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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A simple model for DSS-14 outage times

A model is proposed to describe DSS-14 outage times. Discrepancy Reporting System outage data for the period from January 1986 through September 1988 are used to estimate the parameters of the model. The model provides a probability distribution for the duration of outages, which agrees well with observed data. The model depends only on a small number of parameters, and has some heuristic justification. This shows that the Discrepancy Reporting System in the Deep Space Network (DSN) can be used to estimate the probability of extended outages in spite of the discrepancy reports ending when the pass ends. The probability of an outage extending beyond the end of a pass is estimated as around 5 percent.

Rumsey, H. C.↗
DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Optical and X-ray radiation from fast pulsars - Effects of duty cycle and spectral shape

The optical luminosity of PSR 0540 is considerably stronger than what one would have predicted in a simple model developed earlier where the pulses are synchrotron radiation by secondary electrons near the light cylinder. This discrepancy can be eliminated if one incorporates into the model the effects of the large duty cycle and the spectral properties of PSR 0540. It is also shown that the same model can provide a reasonable fit to the observed X-ray fluxes from fast pulsars.

Pacini, F.↗
NASA NTRS1991

Experiments and modeling

The usual approach in establishing the correctness and accuracy of turbulence models is to numerically solve the modeled differential equations and then compare the results with the experiment. However, in the case of a discrepancy, this procedure does not pinpoint where in the model the drawback lies. It is also possible that the model overcompensates one physical phenomenon and undercompensates the other so that the net result is a good agreement between the two. Therefore, a more desirable approach is to directly compare the individual terms in the equations with their models. To achieve this objective, primary physical experiments were used to carry out the second moment budgets. These can then be used to analyze and assess various models and closure assumptions and seek improvements/modifications where models prove deficient.

Shabbir, Aamir↗
NASA NTRS1986

The prominence-corona interface and its relationship to the chromosphere-corona transition

The classical model of the chromosphere-corona transition does not account for the observed behavior of the differential emission measure for T approx. less than 100,000 K. Several models have been proposed to resolve this discrepancy in physically different ways. Because the observed differential emission measure at the prominence-corona interface is on average nearly the same as in the chromosphere-corona transition, prominences offer a fresh testing ground for models tailored to the chromosphere-corona transition. The researcher considered three such models and concluded that none extends in a natural way to the environment of prominences. The researcher advanced a simple idea involving thermal conduction both along and across the magnetic field from the corona into cool threads.

Rabin, Douglas↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2019

Data-Augmented Modeling of Transition to Turbulence in Rayleigh-Taylor Mixing Layers

Existing turbulence models based on the Reynolds-averaged Navier–Stokes (RANS) equations are ill-equipped to represent transitional phenomena that are characteristic of problems such as those involved in Rayleigh-Taylor mixing layers. In this work, large eddy simulation(LES) data is used to augment and improve the predictive capability of RANS models. In-verse models are used to extract temporally varying embedded discrepancy functions from a number of LES datasets. These discrepancies are generalized into functional forms using neural networks and embedded inside the RANS solver to augment predictions. Specifically, the data-augmented models are demonstrated to provide improved predictions in Rayleigh-Taylor mixing problems at low Atwood numbers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Studies of Localization Effects on Transverse Kinematic Imbalance in the GENIE Generator

The forthcoming Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE and other Fermilab-based experiments as a central value model for neutrino-nucleus cross sections, exhibits known discrepancies in comparison to MicroBooNE cross section data, especially when considering neutrino scattering events' transverse kinematic imbalance (TKI). For quasielastic-like measurements, the data show a larger transverse missing momentum tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous attempts at a solution include variations of final-state interaction (FSI) strengths, incorrectly decreasing both the peak and tail, thus leaving the discrepancy unresolved. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. In this work, GENIE FSI routines have been modified to directly utilize intranuclear particle positions, thereby introducing a newly localized momentum treatment in correlation with the nominal density in both the hA and hN intranuclear cascade models for their 2018 and 2025 variants. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear nucleons' ("scattering center") momentum and its radial dependence validate the localization implementation. However, while such consistent localization produces modest changes in the proton momentum spectrum at low-to-medium momenta, only small changes are observed in the TKI distributions. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved by such a solution.

Bulla, Braden [Ctr. Coll., Danville; Fermilab]↗
DOE OSTI2020

Analytic evaluation of hydrogen neutron elastic scattering moments with free-gas thermal treatment compared to NJOY

In this article, we present an analytic approach to evaluate Legendre moments of hydrogen neutron elastic scattering differential cross section, with and without free-gas thermal treatment. The results for the zero-th (P 0 ) and first (P 1 ) moments are given and compared against a 138-group cross sections produced by NJOY. An MCNP thin-wire test was employed as an independent verification for our analytic model for the free-gas zero-th moment. Excellent agreement was observed between the analytic result and MCNP prediction. They both suggest that an increased thermal cut-off in NJOY THERMR module is advisable for higher accuracy. Discrepancy was also found between the analytic model and NJOY multi-group cross section for the first order Legendre moments of the differential cross section, which might indicate a numerical anomaly in NJOY.

NJOY↗
DOE OSTI2023

Bayesian filtering for model predictive control of stochastic gene expression in single cells

This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Mathematics↗
DOE OSTI2019

Concordance and discordance in cosmology

The success of present and future cosmological studies is tied to the ability to detect discrepancies in complex data sets within the framework of a cosmological model. Tensions caused by the presence of unknown systematic effects need to be isolated and corrected to increase the overall accuracy of parameter constraints, while discrepancies due to new physical phenomena need to be promptly identified. We develop a full set of estimators of internal and mutual agreement and disagreement, whose strengths complement each other. These estimators take into account the effect of prior information and compute the statistical significance of both tensions and confirmatory biases. The estimators that we present optimally weight all parameter space directions that are either fully constrained by the data or the prior, allowing for complete and fair degree of freedom counting. We apply them to a wide range of state-of-the-art cosmological probes and show that these estimators can be easily used, regardless of model and data complexity. Here, we derive a series of results that show that discrepancies indeed arise within the standard ΛCDM model. Several of them exceed the probability threshold of 95% and deserve a dedicated effort to understand their origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Electrochemical Li Intercalation in Black Phosphorus: In Situ and Ex Situ Studies

A systematic study on electrochemical charge transfer in Li-doped black phosphorous (BP) was carried out by both in-situ and ex-situ Raman scattering. Galvanostatic discharge of dedicated in-situ electrochemical cell for Raman spectroscopy was used to study time evolution of vibrational modes under lithiation. In addition to the peak broadening which is a result of structural expansion, peaks corresponding to all three atomic vibrational modes A 1 g , B 2 g and A 2 g were found to red-shift as a result of lithiation. Peaks B 2 g and A 2 g were shifted about 1.6 times more towards lower wavenumbers in comparison to A 1 g . Further characterizations using optical and electron microscopy showed that the intercalation of BP is highly anisotropic, where channels along the zigzag direction were found to be the easy direction for intercalation. X-ray diffraction on intercalated samples confirmed a reduction of thickness as lithiation weakens interlayer bonding, thus resulting in a partial exfoliation of BP flakes. Here, first principle studies using density function theory (DFT) were used to develop a theoretical model for the intercalation mechanism. The discrepancy between the experimental and the theoretical results was also addressed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

The reaction of hydroxyl and methylperoxy radicals is not a major source of atmospheric methanol

Methanol is a benchmark for understanding tropospheric oxidation, but is underpredicted by up to 100% in atmospheric models. Recent work has suggested this discrepancy can be reconciled by the rapid reaction of hydroxyl and methylperoxy radicals with a methanol branching fraction of 30%. However, for fractions below 15%, methanol underprediction is exacerbated. Theoretical investigations of this reaction are challenging because of intersystem crossing between singlet and triplet surfaces – ~45% of reaction products are obtained via intersystem crossing of a pre-product complex – which demands experimental determinations of product branching. Here we report direct measurements of methanol from this reaction. A branching fraction below 15% is established, consequently highlighting a large gap in the understanding of global methanol sources. These results support the recent high-level theoretical work and substantially reduce its uncertainties.

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

Probing Exotic Cross-Shell Interactions at N = 28 with Single-Neutron Transfer on 47 K

Here, we present the first measurement of the 47 K ⁡(𝑑, 𝑝⁢𝛾)⁢ 48 K transfer reaction, performed in inverse kinematics using a reaccelerated beam of 47 K. The level scheme of 48 K has been greatly extended, with nine new bound excited states identified and spectroscopic factors deduced. Uniquely, the 47 K ⁡(𝑑, 𝑝) reaction gives access to nuclear states that are sensitive to the interaction of protons and neutrons in the widely spaced 1⁢𝑠 and 𝑓⁡𝑝 orbitals, respectively. Detailed comparisons with SDPF-U and SDPF-MU shell-model calculations reveal a number of discrepancies between theory and experiment. Intriguingly, a systematic overestimation of spectroscopic factors and a poor reproduction of the energies for 1 − states suggests that the mixing between the 𝜋⁢𝑠$^1_{1/2}$⁢𝑑$^4_{3/2}$ and 𝜋⁢𝑠$^2_{1/2}$⁢𝑑$^3_{3/2}$ proton configurations in 48 K is not correctly described using current interactions, challenging our description of light nuclei around the 𝑁 = 28 island of inversion.

Paxman, Charlie J. [Univ. of Surrey, Guildford (Un↗
DOE OSTI2021

Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation

Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.

Makkar, Nikhil↗
DOE OSTI2019

Stellar Parameters for Pulsating B-star Candidates in the Kepler Field

The field of asteroseismology has enjoyed a large swath of data coming from recent missions (e.g., CoRoT, Kepler, K2). This wealth of new data has allowed the field to expand beyond the previous limitation of a few extremely bright and evolved stars. Asteroseismology relies on accurate surface measurements for boundary conditions, but the predicted physical parameters in the Kepler Input Catalog (KIC) are unreliable for hot stars. We present stellar parameters of 25 candidate pulsating B-star candidates in the Kepler field. We use blue optical spectra to measure the projected rotational velocity (Vsin i), effective temperature (T {sub eff}), and surface gravity (logg) using TLUSTY and Kurucz ATLAS9 model atmospheres. We find a large discrepancy between our spectroscopically derived parameters and those derived from photometry in the KIC and Gaia Data Release 2 (DR2). Using spectral energy distributions, we also measure the radii of these stars and later calculate the luminosities and masses. We find the extinctions (A {sub V}) of these stars to be consistent with zero, which is expected for stars of high Galactic latitude.

79 ASTRONOMY AND ASTROPHYSICS↗