that pairing correlations play an important role and need to be taken into account when the spin cutoff parameter is calculated below the neutron separation energy.

(a,n) reaction↗
DOE OSTI2023

Component-Level Analysis of Heating and Cooling Loads in the U.S. Residential Building Stock

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building simulation↗
DOE OSTI2024

Finite density QCD equation of state: Critical point and lattice-based 𝑇′ expansion

Here, we present a novel construction of the QCD equation of state (EoS) at finite baryon density. Our work combines a recently proposed resummation scheme for lattice QCD results with the universal critical behavior at the QCD critical point. This allows us to obtain a family of equations of state in the range 0 ≤ 𝜇 𝐵 ≤ 700 MeV and 25 MeV ≤ 𝑇 ≤ 800 MeV, which match lattice QCD results near 𝜇 𝐵 =0 while featuring a critical point in the 3D Ising model universality class. The position of the critical point can be chosen within the range accessible to beam-energy scan heavy-ion collision experiments. The strength of the singularity and the shape of the critical region are parametrized using a standard parameter set. We impose stability and causality constraints and discuss the available ranges of critical point parameter choices, finding that they extend beyond earlier parametric QCD EoS proposals. We present thermodynamic observables, including baryon density, pressure, entropy density, energy density, baryon susceptibility and speed of sound, that cover a wide range in the QCD phase diagram relevant for experimental exploration.

Astronomy & Astrophysics↗
DOE OSTI2020

Accelerating geostatistical modeling using geostatistics-informed machine Learning

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.

58 GEOSCIENCES↗
DOE OSTI2023

Entropy and Seebeck signals meet on the edges

Here, we explore the electronic entropy per particle s and Seebeck coefficient S in zigzag graphene ribbons. Pristine and edge-doped ribbons are considered using tight-binding models to inspect the role of edge states in the observed thermal transport properties. As a band gap opens when the ribbons are doped at one or both edges, due to asymmetric edge potentials, we find that s and S signals are closely related to each other: both develop sharp dip-peak line shapes as the chemical potential lies in the gap, while the ratio s/S exhibits a near-constant value equal to the elementary charge e at low temperatures. This constant ratio suggests that S can be seen as the transport differential entropy per charge, as suggested by some authors. Our calculations also indicate that measurement of s and S may be useful as a spectroscopic probe of different electronic energy scales involved in such quantities in gapped materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei

The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton emitters poses an appreciable challenge for nuclear theory as it often involves far extrapolations. In addition, significant discrepancies between nuclear models in the proton-rich territory call for quantified predictions. With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart. Separation energies were computed within nuclear density functional theory using several Skyrme and Gogny energy density functionals. We also considered mass predictions based on two models used in astrophysical studies. Quantified predictions were obtained for each model using Bayesian Gaussian processes trained on separation-energy residuals and combined via Bayesian model averaging. Here, we obtained a good agreement between averaged predictions of statistically corrected models and experiment. In particular, we quantified model results for one- and two-proton separation energies and derived probabilities of proton emission. This information enabled us to produce a quantified landscape of proton-rich nuclei. The most promising candidates for two-proton decay studies have been identified. Finally, the methodology used in this work has broad applications to model-based extrapolations of various nuclear observables. It also provides a reliable uncertainty quantification of theoretical predictions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Full‐dimensional coupled‐channel statistical approach to atom‐triatom systems and applications to H/D + O 3 reaction

Abstract The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long‐lived intermediate complex. In this work, a time‐independent coupled‐channel implementation of the SQM approach is developed for atom‐triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O 3 capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

Yang, Dongzheng↗
DOE OSTI2020

Safety Design of Water Infrastructures in A Modern Era

In this chapter, we review the current practice of engineering safety design of large water management infrastructures. Those large infrastructures are designed using probable maximum precipitation (PMP) to withstand extreme conditions, but climate change has been posing challenges on the conventional PMP estimation approach. We discuss multiple limitations of the current approaches, and propose a few possible solutions based on the advancement of atmospheric modeling and statistical analysis of extreme precipitation mechanisms.

Chen, Xiaodong↗
DOE OSTI2023

Measurement of the ${\Lambda}_c^{+}$ to D 0 production ratio in peripheral PbPb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

We report on a measurement of the ${\Lambda}_c^{+}$ to D 0 production ratio in peripheral PbPb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV with the LHCb detector in the forward rapidity region 2 < y < 4.5. The ${\Lambda}_c^{+}$ (D 0 ) hadrons are reconstructed via the decay channel ${\Lambda}_c^{+}$ → pK – π + (D 0 → K – π + ) for 2 < p T < 8 GeV/c and in the centrality range of about 65–90%. The results show no significant dependence on p T , y or the mean number of participating nucleons. They are also consistent with similar measurements obtained by the LHCb collaboration in pPb and Pbp collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV. The data agree well with predictions from PYTHIA in pp collisions at $\sqrt{s}$ = 5 TeV but are in tension with predictions of the Statistical Hadronization model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurement of the production cross section of prompt $ {\Xi}_{\mathrm{c}}^0 $ baryons at midrapidity in pp collisions at $ \sqrt{s} $ = 5.02 TeV

The transverse momentum (p T ) differential cross section of the charm-strange baryon $ {\Xi}_{\mathrm{c}}^0 $ is measured at midrapidity (|y| < 0.5) via its semileptonic decay into e + Ξ - ν e in pp collisions at $\sqrt{s}$ = 5.02 TeV with the ALICE detector at the LHC. The ratio of the p T -differential $ {\Xi}_{\mathrm{c}}^0 $-baryon and D 0 -meson production cross sections is also reported. The measurements are compared with simulations with different tunes of the PYTHIA 8 event generator, with predictions from a statistical hadronisation model (SHM) with a largely augmented set of charm-baryon states beyond the current lists of the Particle Data Group, and with models including hadronisation via quark coalescence. The p T -integrated cross section of prompt $ {\Xi}_{\mathrm{c}}^0 $-baryon production at midrapidity is also reported, which is used to calculate the baryon-to-meson ratio $ {\Xi}_{\mathrm{c}}^0 $/D 0 = 0.20 ± 0.04 (stat.)$^{+0.08}_{-0.07}$ (syst.). These results provide an additional indication of a modification of the charm fragmentation from e + e - and e - p collisions to pp collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

High-precision measurements of half-lives for 69 Ge, 73 Se, 83 Sr, 85 mSr, and 63 Zn radionuclides relevant to the astrophysical p-process via photoactivation at the Madison Accelerator Laboratory

The ground state half-lives of 69 Ge, 73 Se, 83 Sr, 63 Zn, and the half-life of the 1/2 - isomer in 85 Sr have been measured with high precision using the photoactivation technique at an unconventional bremsstrahlung facility that features a repurposed medical electron linear accelerator. The γ -ray activity was counted over about 6 half-lives with a high-purity germanium detector, enclosed into an ultra low-background lead shield. The measured half-lives are: T 1/2 ( 69 Ge) = 38.82 ± 0.07 (stat) ± 0.06 (sys) h; T 1/2 ( 73 Se) = 7.18 ± 0.02 (stat) ± 0.004 (sys) h; T 1/2 ( 83 Sr) = 31.87 ± 1.16 (stat) ± 0.42 (sys) h; T 1/2 ( 85m Sr) = 68.24 ± 0.84 (stat) ± 0.11 (sys) min; T 1/2 ( 63 Zn) = 38.71 ± 0.25 (stat) ± 0.10 (sys) min. These high-precision half-life measurements will contribute to a more accurate determination of corresponding ground-state photoneutron reaction rates, which are part of a broader effort of constraining statistical nuclear models needed to calculate stellar nuclear reaction rates relevant for the astrophysical p -process nucleosynthesis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Single molecule characterization of anomalous transport in a thin, anisotropic film

Here, the diffusion of small, charged molecules incorporated in an anisotropic polyelectrolyte multilayers (PEMs) was tracked in three dimensions by combining single-molecule fluorescence localization (to characterize lateral diffusion) with Förster resonance energy transfer (FRET) between diffusing molecules and the supporting surface (to measure diffusion in the surface-normal direction). Analysis of the surface-normal diffusion required model-based statistical analysis to account for the inherently noisy FRET signal. Combining these distinct single-molecule methods, which are inherently sensitive to different length scales, permitted simultaneous characterization of severely anisotropic diffusion, which was more than three orders of magnitude slower in the surface-normal direction. We hypothesize that the anomalously slow surface-normal diffusion was related to the periodic distribution of charge in the PEM, which created electrostatic barriers. The motion was strongly subdiffusive, with anomalous temporal scaling exponents in lateral and normal directions, suggesting a connection to the transient, random fractal conformation of polymer chains in the film’s matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heterogeneous catalysis: Optimal performance at a phase boundary?

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. Here, we substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

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

The influence of temperature on the strain-hardening behavior of Fe-22/25/28Mn-3Al-3Si TRIP/TWIP steels

In this work, the influence of temperature and stacking fault energy (SFE) on the strain-hardening behavior and critical resolved shear stress for twinning was investigated for three Fe–22/25/28Mn–3Al–3Si wt.% transformation- and twinning-induced plasticity (TRIP/TWIP) steels. The SFEs were calculated by two different methods, density functional theory and statistical thermodynamic modeling. The dislocation structure, observed at low levels of plastic deformation, transitions from “planar” to “wavy” dislocation glide with an increase in temperature, Mn content, and/or SFE. The change in dislocation glide mechanisms from planar to wavy reduces the strain hardening rate, in part due to fewer planar obstacles and greater cross slip activity. Additionally, the alloys exhibit a large decrease in strength and ductility with increasing temperature from 25 to 200 °C, attributed to a substantial reduction in the thermally activated component of the flow stress, predominate suppression of TRIP and TWIP, and a significant increase in the critical resolved shear stress for mechanical twinning. Interestingly, the increase in SFE with temperature had a rather minor influence on the critical resolved shear stress for mechanical twinning, and other temperature dependent factors which likely play a more dominant role are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
DOE OSTI2026

Excited states in semi-magic 82 Ge populated in the βn-decay of 83 Ga

For this work, the βn and β-decay of the neutron-rich 83 Ga isotopes produced in proton-induced fission of 238 U were studied at the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Laboratory using on-line mass separation and the hybrid-3Hen neutron-γ detector array. Excited states in semi-magic 82 Ge populated in βn decay of 83 Ga were identified using neutron-γ coincidences. The measured relative population of excited states in 82 Ge is compared with a βn emission model, where the neutron emission model combines the strength distribution obtained from the shell-model calculations with the statistical Hauser-Feshbach model.

3HEN↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗