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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 955 records · Page 53

Open Data, Models, and Codes for Vanadium Redox Batch Cell Systems: A Systems Approach Using Zero-Dimensional Models

Abstract In this paper, we study, analyze, and validate some important zero-dimensional physics-based models for vanadium redox batch cell (VRBC) systems and formulate an adequate physics-based model that can predict the battery performance accurately. In the model formulation process, a systems approach to multiple parameters estimation has been conducted using VRBC systems at low C-rates (~C/30). In this batch cell system, the effect of ions' crossover through the membrane is dominant, and therefore, the capacity loss phenomena can be explicitly observed. Paradoxically, this means that using the batch system might be a better approach for identifying a more suitable model describing the effect of ions transport. Next, we propose an efficient systems approach, which enables to help understand the battery performance quickly by estimating all parameters of the battery system. Finally, open source codes, executable files, and experimental data are provided to enable people's access to robust and accurate models and optimizers. In battery simulations, different models and optimizers describing the same systems produce different values of the estimated parameters. Providing an open access platform can accelerate the process to arrive at robust models and optimizers by continuous modification from the users' side.

25 ENERGY STORAGE↗

Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data

This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb -1 of LHC proton–proton collision data taken at centre-of-mass energies of √s=7 and 8 TeV. The reconstruction of electron and photon energies is optimised using multivariate algorithms. The response of the calorimeter layers is equalised in data and simulation, and the longitudinal profile of the electromagnetic showers is exploited to estimate the passive material in front of the calorimeter and reoptimise the detector simulation. After all corrections, the Z resonance is used to set the absolute energy scale. For electrons from Z decays, the achieved calibration is typically accurate to 0.05 % in most of the detector acceptance, rising to 0.2 % in regions with large amounts of passive material. The remaining inaccuracy is less than 0.2–1 % for electrons with a transverse energy of 10 GeV, and is on average 0.3 % for photons. The detector resolution is determined with a relative inaccuracy of less than 10 % for electrons and photons up to 60 GeV transverse energy, rising to 40 % for transverse energies above 500 GeV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Measurement of the muon reconstruction performance of the ATLAS detector using 2011 and 2012 LHC proton–proton collision data

This study presents the performance of the ATLAS muon reconstruction during the LHC run with pp collisions at √s = 7–8 TeV in 2011–2012, focusing mainly on data collected in 2012. Measurements of the reconstruction efficiency and of the momentum scale and resolution, based on large reference samples of J/ψ → μμ, Z → μμ and Υ → μμ decays, are presented and compared to Monte Carlo simulations. Corrections to the simulation, to be used in physics analysis, are provided. Over most of the covered phase space (muon |η| < 2.7 and 5 ≲ p T ≲ 100 GeV) the efficiency is above 99% and is measured with per-mille precision. The momentum resolution ranges from 1.7% at central rapidity and for transverse momentum p T ≃ 10 GeV, to 4% at large rapidity and p T ≃ 100 GeV. The momentum scale is known with an uncertainty of 0.05% to 0.2% depending on rapidity. A method for the recovery of final state radiation from the muons is also presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of the top-quark mass in the fully hadronic decay channel from ATLAS data at $\sqrt{s}=7\mathrm{\,TeV}$

In this study, the mass of the top quark is measured in a data set corresponding to 4.6 fb -1 of proton–proton collisions with centre-of-mass energy √s=7 TeV collected by the ATLAS detector at the LHC. Events consistent with hadronic decays of top–antitop quark pairs with at least six jets in the final state are selected. The substantial background from multijet production is modelled with data-driven methods that utilise the number of identified b-quark jets and the transverse momentum of the sixth leading jet, which have minimal correlation. The top-quark mass is obtained from template fits to the ratio of three-jet to dijet mass. The three-jet mass is calculated from the three jets produced in a top-quark decay. Using these three jets the dijet mass is obtained from the two jets produced in the W boson decay. The top-quark mass obtained from this fit is thus less sensitive to the uncertainty in the energy measurement of the jets. A binned likelihood fit yields a top-quark mass of m t =175.1±1.4(stat.) ±1.2(syst.) GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at $\sqrt{s} = 7$ and 8 TeV in the ATLAS experiment

Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the H → γγ,ZZ*,WW*,Zγ, $b\bar{b}$,ττ and μμ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 fb- 1 at s√ = 7 TeV and 20.3 fb -1 at s√ = 8 TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is 1.18 +0.15 -0.14 . The observed Higgs boson production and decay rates are interpreted in a leading-order coupling framework, exploring a wide range of benchmark coupling models both with and without assumptions on the Higgs boson width and on the Standard Model particle content in loop processes. The data are found to be compatible with the Standard Model expectations for a Higgs boson at a mass of 125.36 GeV for all models considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A measurement of the calorimeter response to single hadrons and determination of the jet energy scale uncertainty using LHC Run-1 pp-collision data with the ATLAS detector

A measurement of the calorimeter response to isolated charged hadrons in the ATLAS detector at the LHC is presented. This measurement is performed with 3.2 nb –1 of proton–proton collision data at √s = 7 TeV from 2010 and 0.1 nb –1 of data at √s = 8 TeV from 2012. A number of aspects of the calorimeter response to isolated hadrons are explored. After accounting for energy deposited by neutral particles, there is a 5% discrepancy in the modelling, using various sets of Geant4 hadronic physics models, of the calorimeter response to isolated charged hadrons in the central calorimeter region. The description of the response to anti-protons at low momenta is found to be improved with respect to previous analyses. The electromagnetic and hadronic calorimeters are also examined separately, and the detector simulation is found to describe the response in the hadronic calorimeter well. The jet energy scale uncertainty and correlations in scale between jets of different momenta and pseudorapidity are derived based on these studies. The uncertainty is 2–5% for jets with transverse momenta above 2 TeV, where this method provides the jet energy scale uncertainty for ATLAS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Search for Higgs boson pair production in the γγWW* channel using $pp$ collision data recorded at $\sqrt{s}$ $=13$ TeV with the ATLAS detector

Searches for non-resonant and resonant Higgs boson pair production are performed in the γγWW* channel with the final state of γγℓνjj using 36.1 fb -1 of proton–proton collision data recorded at a centre-of-mass energy of √s= 13 TeV by the ATLAS detector at the Large Hadron Collider. No significant deviation from the Standard Model prediction is observed. A 95% confidence-level observed upper limit of 7.7 pb is set on the cross section for non-resonant production, while the expected limit is 5.4 pb. A search for a narrow-width resonance X decaying to a pair of Standard Model Higgs bosons HH is performed with the same set of data, and the observed upper limits on σ(pp→X) × B(X→HH) range between 40.0 and 6.1 pb for masses of the resonance between 260 and 500 GeV, while the expected limits range between 17.6 and 4.4 pb. When deriving the limits above, the Standard Model branching ratios of the H→γγ and H→WW* are assumed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of the top quark mass in the $t\bar{t}\rightarrow $ lepton+jets channel from $\sqrt{s}=8$ TeV ATLAS data and combination with previous results

The top quark mass is measured using a template method in the $t\bar{t}$→ lepton+jets channel (lepton is e or μ) using ATLAS data recorded in 2012 at the LHC. The data were taken at a proton–proton centre-of-mass energy of √s = 8 TeV and correspond to an integrated luminosity of 20.2 fb –1 . The $t\bar{t}$ → lepton+jets channel is characterized by the presence of a charged lepton, a neutrino and four jets, two of which originate from bottom quarks (b). Exploiting a three-dimensional template technique, the top quark mass is determined together with a global jet energy scale factor and a relative b-to-light-jet energy scale factor. The mass of the top quark is measured to be m top = 172.08 ± 0.39(stat) ± 0.82(syst) GeV. A combination with previous ATLAS m top measurements gives m top = 172.69 ± 0.25(stat) ± 0.41(syst) GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Neuromorphic computing for temporal scientific data classification

In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required.

Schuman, Catherine↗

Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism

Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation dates to the 1920s with the Shewhart control chart; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual = data − prediction. This paper reviews alarm threshold estimation, introduces model selection options, and considers a range of assumptions regarding the data-generating mechanism for PM residuals. Two PM examples from nuclear safeguards are included to motivate the need for alarm threshold estimation. The first example involves mixtures of probability distributions that arise in solution monitoring, which is a common type of PM. The second example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals.

Burr, Tom↗

Data Feature Extraction for High-Rate 3-Phase Data

This algorithm processes high-rate 3-phase signals to identify the start time of each signal and estimate its envelope as data features. The start time and magnitude of each signal during the steady state is also extracted. The features can be used to detect abnormal signals. This algorithm is developed to analyze Exxeno's 3-phase voltage and current data recorded from refrigeration systems to detect device failure or degradation.

Source record↗

Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.

Software↗

Data for Propagation Method and Planting Density Influence Canopy Developmental Transition and Biomass Productivity in Miscanthus × giganteus

Understanding how establishment practices influence the mechanisms underlying Miscanthus × giganteus (miscanthus) productivity and canopy development is critical for optimizing management. Data was collected during the juvenile (2011–2013) and mature (2024) phases of a long-term field experiment established in Urbana, Illinois, to evaluate the effects of propagation method (plug propagation [PP] and rhizome propagation [RP]), planting density (1.0, 0.75, and 0.25 plants m⁻²), and nitrogen application (0 and 67 kg N ha⁻¹) on end-of-season biomass yield, tiller mass, tiller density, and tiller height. Linear regression models identified the dominant predictors of yield across stand ages and management regimes. Planting density, nitrogen (N) application, and propagation method significantly influenced early yield and canopy development. During the juvenile phase, biomass yield was driven by tiller density due to canopy expansion; in the mature phase, yield became driven by tiller mass. The PP plots produced higher tiller density than the RP plots, resulting in faster canopy closure and higher juvenile-phase yields. Rhizome-propagated (RP) plots produced lower tiller density, but individual tillers were 3.3–6.4 g tiller−1 heavier than PP tillers. After the canopy reached equilibrium, the PP and RP yields were similar because greater RP tiller mass compensated for its lower tiller density. Higher planting density resulted in greater yield and tiller density during the second year (2012), but this effect was absent from the third year (2013) onward. In the juvenile phase, N fertilization enhanced yield by 1.6–3.4 Mg ha−1. Initiating fertilization in 2013 on unfertilized plots produced biomass similar to that in fertilized plots, suggesting yield recovery in the mature phase. These findings revealed that establishment strategies, including propagation method and planting density, influence juvenile miscanthus canopy development and productivity, transitioning from tiller-density- to mass-dominated yields, but not mature phase productivity.

Miscanthus↗

Data for Creating Yellow Seed Camelina sativa with Enhanced Oil Accumulation by CRISPR-Mediated Disruption of Transparent Testa 8

Camelina ( Camelina sativa L.), a hexaploid member of the Brassicaceae family, is an emerging oilseed crop being developed to meet the increasing demand for plant oils as biofuel feedstocks. In other Brassicas, high oil content can be associated with a yellow seed phenotype, which is unknown for camelina. We sought to create yellow seed camelina using CRISPR/Cas9 technology to disrupt its Transparent Testa 8 (TT8) transcription factor genes and to evaluate the resulting seed phenotype. We identified three TT8 genes, one in each of the three camelina subgenomes, and obtained independent CsTT8 lines containing frameshift edits. Disruption of TT8 caused seed coat colour to change from brown to yellow reflecting their reduced flavonoid accumulation of up to 44%, and the loss of a well-organized seed coat mucilage layer. Transcriptomic analysis of CsTT8-edited seeds revealed significantly increased expression of the lipid-related transcription factors LEC1, LEC2, FUS3, and WRI1 and their downstream fatty acid synthesis-related targets. These changes caused metabolic remodelling with increased fatty acid synthesis rates and corresponding increases in total fatty acid (TFA) accumulation from 32.4% to as high as 38.0% of seed weight, and TAG yield by more than 21% without significant changes in starch or protein levels compared to parental line. These data highlight the effectiveness of CRISPR in creating novel enhanced-oil germplasm in camelina. The resulting lines may directly contribute to future net-zero carbon energy production or be combined with other traits to produce desired lipid-derived bioproducts at high yields.

Biofuels↗

Data for NB6 HBRR Science Design ORNL/TM-2025/3807

Data for the report (ORNL/TM-2025/3807) that describes the calculations and the Monte Carlo Ray Tracing simulations performed using the McStas package to determine the coatings and geometry for the NB-6 guide. It provides the information to inform the mechanical design, validation tests and verification that it meets the science requirements.

47 OTHER INSTRUMENTATION↗