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Results for “Model discrepancy”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

On the extraction of laminar flame speed and Markstein length from outwardly propagating spherical flames

Large discrepancies among the laminar flame speeds and Markstein lengths of methane/air mixtures measured by different researchers using the same constant-pressure spherical flame method are observed. As an effort to reduce these discrepancies, one linear model (LM, the stretched flame speed changes linearly with the stretch rate) and two non-linear models (NM I and NM II, the stretched flame speed changes non-linearly with the stretch rate) for extracting the laminar flame speed and Markstein length from propagating spherical flames are investigated. The accuracy and performance of the LM, NM I, and NM II are found to strongly depend on the Lewis number. It is demonstrated that NM I is the most accurate for mixtures with large Lewis number (positive Markstein length) while NM II is the most accurate for mixtures with small Lewis number (negative Markstein length). Therefore, in order to get accurate laminar flame speed and Markstein length from spherical flame experiments, different non-linear models should be used for different mixtures. The validity of the theoretical results is further demonstrated by numerical and experimental studies. The results of this study can be used directly in spherical flame experiments measuring the laminar flame speed and Markstein length. (author)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Experimental study of energy-dependent angular broadening of MeV electron beams for high-resolution imaging in thick samples

In scanning transmission electron microscopy (STEM), spatial resolution is primarily influenced by the projected size of the electron probe within the specimen. In thin samples, a large semi-convergence angle enables a tightly focused beam and sub-nanometer resolution. However, in thick specimens, resolution is fundamentally limited by transverse beam broadening from multiple large-angle scattering events—for example, a probe with 10 mrad angular divergence can broaden by ∼100 nm over a 10 μm path. Since this broadening scales inversely with beam energy, MeV-STEM offers a promising route for high-resolution imaging in thick materials. To quantitatively assess this effect, we performed high-precision measurements at UCLA’s PEGASUS beamline, characterizing beam divergence and intensity profiles for 3–8 MeV electrons transmitted through a wedged-silicon sample of varying thickness. Our results reconcile discrepancies among analytical models and validate Monte Carlo simulations. Here, we find that increasing beam energy from 3.0 to 5.8 MeV reduces angular broadening by a factor of 2.6, with diminishing returns observed at 7.6 MeV. These findings provide a quantitative framework for optimizing MeV-STEM parameters in high-resolution imaging of thick biological and microelectronic specimens, and for guiding beam energy selection in other advanced imaging modes beyond STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R S ), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R S are irreconcilable. When we estimate GPP based on R S measurements and some assumptions about R S :GPP ratios, we found the resulted global GPP values (bootstrap mean ${149}^{+29}_{-23}$ Pg C yr ⁻1 ) are significantly higher than most GPP estimates reported in the literature (${113}^{+18}_{-18}$ Pg C yr ⁻1 ). Similarly, historical GPP estimates imply a soil respiration flux (Rs GPP , bootstrap mean of ${68}^{+10}_{-8}$ Pg C yr ⁻1 ) statistically inconsistent with most published R S values (${87}^{+9}_{-8}$ Pg C yr ⁻1 ), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R S :GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Simultaneous measurements of several state variables in shocked carbon by imaging x-ray scattering

We apply the novel experimental technique of imaging x-ray Thomson scattering to measure the spatial profiles of the temperature, ionization state, relative material density, and the shock speed in a high-energy density system. A blast wave driven in a low-density foam is probed with 90∘ scattering of 7.8 keV helium-like nickel x-rays, which are spectrally dispersed and resolved in one spatial dimension by a doubly curved crystal. The inferred properties of the shock are shown to be self-consistent with 1D analytical estimates. These high-resolution measurements enable a direct comparison of the observed temperature with the results from hydrodynamic simulations. We find good agreement with the simulations for the temperature at the shock front but discrepancies in the modeling of the spatial temperature profile and shock speed. These results indicate the challenges in modeling the shock dynamics of structured materials like foams, commonly used in many high-energy density and laboratory astrophysics experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

FLUKA: Predictive power for cosmogenic backgrounds

The next generation of experiments searching for rare physics events with increased sensitivity will require precise predictions of cosmogenic backgrounds. Recent high quality deep underground measurements for cosmogenic neutrons in large liquid scintillator targets were used to study the FLUKA simulation package for this purpose. The results and conclusions drawn from a detailed benchmark comparison with data from the Borexino experiment were reported recently. In general, good agreement between data and simulation results were found with some identified discrepancies. Improved physics models already implemented in the current version of the FLUKA code, which will be publicly available with the upcoming code release, address the more important identified issues. A careful evaluation of the improved predictions is ongoing. However, the agreement between preliminary FLUKA simulation results and the Borexino experimental data are excellent. The preliminary findings will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

DO INTERMEDIATE-MASS BLACK HOLES EXIST IN GLOBULAR CLUSTERS?

The existence of intermediate-mass black holes (IMBHs) in globular clusters (GCs) remains a crucial problem. Searching for IMBHs in GCs reveals a discrepancy between radio observations and dynamical modelings: the upper mass limits constrained by radio observations are systematically lower than that of dynamical modelings. One possibility for such a discrepancy is that, as we suggest in this work, there exist outflows in accretion flows. Our results indicate that, for most sources, current radio observations cannot rule out the possibility that IMBHs may exist in GCs. In addition, we adopt an M-dot -L{sub R} relation to revisit this issue, which confirms the results obtained by the fundamental plane relation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

SEVEN-YEAR WILKINSON MICROWAVE ANISOTROPY PROBE (WMAP ) OBSERVATIONS: GALACTIC FOREGROUND EMISSION

We present updated estimates of Galactic foreground emission using seven years of WMAP data. Using the power spectrum of differences between multi-frequency template-cleaned maps, we find no evidence for foreground contamination outside of the updated (KQ85y7) foreground mask. We place a 15 {mu}K upper bound on rms foreground contamination in the cleaned maps used for cosmological analysis. Further, the cleaning process requires only three power-law foregrounds outside of the mask. We find no evidence for polarized foregrounds beyond those from soft (steep-spectrum) synchrotron and thermal dust emission; in particular we find no indication in the polarization data of an extra 'haze' of hard synchrotron emission from energetic electrons near the Galactic center. We provide an updated map of the cosmic microwave background (CMB) using the internal linear combination method, updated foreground masks, and updates to point source catalogs using two different techniques. With additional years of data, we now detect 471 point sources using a five-band technique and 417 sources using a three-band CMB-free technique. In total there are 62 newly detected point sources, a 12% increase over the five-year release. Also new are tests of the Markov chain Monte Carlo foreground fitting procedure against systematics in the time-stream data, and tests against the observed beam asymmetry. Within a few degrees of the Galactic plane, the behavior in total intensity of low-frequency foregrounds is complicated and not completely understood. WMAP data show a rapidly steepening spectrum from 20 to 40 GHz, which may be due to emission from spinning dust grains, steepening synchrotron, or other effects. Comparisons are made to a 1 deg 408 MHz map (Haslam et al.) and the 11 deg ARCADE 2 data (Singal et al.). We find that spinning dust or steepening synchrotron models fit the combination of WMAP and 408 MHz data equally well. ARCADE data appear inconsistent with the steepening synchrotron model and consistent with the spinning dust model, though some discrepancies remain regarding the relative strength of spinning dust emission. More high-resolution data in the 10-40 GHz range would shed much light on these issues.

79 ASTRONOMY AND ASTROPHYSICS↗
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

Nonlocal nucleon matrix elements in the rest frame

Extracting parton structure from lattice quantum chromodynamics (QCD) calculations requires studying the coordinate scale 𝑧 3 dependence of the matrix elements of bilocal operators. The most significant contribution comes from the 𝑧 3 dependence induced by ultraviolet (UV) renormalization of the Wilson line. We demonstrate that the next-to-leading order perturbative calculations of the renormalization factor can describe, to a few percent accuracy, the logarithm of the lattice QCD rest frame matrix elements with separations up to distances of 0.6 fm on multiple lattice spacings. The residual discrepancies can be modeled by a leading effect from the structure of the nucleon.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Analysis of the Daya Bay Reactor Antineutrino Flux Changes with Fuel Burnup

We investigate the recent Daya Bay results on the changes in the antineutrino flux and spectrum with the burnup of the reactor fuel. We find that the discrepancy between current model predictions and the Daya Bay results can be traced to the original measured 235 U/ 239 Pu ratio of the fission beta spectra that were used as a base for the expected antineutrino fluxes. An analysis of the antineutrino spectra that is based on a summation over all fission fragment beta-decays, using nuclear database input, explains all of the features seen in the Daya Bay evolution data. However, this summation method still predicts an anomaly. Thus, we conclude that there is currently not enough information to use the antineutrino flux changes to rule out the possible existence of sterile neutrinos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Using Diffusion-Reaction Simulation to Study the Formation and Self-Compensation Mechanism of Cu Doping in CdTe

An improved model of copper p-type doping in CdTe absorbers is proposed that accounts for the mechanisms related to tightly bound Cu(i)-Cu(Cd) and Cd(i)-Cu(Cd) complexes that both limit diffusion and cause self-compensation of Cu species. The new model explains apparent discrepancy between DFT-calculated and fitted diffusion parameters of Cu reported in our previous work, and allows for better understanding of performance and metastabilities in CdTe PV devices.

14 SOLAR ENERGY↗
DOE OSTI2015

Application of a modified method of ultrasonic measurements for determination of elastic moduli of rocks

The velocities of elastic waves with quasi-longitudinal and quasi-transverse polarizations in a spherical rock sample have been measured. The experimental values of velocities are used to calculate 21 elastic moduli of the sample. For comparison, the effective elastic properties of the sample are simulated based on the data on the crystallographic textures of rock-forming minerals obtained by neutron diffraction. It is shown that the largest discrepancy between the model predictions and experimental velocity values is observed for transversely polarized waves.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

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↗
DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗
DOE OSTI2024

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Chowdhury, Agniva↗
DOE OSTI2024

Simulation Study of High-Precision Characterization of MeV Electron Interactions for Advanced Nano-Imaging of Thick Biological Samples and Microchips

The resolution of a mega-electron-volt scanning transmission electron microscope (MeV-STEM) is primarily governed by the properties of the incident electron beam and angular broadening effects that occur within thick biological samples and microchips. A precise understanding and mitigation of these constraints require detailed knowledge of beam emittance, aberrations in the STEM column optics, and energy-dependent elastic and inelastic critical angles of the materials being examined. This simulation study proposes a standardized experimental framework for comprehensively assessing beam intensity, divergence, and size at the sample exit. This framework aims to characterize electron-sample interactions, reconcile discrepancies among analytical models, and validate Monte Carlo (MC) simulations for enhanced predictive accuracy. Our numerical findings demonstrate that precise measurements of these parameters, especially angular broadening, are not only feasible but also essential for optimizing imaging resolution in thick biological samples and microchips. By utilizing an electron source with minimal emittance and tailored beam characteristics, along with amorphous ice and silicon samples as biological proxies and microchip materials, this research seeks to optimize electron beam energy by focusing on parameters to improve the resolution in MeV-STEM/TEM. This optimization is particularly crucial for in situ imaging of thick biological samples and for examining microchip defects with nanometer resolutions. Our ultimate goal is to develop a comprehensive mapping of the minimum electron energy required to achieve a nanoscale resolution, taking into account variations in sample thickness, composition, and imaging mode.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗