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

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗
DOE OSTI2025

Application of soot carbonization kinetics to deduce meaningful soot formation rates in premixed flat flames

Soot formation rates measured in fuel-rich premixed flat flames are frequently used to calibrate or validate chemical kinetic models of soot formation. Unfortunately, these flames feature an extended region of soot precursor particle inception and carbonization that complicates interpretation of soot measurements and leads to a fundamental inconsistency in the nature of the soot material that is modeled versus what is being measured when using non-intrusive, optical techniques. In the work presented here, previously reported data on two canonical sooting ethylene-air premixed flames at 1 atm pressure are interpreted via a new analysis approach that combines soot optical dispersion coefficient measurements with soot carbonization kinetics. This analytical approach explicitly accounts for the production of poorly ordered soot precursor particle mass and its carbonization over time in the flames, providing a clear distinction between the formation rate of precursor particles and their transformation into ordered, solid soot particulate mass. In particular, the results of the analysis show that the precursor particles form much earlier in the flame than the majority of the carbonized soot and their formation rate is two to three times faster than that of ordered soot. The results also show that particle agglomeration begins when the particles are at an intermediate state of carbonization. In conclusion, these results offer a valuable new interpretation of these important datasets and should lead to substantial improvements in the development and calibration of quantitative soot models.

Soot formation↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2022

Search for heavy resonances decaying to a pair of Lorentz-boosted Higgs bosons in final states with leptons and a bottom quark pair at $ \sqrt{s} $ = 13 TeV

A search for new heavy resonances decaying to a pair of Higgs bosons (HH) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented. Data were collected with the CMS detector at the LHC in 2016–2018, corresponding to an integrated luminosity of 138 fb - 1 . Resonances with a mass between 0.8 and 4.5 TeV are considered using events in which one Higgs boson decays into a bottom quark pair and the other into final states with either one or two charged leptons. Specifically, the single-lepton decay channel HH → b b ¯ WW * → b b ¯ ℓ v q q ¯ ' and the dilepton decay channels HH → b b ¯ WW * → b b ¯ ℓ v ℓ v and HH → b b ¯ ττ → b b ¯ ℓ vv ℓ vv are examined, where ℓ in the final state corresponds to an electron or muon. The signal is extracted using a two-dimensional maximum likelihood fit of the H → b b ¯ jet mass and HH invariant mass distributions. No significant excess above the standard model expectation is observed in data. Model-independent exclusion limits are placed on the product of the cross section and branching fraction for narrow spin-0 and spin-2 massive bosons decaying to HH. The results are also interpreted in the context of radion and bulk graviton production in models with a warped extra spatial dimension. The results provide the most stringent limits to date for X → HH signatures with final-state leptons and at some masses provide the most sensitive limits of all X → HH searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Cross-scale modeling and experimental integration for advancing cathode electrolyte interphase studies in high energy density lithium-ion batteries

Electrochemical interfaces are critical to the performance and durability of lithium-ion batteries (LIBs). The solid electrode-electrolyte interphase (SEI and CEI) structures that form during cycling can passivate reactive surfaces, ensuring safe operation, but also may contribute to performance degradation. Understanding the microscopic factors influencing interphase formation, growth, and evolution is essential for balanced battery design. While significant research has focused on the anode-electrolyte interphase (SEI), the cathode-electrolyte interphase (CEI) remains less explored, despite its importance in high-voltage and advanced battery technologies. Challenges in conducting in-situ or operando experiments arise from the occluded nature of these interfaces and the long timescales involved, often leading to biased interpretations. A validated multi-scale, multi-physics modeling approach, integrated with advanced characterization techniques, can effectively elucidate the intrinsic stability of electrolyte and cathode surfaces, the impact of chemical heterogeneity, and the role of microstructural features on CEI performance. In conclusion, this article reviews current modeling and simulation strategies for studying CEI in advanced LIBs and highlights opportunities for future methodological advancements and experimental integration.

Cathode-electrolyte interphase↗
DOE OSTI2021

A Visual Designer of Layer‐wise Relevance Propagation Models

Abstract Layer‐wise Relevance Propagation (LRP) is an emerging and widely‐used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a “best” LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance‐based visual analysis with two important functions: relevance‐based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

Huang, Xinyi↗
DOE OSTI2020

Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at $ \sqrt{s} $ = 13 TeV

A search for charged Higgs bosons (H$^{±}$) decaying into a top and a bottom quark in the all-jet final state is presented. The analysis uses LHC proton-proton collision data recorded with the CMS detector in 2016 at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{−1}$. No significant excess is observed above the expected background. Model-independent upper limits at 95% confidence level are set on the product of the H$^{±}$ production cross section and branching fraction in two scenarios. For production in association with a top quark, limits of 21.3 to 0.007 pb are obtained for H$^{±}$ masses in the range of 0.2 to 3 TeV. Combining this with a search in leptonic final states results in improved limits of 9.25 to 0.005 pb. The complementary s-channel production of an H$^{±}$ is investigated in the mass range of 0.8 to 3 TeV and the corresponding upper limits are 4.5 to 0.023 pb. These results are interpreted using different minimal supersymmetric extensions of the standard model.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Simultaneous Optimization of Crystal Plasticity Hardening Parameters

Crystal plasticity models relate macroscopic deformation behavior to the evolution of slip systems strength, but their parameterization is often non-unique, with multiple parameter sets being able to describe the same macroscopic behavior. To address this issue, the present work adopts a Bayesian optimization framework for the parameterization of face-centered cubic plasticity models while simultaneously considering multiple experimental datasets from the literature. For single crystal Cu, parameter optimization was guided by the tensile stress–strain curves along several crystallographic orientations, with an adequate fit being found for five orientations at once. While additional parameters allowed for the consideration of more physical mechanisms, like different slip system interaction strengths or misorientations inherent to the experimental data, the extra dimensionality was found to limit the efficiency of the global minimization procedure. For polycrystalline Ni, multiple grain sizes were considered together in a representative polycrystalline model, with the optimization able to reconcile the model with the data for three grain sizes at once. As meaningful interpretation of parameters relies on the uniqueness of their values, incorporating multiple datasets into this discerning parameterization procedure enables more robust prediction and application of crystal plasticity models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temperature-dependent spectral emittance of bauxite and silica particle beds

Bauxite and silica particles are candidate materials for solar thermal energy storage at high temperatures. The temperature-dependent emittance of packed beds with bauxite and silica particles was measured using a newly upgraded emissometer at wavelengths 2 μm ≤ λ ≤ 16 μm and temperatures up to ~730 K. The room-temperature emittance was obtained from the measured directional-hemispherical reflectance. A fused silica disc was used to test the emissometer by comparing the measured spectral emittance with the calculated emittance from a fitted Lorentz oscillator model. For the polycrystalline silica particles and the fused silica disc, the measured emittance increases with temperature in the mid-infrared region. The underlying mechanism is interpreted as the temperature-dependent damping coefficient in the Lorentz oscillator model. Two types of bauxite particles with different compositions and sizes were investigated. For λ > 10 μm, the measured emittance at elevated temperatures is higher than that at room temperature. In the region 2 μm < λ < 6 μm, the temperature dependence varies for different types of particles. The total emittance of bauxite particle beds was calculated by spectral integration using Planck’s distribution at the prescribed temperature. As a result, the calculated total emittance is between 0.89 and 0.96, but it does not change monotonically with temperature.

42 ENGINEERING↗
DOE OSTI2021

Infrared surface phonon nanospectroscopy of an interacting dielectric-particle–dielectric-substrate dimer using fast electrons

Refinements in energy monochromation and aberration correction in state-of-the-art scanning transmission electron microscopes has opened access to the far infrared regime for spectroscopic characterization at the nanoscale. At these low energies, the dielectric environment, such as a dielectric slab adjacent to the target specimen, may no longer play a passive role in the spectrum. Instead, the environment may itself host resonances that mix with those of the target and complicate interpretation of its spectral responses. This article explores a theoretical description of the coupling between the collective vibrational surface modes of a dielectric particle and dielectric slab of varying thickness for the purpose of elucidating the interacting phononic excitations in dielectric materials typical of inelastic electron scattering measurements in the infrared. Dynamical coordinates and a governing Hamiltonian are rigorously defined in the quasistatic limit to account for phonon mode mixing and forcing by an aloof electron probe, which travels along a grazing trajectory, parallel to the dielectric slab. As the spectral window of interrogation by fast electron probes has been extended down to thermal energies with unprecedented meV energy resolution, theoretical models like that presented herein are crucial for accurate interpretation of experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Varied phenomenology of models displaying dynamical large-deviation singularities

Singularities of dynamical large-deviation functions are often interpreted as the signal of a dynamical phase transition and the coexistence of distinct dynamical phases, by analogy with the correspondence between singularities of free energies and equilibrium phase behavior. Here we study models of driven random walkers on a lattice. These models display large-deviation singularities in the limit of large lattice size, but the extent to which each model's phenomenology resembles a phase transition depends on the details of the driving. We also compare the behavior of ergodic and nonergodic models that present large-deviation singularities. Finally, we argue that dynamical large-deviation singularities indicate the divergence of a model timescale, but not necessarily one associated with cooperative behavior or the existence of distinct phases.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. Furthermore, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

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

Effective field theory interpretation of lepton magnetic and electric dipole moments

We perform a model-independent analysis of the magnetic and electric dipole moments of the muon and electron. We give expressions for the dipole moments in terms of operator coefficients of the low-energy effective field theory (LEFT) and the Standard Model effective field theory (SMEFT). We use one-loop renormalization group improved perturbation theory, including the one-loop matching from SMEFT onto LEFT, and one-loop lepton matrix elements of the effective-theory operators. Semileptonic four-fermion operators involving light quarks give sizable non-perturbative contributions to the dipole moments, which are included in our analysis. We find that only a very limited set of the SMEFT operators is able to generate the current deviation of the magnetic moment of the muon from its Standard Model expectation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A multi-physics constitutive model to predict hydrolytic aging in quasi-static behaviour of thin cross-linked polymers

The effect of hydrolytic aging on mechanical quasi-static responses of rubber-like materials, in particular, the idealized Mullins effect and permanent set have been modeled. The effect of hydrolytic damage on the mechanical integrity of the polymer matrix is modeled as the direct competition of two micro-structural phenomena (i) chain scission and (ii) reduction of cross-links. Both phenomena and their correlation were modeled and thus, the strain energy of the polymer matrix is written with respect to three independent mechanisms; i) the shrinking original matrix that has not been attacked by water, ii) conversion of the first network to a new network due to the reduction of the cross-links, and iii) energy loss from network degradation due to water attacks to polymer active agents. The proposed model satisfies the Clausius-Duhem inequality and is thus physically feasible. The model is validated with respect to sets of our experimental data and other sets available in the literature. The proposed model is based on the assumption of homogeneous diffusion and mainly relevant for thin samples. In view of its accuracy, interpret-ability, and deep insight it provides into the nature of damage accumulation, the model is a good choice for further implementation in FE applications.

42 ENGINEERING↗
DOE OSTI2026

Global energy balance simulations of shattered pellet injection

The dynamics of shattered pellet injection (SPI) shutdowns are simulated using a time-dependent global energy balance model, based on a modification of the KPRAD framework. The new SPI particle source in the model calculates the ablation of individual pellet fragments that enter the plasma as a temporally resolved plume, thus capturing the effects of earlier fragments on the ablation of those that follow, which has a significant impact on the overall assimilation. Despite the reduced physics and the global averaging of all quantities, results from a large number of DIII-D, KSTAR, and JET experiments are well reproduced, including the plasma cooling timescales, particle assimilation, and current quench (CQ) rates. Cooling timescales and CQ rates are in good agreement for pellets containing as little as ∼1% neon, while particle assimilations are most accurate for neon fractions above ∼15% by number of atoms. Below this, the assimilation tends to be overestimated due to the lack of radial transport in the particle balance, which becomes important in the low-Z limit. Predictive simulations of mixed-composition dual-SPI shutdowns in ITER are compared against those with the 3D non-linear magnetohydrodynamic code JOREK and are found to reproduce overall trends observed in the higher-fidelity modeling across a range of injection scenarios. The general success of the model points to the critical role of energy balance in determining SPI particle assimilation and the subsequent disruption dynamics and highlights the value of these simulations for experimental interpretation and for optimizing the deployment of computationally expensive, higher-fidelity models.

energy balance model↗
DOE OSTI2022

A Physics-Based DNI Model for Advancing Solar Resource Assessment and Forecasting: Preprint

Direct Normal Irradiance (DNI) is one of the most used quantities to quantify the magnitude of solar energy resource. The concept of DNI is often interpreted differently for ground measurements and solar forecasting by numerical weather prediction (NWP) models, leading to substantial bias during evaluation of DNI forecasts especially under cloudy-sky conditions. To eliminate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to provide a physics-based solution of solar radiation in the circumsolar region. The FARMS-DNI is implemented in the Weather Research and Forecasting model with solar extensions (WRF-Solar) to forecast day-ahead DNI in the north America. By comparing with conventional predictions from WRF-Solar and satellite observations from the National Solar Radiation Data Base (NSRDB), we found significant improvements in our prediction of DNI.

DNI↗
DOE OSTI2023

Neutron Skin Measurement of 208Pb and 48Ca Using Parity Violating Electron Scattering

Parity-violating electron scattering experiments (PVES) provides a clean probe of neutron densities that is model independent and free from strong interaction uncertainties in interpretation. The PREX-2 and CREX experiments were run in 2019 and 2020 at Jefferson laboratory measured the nucleon skin thickness, the difference between the r.m.s. neutron radius Rn and the r.m.s. proton radius Rp, of 208Pb and 48Ca via parity violating electroweak asymmetry in the elastic scattering of longitudinally polarized electrons. PREX-2 experiment was performed with 950 MeV electrons scattered at a 5¿ angle with Q2 = 0.00616 ± 0.00004 (GeV/c)2, while CREX used 2182 MeV electrons at the same angle with Q2 = 0.0297 ± 0.0002 (GeV/c)2. For PREX-2 the measured asymmetry was AP V = 550 ± 16 (stat.) ± (syst.) ppb, which corresponds to Rskin = 0.278 ± 0.078 (exp.) ± 0.012 (theo.) fm. The CREX asymmetry was AP V = 2668 ± 106 (stat.) ± 40 (syst.) ppb, which corresponds to Rskin = 0.121 ± 0.026 (exp.) ±0.024 (model) fm. One of the crucial systematic uncertainty that PREX-2 and CREX were sensitive to was the non-parity violating asymmetries that resulted from the helicity correlated false asymmetries in the polarized electron beam. There was a lot of work put towards understanding and suppressing the asymmetries arising from these effects. The parity violating asymmetry measurement required a very precise determination of the electron beam polarization. To accurately determine the beam polarization, a Compton polarimeter was used during both PREX-2 and CREX. A careful alignment of the laser to the Fabry-Perot cavity, data analysis and systematic control was employed to get a precise beam polarization result for the experiments. The PREX-2 measurement has broad implications for increasing our knowledge about neutron star structure and the equation of state of nuclear matter. The combined PREX and CREX results will have implications for future energy density functional calculations and the theory of nuclear structure.

Premathilake, Sachinthani↗