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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nucleon helicity generalized parton distribution at physical pion mass from lattice QCD

The generalized parton distributions (GPDs) offer a window on three-dimensional imaging of the nucleon, providing understanding of how the fundamental properties of the nucleon, such as its mass and spin, arise from the underlying quark and gluon degrees of freedom. In this work, we present the first lattice calculation of the nucleon isovector helicity GPD at physical pion mass, using an a≈0.09 fm lattice ensemble with 2+1+1 flavors of highly improved staggered quarks generated by MILC Collaboration. We perform the GPD calculation in Breit frame using averaged nucleon boost momentum P z ≈2.2 GeV with nonzero momentum transfers in [0.2,1.0] GeV 2 . Nonperturbative renormalization in RI/MOM scheme is used to obtain the quasi-distribution before matching to the lightcone GPDs. The three-dimensional distribution $\tilde{H}$(x, Q 2 ) is presented, along with the three-dimensional nucleon tomography and impact-parameter-dependent distribution for selected Bjorken x at μ=3 GeV in $\overline{MS}$ scheme.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Directly probing the Higgs-top coupling at high scales

We explore the sensitivity to new physics for the coupling of the Higgs boson (h) and top quark (t) at high energy scales with the process ${pp}$→ $t\bar{t}h$ at the high-luminosity LHC. This process probes the coupling in both the space-like and time-like domains at a high scale, complementary to the off-shell Higgs processes in the time-like domain. The effects from physics beyond the Standard Model are parametrized in terms of the effective field theory framework and a non-local Higgs-top form factor. Focusing on the boosted Higgs regime in association with jet substructure techniques, we show that the present search can directly probe the Higgs-top coupling to good precision, providing a strong sensitivity to the new physics scale

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state in proton-proton collisions at √s = 13 TeV

A search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state is presented. The analysis is based on proton-proton collision data recorded by the CMS detector in 2016–2018 at a centre-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 138 fb –1 . The search is sensitive to resonances with masses between 1.3 and 6 TeV, decaying to bosons that are highly Lorentz-boosted such that each of the bosons forms a single large-radius jet. Machine learning techniques are employed to identify such jets. No significant excess over the estimated standard model background is observed. A maximum local significance of 3.6 standard deviations, corresponding to a global significance of 2.3 standard deviations, is observed at masses of 2.1 and 2.9 TeV. In a heavy vector triplet model, spin-1 Z' and W' resonances with masses below 4.8 TeV are excluded at the 95% confidence level (CL). These limits are the most stringent to date. In a bulk graviton model, spin-2 gravitons and spin-0 radions with masses below 1.4 and 2.7 TeV, respectively, are excluded at 95% CL. Production of heavy resonances through vector boson fusion is constrained with upper cross section limits at 95% CL as low as 0.1 fb.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Measurement of the production cross section of a Higgs boson with large transverse momentum in its decays to a pair of τ leptons in proton-proton collisions at s = 13 TeV

A measurement of the production cross section of a Higgs boson with transverse momentum greater than 250GeV is presented where the Higgs boson decays to a pair of τ leptons. It is based on proton-proton collision data collected by the CMS experiment at the CERN LHC at a center-of-mass energy of 13TeV. The data sample corresponds to an integrated luminosity of 138 fb − 1 . Because of the large transverse momentum of the Higgs boson the τ leptons from its decays are boosted and produced spatially close, with their decay products overlapping. Therefore, a dedicated algorithm was developed to reconstruct and identify them. The observed (expected) significance of the measured signal with respect to the standard model background-only hypothesis is 3.5 (2.2) standard deviations. The product of the production cross section and branching fraction is measured to be 1.64 − 0.54 + 0.68 times the standard model expectation. The fiducial differential production cross section is also measured as functions of the Higgs boson and leading jet transverse momenta. This measurement extends the probed large-transverse-momentum region in the ττ final state beyond 600GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Experimental impact of jet fragmentation reference frames at particle colliders

In collider physics, the properties of hadronic jets are often measured as a function of their lab-frame momenta. However, jet fragmentation must occur in a particular rest frame defined by all color-connected particles. Since this frame may not be the lab frame, the fragmentation of a jet depends on the properties of its sibling objects. This non-factorizability of jets has consequences for experimental jet techniques such as jet tagging, boosted boson measurements, and searches for physics Beyond the Standard Model. In this paper, we will describe the effect and show its impact as predicted by simulation.

Fragmentation into hadrons↗
DOE OSTI2025

Search for Higgs boson decays into a Z boson and a light hadronically decaying resonance in pp collisions at s =13 TeV with the ATLAS detector

A search for decays of the Higgs boson into a Z boson and a light resonance, with a mass of 0.5–3.5 GeV, is performed using the full 140 fb−1 dataset of 13 TeV proton–proton collisions recorded by the ATLAS detector during LHC Run 2. Leptonic decays of the Z boson and hadronic decays of the light resonance are considered. The resonance can be interpreted as a J / ψ or η c meson, an axion-like particle, or a light pseudoscalar predicted in two-Higgs-doublet models. Due to its low mass, this resonance is produced with a high Lorentz boost in the laboratory frame and therefore reconstructed as a single small-radius jet of hadrons. A neural network is used to correct the Monte Carlo simulation of the total expected background using data from sideband regions. Two additional neural networks are used to distinguish signal from background, enhancing the purity of the signal region. A binned profile-likelihood fit is performed on the final-state invariant mass distribution. No significant excess of events relative to the expected background is observed, and upper limits at 95% confidence level are set on the Higgs boson's branching fraction to a Z boson and a light resonance. The exclusion limit is ∼10% for the lower masses, and increases for higher masses. Upper limits on the effective coupling C Z H eff / Λ of an axion-like particle to a Higgs boson and Z boson are also set at 95% confidence level, and range from 0.9 to 2 TeV−1.

Aad, G↗
DOE OSTI2025

Review of top quark mass measurements in CMS

The top quark mass is one of the most intriguing parameters of the standard model (SM). Its value indicates a Yukawa coupling close to unity, and the resulting strong ties to Higgs physics make the top quark mass a crucial ingredient for understanding essential aspects of the electroweak sector of the SM. This review offers the first comprehensive overview of the top quark mass measurements performed by the CMS Collaboration using the data collected at centre-of-mass energies of 7, 8, and 13 TeV. Moreover, a detailed description of the top quark event reconstruction is provided and dedicated studies of the dominant uncertainties in the modelling of the signal processes are discussed. The interpretation of the experimental results on the top quark mass in terms of the SM Lagrangian parameter is challenging and is a focus of an ongoing discussion in the theory community. The CMS Collaboration has performed two main types of top quark mass measurements, addressing this challenge from different perspectives: highly precise ‘direct’ measurements, based on reconstructed top quark decay products and relying exclusively on Monte-Carlo simulations, as well as ‘indirect’ measurements, where the simulations are employed to determine parton-level cross sections that are compared to fixed-order perturbative calculations. Recent mass extractions using Lorentz-boosted top quarks open a new avenue of measurements based on top quark decay products contained in a single particle jet, with promising prospects for accurate theoretical interpretations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Asynchronous GPU-based DEM solver embedded in commercial CFD software with polyhedral mesh support

A novel graphical processing unit-based discrete element method solver is introduced to improve stability, performance, and provide seamless integration into commercial or open-source computational fluid dynamics software. A key innovation is eliminating a need for network communication between solvers, which was previously required for cross-platform coupling. This is accomplished by a direct coupling method that employs dynamic-linked libraries. Furthermore, the solver optimizes memory usage by streamlining the particle-cell search algorithm by eliminating the cells' searching grid. This ensures the solver is compatible with a wide range of mesh types, providing high geometric flexibility. The approach simplifies the simulation process by directly incorporating computational fluid dynamics mesh information into the discrete element method solver. The performance analysis indicates about sixteen times boost in computational speed compared to benchmark central processing unit-based solvers. Finally, the solver's compatibility with polyhedral meshes, a vital advantage for complex geometries, is tested against a referenced study regarding the simulation of an immersed-tube fluidized bed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

An improved detailed chemical kinetic model for C3-C4 linear and iso-alcohols and their blends with gasoline at engine-relevant conditions

Propanol and butanol isomers have received significant research attention as promising fuel additives or neat biofuels. Robust chemical kinetic models are needed that can provide accurate and efficient predictions of combustion performance across a wide range of engine relevant conditions. Herein, this study seeks to improve the understanding of ignition and combustion behavior of pure C3-C4 linear and iso-alcohols, and their blends with gasoline at engine-relevant conditions. In this work, a kinetic model with improved thermochemistry and reaction kinetics was developed based on recent theoretical calculations of H-atom abstraction and peroxy radical reaction rates. Kinetic model validations are reported, and the current model reproduces the ignition delay times of the C3 and C4 alcohols well. Variations in reactivity over a wide range of temperatures and other operating conditions are also well predicted by the current model. Recent ignition delay time measurements from a rapid compression machine of neat iso-propanol and iso-butanol [Cheng et al., Proc. Combust Inst. (2020)] and blends with a research grade gasoline [Goldsborough et al., Proc. Combust Inst. (2020)] at elevated pressure (20–40 bar) and intermediate temperatures (780–950 K) were used to demonstrate the accuracy of the current kinetic model at conditions relevant to boosted spark-ignition engines. The effects of alcohol blending with gasoline on the autoignition behavior are discussed. The current model captures the suppression of reactivity in the low-temperature and negative-temperature-coefficient (NTC) region when either isopropanol and isobutanol are added to a research grade gasoline. Sensitivity and reaction flux analysis were performed to provide insights into the relevant fuel chemistry of the C3-C4 alcohols.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Effects of isoalcohol blending with gasoline on autoignition behavior in a rapid compression machine: Isopropanol and isobutanol

Alcohols, and particularly isoalcohols, are potentially advantageous blendstocks towards achieving efficient, low-carbon intensity internal combustion engines. Their use in advanced configurations, such as boosted spark-ignition or spark-assisted compression ignition, requires a comprehensive understanding of their blending effects on the low- and intermediate-temperature autoignition behavior of petroleum-derived gasoline. This work reports an experimental and modeling study of such autoignition characteristics quantified in a twin-piston rapid compression machine. Isopropanol and isobutanol are blended into a research-grade gasoline (FACE-F) at oxygenate blend levels of 0 to 30% vol/vol, with tests conducted at pressures of 20 and 40 bar, temperatures from 700 to 1000 K, and dilute stoichiometric fuel loadings. Changes to overall reactivity, including first-stage and main ignition times, and preliminary exothermicity are established, with comparisons made to previous measurements with ethanol-blended FACE-F gasoline. Furthermore, it is found that at low-temperature/NTC conditions (700–860 K) the isoalcohols suppress first-stage reactivity and associated heat release while main ignition times are extended. At NTC/intermediate-temperature (860–1000 K) conditions changes to fuel reactivity are less significant with isopropanol slightly suppressing reactivity and isobutanol promoting ignition. Detailed chemical kinetic modeling is used to interpret the experimental measurements. Overall trends of suppression or promotion in the blending behavior are reasonably captured by the model. Sensitivity and rate of production analyses indicate that at lower temperatures H-atom abstraction reactions from the surrogate fuel molecules (e.g., cyclopentane, isooctane) and the isoalcohols via OH are important leading to TC 3 H 6 OH and IC 4 H 8 OH–C radicals, for isopropanol and isobutanol respectively, which act as scavengers in the system. At higher temperatures, similar chemistries are dominant, but there is an increasing importance of abstraction by HO 2 . The kinetic modeling also indicates that the promoting effect of isobutanol at higher temperatures is due to the increased abstractions at the γ-sites, while at lower temperatures abstraction at the α-site leads to greater reactivity suppression.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An experimental and modeling study on autoignition of 2-phenylethanol and its blends with n-heptane

2-Phenylethanol (2-PE) is an aromatic alcohol with high research octane number, high octane sensitivity, and a potential to be produced using biomass. Considering that 2-PE can be used as a fuel additive for boosting the anti-knocking quality of gasoline in spark-ignition engines and as the low reactivity fuel or fuel component in dual-fuel reactivity controlled compression ignition (RCCI) engines, it is of fundamental and practical interest to understand the autoignition chemistry of 2-PE, especially at low-to-intermediate temperatures (<1000 K). Based upon the experimental ignition delay time (IDT) results of neat 2-PE obtained from our previous rapid compression machine (RCM) investigation and the literature shock tube study, a detailed chemical kinetic model of 2-PE is developed herein, covering low-to-high temperature regimes. Besides, RCM experiments using binary fuel blends of 2-PE and n-heptane (nC7) are conducted in this work to investigate the nC7/2-PE blending effects, as they represent a dual-fuel system for RCCI operations. Furthermore, the newly developed 2-PE model is merged with a well-validated nC7 kinetic model to generate the current nC7/2-PE binary blend model. Overall, the consolidated model reasonably predicts the experimental IDT data of neat 2-PE and nC7/2-PE blends, as well as captures the experimental effects of pressure, equivalence ratio, and blending ratio on autoignition. Finally, model-based chemical kinetic analyses are carried out to understand and identify the controlling chemistry accounting for the observed blending effects in RCM experiments. In conclusion, the analyses reveal that nC7 enhances 2-PE autoignition via providing extra $\dot{O}$H radicals to the shared radical pool, while the diminished nC7 promoting effect on 2-PE autoignition with increasing temperature is due to the negative temperature coefficient characteristics of nC7.

2-phenlyethanol↗
DOE OSTI2026

A comparative study of multimodal data fusion strategies for planetary spectroscopy

Integrating heterogeneous data sources can improve scientific inference when different modalities capture complementary information, but doing so is challenging in high-dimensional, small-sample settings. In spectroscopy for planetary exploration, Laser-Induced Breakdown Spectroscopy (LIBS), Raman Spectroscopy (Raman), Visible Infrared Spectroscopy (VISIR), and Mid-Infrared Spectroscopy (MIR) each examine different aspects of composition and mineralogy, raising fundamental questions about when and how data fusion improves predictive performance. Using a Mars-relevant set of geologic standards with measurements from all four modalities, we present a rigorous systematic evaluation of four data fusion strategies: low-level (data) fusion, mid-level (feature) fusion, high-level (decision) fusion, and residual-boosting (sequential) fusion. We assess performance in predicting oxide composition via nested cross-validation and corrected significance testing to evaluate whether data fusion improves upon single-modality baselines. We show that data fusion does not uniformly improve accuracy, and that observed gains are modest, oxide-dependent, and sensitive to modality and model structure. To move beyond aggregate accuracy metrics, we use model coefficients, permutation importance, and residual gain analysis to examine how the fusion models weight individual modalities and to identify patterns of apparent complementarity or redundancy. Though focused on spectroscopy for planetary exploration, our framework for data fusion evaluation and interpretation extends to other scientific domains with heterogeneous and scarce data and provides a principled approach evaluating data fusion strategies, interpreting modality contributions, and understanding tradeoffs among data fusion strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Projecting Life-Cycle Environmental Impacts of Corn Production in the U.S. Midwest under Future Climate Scenarios using machine learning approach

Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanism models, capable of capturing the heterogeneity, tend to be very complicated, and time-consuming. Efficient predictions of life-cycle environmental impacts from agricultural production are lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate current and future life-cycle environmental impacts of corn production in 12 U.S. Midwest at county scale under four Intergovernmental Panel on Climate Change (IPCC)’s climate scenarios (RCP2.6. 4.5, 6.0, and 8.5) for years 2009-2100. Results show that future life-cycle GW, EU and AD impacts of corn production will increase under all four scenarios, with the worst environmental impacts shown under the RCP 8.5 scenario. Significant spatial variation were estimated to be 3.6, 29.9 and 1.8-folds under the RCP4.5 scenario, and 6.6, 152.9 and 1.6-folds under the RCP8.5 scenario, respectively. Findings from this study demonstrate the importance of considering county-level life-cycle environmental impacts and the influences of climate change in future years to aid in developing potential adaptation and mitigation programs and policies.

Machine learning, climate change, projections, env↗
DOE OSTI2025

Evaluating system responses to electric vehicle charging infrastructure expansion through data-driven simulation

Understanding the system responses to electric vehicle (EV) charging infrastructure expansion, including vehicle charging needs, station utilization, and energy consumption, is critical for effective planning to meet growing charging demand without unnecessary resource investment. This study evaluates the system responses to EV charging infrastructure expansion, focusing on charging needs, station utilization, and energy consumption. Using trip data from the National Household Travel Survey and origin–destination patterns, we simulated trip chains in downtown Atlanta with 10 % EV penetration. We assessed 32 scenarios involving different charging port power levels and siting strategies. Furthermore, we found that higher-power ports were more sensitive to placement, with concentrated expansion boosting station utilization more than uniform expansion. Adding high-power ports did not always increase peak energy consumption; in some cases, a few 400 kW ports reduced overall consumption compared to 150 kW ports by enabling faster charging and higher vehicle turnover.

Electric vehicle↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
DOE OSTI2022

Investigating the Role of Copper in Arsenic Doped Cd(Se,Te) Photovoltaics

The open circuit voltage (VOC) deficit in Cd(Se,Te)-based photovoltaics remains a critical obstacle for pushing the technology closer to theoretical performance limits. Arsenic doping has become a dominant and promising route to achieve the higher p-type carrier concentrations necessary for higher VOC, but challenges associated with this alternate defect chemistry and higher doping density have hindered progress. Here we show that while arsenic doping enables high carrier concentrations (>1016 cm-3), co-doping with copper can provide a boost to VOC without a significant change to carrier concentration. A large data set is initially used to explore current-voltage and capacitance-voltage trends associated with arsenic doped devices with and without copper. A smaller subset is then used to probe these trends using a wide variety of characterization techniques. Copper is found to facilitate reduced interface recombination and potentially improved bulk absorber characteristics, though the mechanisms for these improvements are not yet clear. Despite the improved performance of co-doped devices, VOC is still far below its potential especially for highly doped devices. Low emitter doping in conjunction with high absorber doping seems to be a plausible cause for this significant deficit, though other device properties may exacerbate this problem.

CdSeTe↗