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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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DOE OSTI2021

Meta-uncertainty for particle image velocimetry

Uncertainty quantification for Particle Image Velocimetry (PIV) is critical for comparing experimentally measured flow fields with Computational Fluid Dynamics (CFD) results, and model design and validation. However, PIV features a complex measurement chain with coupled, non-linear error sources, and quantifying the uncertainty is challenging. Multiple assessments show that none of the current methods can reliably measure the actual uncertainty across a wide range of experiments, and estimates can vary. Because the current methods differ in assumptions regarding the measurement process and calculation procedures, it is not clear which method is best to use for an experiment where the error distribution is unknown. To address this issue, we propose a method to estimate an uncertainty method's sensitivity and reliability, termed the Meta-Uncertainty. The novel approach is automated, local, and instantaneous, and based on perturbation of the recorded particle images. We developed an image perturbation scheme based on adding random unmatched particles to the interrogation window pair considering the signal-to-noise (SNR) of the correlation plane. Each uncertainty scheme's response to several trials of random particle addition is used to estimate a reliability metric, defined as the rate of change of the inter-quartile range (IQR) of the uncertainties with increasing levels of particle addition. We also propose applying the meta-uncertainty as a weighting metric to combine uncertainty estimates from individual schemes, based on ideas from the consensus forecasting literature. We use planar and stereo PIV measurements across a range of canonical flows to assess the performance of the uncertainty schemes. Further, a novel method is introduced to assess an uncertainty scheme's performance based on a quantile comparison of the error and uncertainty distributions, generalizing the current method of comparing the RMS of the two distributions. Here, the results show that the combined uncertainty method outperforms the individual methods, and this work establishes the meta-uncertainty as a useful reliability assessment tool for PIV uncertainty quantification.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

(U) Correlated Sampling Using Batch Statistics to Reduce the Uncertainty of Combinations of KSEN Outputs with MCNP6

The relative sensitivity of k eff to the densities of nuclides in a material are combined to compute relative sensitivities to other inputs. When computed in a single Monte Carlo run, the nuclide density sensitivities are correlated, and the statistical uncertainties propagated to other inputs will be incorrect unless those correlations are accounted for. Equations are presented to apply correlated sampling using batch statistics for the sum of an arbitrary number of random tallies and the difference of two random tallies when each is multiplied by a different constant. When correlated sampling is used on a recent benchmark evaluation, the correct statistical uncertainties for certain combinations of sensitivities are dramatically smaller than the incorrect uncertainties. A user-controlled, regular output of MCNP6’s KSEN sensitivities that allows batch statistics to be applied to combinations would be extremely valuable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

In this paper, we developed a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) simulations for bubbly flows. The proposed approach is supported by high-resolution two-phase flow measurements, including those by double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distributions of key physical quantities of interest (QoIs), including the void fraction and phasic velocities, are obtained to support the Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified while the aleatory uncertainties from stochastic fluctuations of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of the QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of an open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainties of MCFD simulations can be significantly reduced. The proposed approach has the potential for other applications that involve numerical models with empirical parameters.

42 ENGINEERING↗

DOE OSTI2023

Sister Rod Destructive Examinations (FY23) Appendix F3: Uncertainty and Conservative Bias in the Cyclic Integrated Reversible-Bending Fatigue Test

The development and application of the Cyclic Integrated Reversible-Bending Fatigue Tester (CIRFT) has been documented over the past 10 years. The CIRFT system was developed in collaboration with the U.S. Nuclear Regulatory Commission (NRC) to address the concern that high-burnup fuel rod fatigue performance was degraded compared to cladding-only behavior. Fatigue data measured by CIRFT on high-burnup fuel rods were then used by the NRC to develop a fatigue response curve for pressurized water reactor fuel rods with Zircaloy-4 cladding in NUREG-2224. Subsequently, CIRFT has been used in the sister rod project to test whether reoriented hydrides associated with vacuum drying influenced the fatigue lifetime, to extend the fatigue testing database to include fuel rods with ZIRLO cladding, and to add to the experience on both Zircaloy-4 and M5 cladding. These data will be used as a reference for subsequent characterization of fuel rods removed from the high-burnup demonstration cask to better understand the effects of long-term dry storage, which can be thought of as a multiyear elevated temperature anneal. The objective of fatigue testing is to provide data that can be used to develop a best-estimate fatigue curve and a conservative fatigue design curve that accounts for uncertainty and can be compared with actual transportation conditions. The process for defining a design fatigue curve for commercial used fuel rods was developed by Oak Ridge National Laboratory (ORNL) in FY22. One issue noted in the fatigue evaluation was that the calculated combined uncertainty in the strain amplitude values determined from the measured data was large. It was proposed then to directly measure the uncertainty in the strain amplitude to either validate the calculated combined uncertainty or update the uncertainty based on the measurement data. Thus, the purpose of this appendix is to document the measured uncertainty in the strain amplitude data. In the process, it was determined that there has been a conservative bias in the calculated strain amplitude that is small enough to be within the measurement uncertainty. This observation is discussed herein, and a case is made to continue to include the conservative bias in the data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Quantifying modeling uncertainties when combining multiple gravitational-wave detections from binary neutron star sources

Here, with the increasing sensitivity of gravitational-wave detectors, we expect to observe multiple binary neutron-star systems through gravitational waves in the near future. The combined analysis of these gravitational-wave signals offers the possibility to constrain the neutron-star radius and the equation of state of dense nuclear matter with unprecedented accuracy. However, it is crucial to ensure that uncertainties inherent in the gravitational-wave models will not lead to systematic biases when information from multiple detections is combined. To quantify waveform systematics, we perform an extensive simulation campaign of binary neutron-star sources and analyze them with a set of four different waveform models. For our analysis with 38 simulations, we find that statistical uncertainties in the neutron-star radius decrease to ± 250 m (2% at 90% credible interval) but that systematic differences between currently employed waveform models can be twice as large. Hence, it will be essential to ensure that systematic biases will not become dominant in inferences of the neutron-star equation of state when capitalizing on future developments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Creating high-precision reference gas standards of 85Kr for groundwater age-dating

Absolute gas counting (AGC) was applied to two gas blends of 85Kr in argon-methane (P10) counting gas to establish a high-precision specific activity (Bq/cm3) reference value for characterizing 85Kr detection efficiency for groundwater age dating measurements. The AGC or length-compensated technique has been utilized by the metrology community for decades and is an accepted method for developing radioactive gas standards. The AGC capability at Pacific Northwest National Laboratory (PNNL) uses a set of nine unequal-length proportional counters with precisely-measured internal volumes, and a gas loading system with high-precision pressure and temperature sensors. A series of AGC measurements were collected at multiple pressures to determine the inverse pressure relationship (1/P) for 85Kr and define a wall-effect correction that accounts for events decaying into the detector wall and not depositing sufficient energy in the gas to be detected. In addition to the wall-effect, two additional corrections were evaluated and are discussed in detail. Specifically, the threshold effect which accounts for events deposited below the analysis threshold and a detection efficiency as a function of detector volume effect that was observed during analysis. A robust uncertainty model was developed using the Guide to the expression of Uncertainty in Measurements (GUM) approach. The combination of carefully scrutinized correction factors, precise measurements of pressure, temperature and detector volume, and robust counting statistics resulted in the determination of high-precision specific activity values with 0.50% or less total combined uncertainty for two Kr-in-P10 reference gas standards (KP10) that will enable new groundwater age-dating measurements at PNNL.

Kr-85↗

DOE OSTI2020

Measurement of properties of B$^0_\mathrm{s}\to\mu^+\mu^-$ decays and search for B$^0\to\mu^+\mu^-$ with the CMS experiment

Results are reported for the $ {\mathrm{B}}_{\mathrm{s}}^0 $→ μ$^{+}$μ$^{−}$ branching fraction and effective lifetime and from a search for the decay B$^{0}$→ μ$^{+}$μ$^{−}$. The analysis uses a data sample of proton-proton collisions accumulated by the CMS experiment in 2011, 2012, and 2016, with center-of-mass energies (integrated luminosities) of 7 TeV (5 fb$^{−1}$), 8 TeV (20 fb$^{−1}$), and 13 TeV (36 fb$^{−1}$). The branching fractions are determined by measuring event yields relative to B$^{+}$→ J/ψK$^{+}$ decays (with J/ψ → μ$^{+}$μ$^{−}$), which results in the reduction of many of the systematic uncertainties. The decay $ {\mathrm{B}}_{\mathrm{s}}^0 $→ μ$^{+}$μ$^{−}$ is observed with a significance of 5.6 standard deviations. The branching fraction is measured to be $ \mathrm{\mathcal{B}}\left({\mathrm{B}}_{\mathrm{s}}^0\to {\upmu}^{+}{\upmu}^{-}\right)=\left[2.9\pm 0.7\left(\exp \right)\pm 0.2\left(\mathrm{frag}\right)\right]\times {10}^{-9} $, where the first uncertainty combines the experimental statistical and systematic contributions, and the second is due to the uncertainty in the ratio of the $ {\mathrm{B}}_{\mathrm{s}}^0 $ and the B$^{+}$ fragmentation functions. No significant excess is observed for the decay B$^{0}$→ μ$^{+}$μ$^{−}$, and an upper limit of ℬ(B$^{0}$ → μ$^{+}$μ$^{−}$) < 3.6 × 10$^{−10}$ is obtained at 95% confidence level. The $ {\mathrm{B}}_{\mathrm{s}}^0 $→ μ+μ− effective lifetime is measured to be $ {\tau}_{\upmu^{+}{\upmu}^{-}}={1.70}_{-0.44}^{+0.61} $ ps. These results are consistent with standard model predictions.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗

DOE OSTI2022

Cosmological dependence of sterile neutrino dark matter with self-interacting neutrinos

Unexplored interactions of neutrinos could be the key to understanding the nature of the dark matter (DM). In particular, active neutrinos with new self-interactions can produce keV-mass sterile neutrinos that account for the whole of the DM through the Dodelson-Widrow mechanism for a large range of active-sterile mixing values. This production typically occurs before Big-Bang Nucleosynthesis (BBN) in a yet uncharted era of the Universe. We assess how the mixing range for keV-mass sterile neutrino DM is affected by the uncertainty in the early Universe pre-BBN cosmology. This is particularly relevant for identifying the viable parameter space of sterile neutrino searches allowed by all astrophysical limits, as well as for cosmology, since the detection of a sterile neutrino could constitute the first observation of a particle providing information about the pre-BBN epoch. Here, we find that the combined uncertainties in the early Universe cosmology and neutrino interactions significantly expand the allowed parameter space for sterile neutrinos that can constitute the whole of the DM.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Ab initio quantum scattering calculations and a new potential energy surface for the HCl( X 1Σ+)–O2(X3Σg−) system: Collision-induced line shape parameters for O2-perturbed R(0) 0–0 line in H35Cl

The remote sensing of abundance and properties of HCl—the main atmospheric reservoir of Cl atoms that directly participate in ozone depletion—is important for monitoring the partitioning of chlorine between “ozone-depleting” and “reservoir” species. Such remote studies require knowledge of the shapes of molecular resonances of HCl, which are perturbed by collisions with the molecules of the surrounding air. In this work, we report the first fully quantum calculations of collisional perturbations of the shape of a pure rotational line in H35Cl perturbed by an air-relevant molecule [as the first model system we choose the R(0) line in HCl perturbed by O2]. The calculations are performed on our new highly accurate HCl(X1Σ+)–O2(X3Σg−) potential energy surface. In addition to pressure broadening and shift, we also determine their speed dependencies and the complex Dicke parameter. This gives important input to the community discussion on the physical meaning of the complex Dicke parameter and its relevance for atmospheric spectra (previously, the complex Dicke parameter for such systems was mainly determined from phenomenological fits to experimental spectra and the physical meaning of its value in that context is questionable). We also calculate the temperature dependence of the line shape parameters and obtain agreement with the available experimental data. We estimate the total combined uncertainties of our calculations at 2% relative root-mean-square error in the simulated line shape at 296 K. This result constitutes an important step toward computational population of spectroscopic databases with accurate ab initio line shape parameters for molecular systems of terrestrial atmospheric importance.

Chemistry↗

DOE OSTI2025

Design of Quasi-Integral Experiments at the Flexible Neutron Source at the University of Tennessee

Nuclear reaction data libraries make use of experimental data that are differential in reaction and neutron energy for evaluation and data that are integral in reaction and energy for validation. In many cases, integral measurements can provide lower experimental uncertainties than differential measurements, but the impact of individual reactions and energies cannot generally be determined from the single experimental observable. “Quasi-integral” or “quasi-differential” measurements, which are integral in one characteristic (energy or reaction) and differential in the other, can often provide a middle ground of lower experimental uncertainties combined with some ability to unpack the impact of individual reactions or energies. Furthermore, the Flexible Neutron Source (FNS) at the University of Tennessee, Knoxville, is a new experimental facility that can perform integral and quasi-integral measurements by removing components of the integral configuration to determine the impact of specific reactions. Possible flux-averaged cross-section measurements, flux perturbation measurements, and neutron downscattering measurements are simulated here to show the value of the FNS to evaluation and validation of nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

The Complete α 8 m Contributions to the 1s Lamb Shift in Hydrogen

Precision studies of simple atoms, such as hydrogen, play an essential role in tests of bound-state QED and determining fundamental constants, such as the Rydberg constant and the proton charge radius. One of the QED predictions is for the Lamb shift of hydrogenic energy levels and, in particular, of the ground state. Here, the value of the 1s Lamb shift in hydrogen and deuterium is required for an accurate determination of the Rydberg constant and the proton charge radius utilizing data from high-resolution spectroscopy of hydrogen and deuterium atoms, as well as for precision tests of bound-state QED. The dominant QED contribution to the uncertainty is due to the α 8 m external-field contributions. We discuss here our recent results on the two- and three-loop contributions, which essentially reduce the theoretical uncertainty. Combined with recent calculations of Laporta on the slope of the Dirac form factor in the three-loop level, our results allow for completion of calculations of α 8 m contributions to the Lamb shift of the ground state in the hydrogen atom.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Superallowed decays within and beyond the standard model

This note reviews the role of superallowed transitions in determining the strength of the weak interaction among the lightest quarks and in searching for new physics beyond the standard electroweak model. The two sets of superallowed decays in nuclei considered here are pure Fermi and mirror transitions. The first have been scrutinized for more than 50 years. Here, the most relevant results are presented and the role of the nucleus-dependent radiative correction and nucleus-independent inner radiative correction are reviewed. In this context, the systematic study of mirror transitions started about 15 years ago. Despite the significant progress made since then, the data is still limited by experimental uncertainties. Combining the results from all superallowed transitions, which are fully consistent, provides a test of unitarity of the first row of the Cabibbo-Kobayashi-Maskawa matrix, which displays a 2σ tension with the standard model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Measurement of the Eta Meson Radiative Decay Width via the Primakoff Effect

The ? meson is an interesting tool to study fundamental symmetries in Quantum Chromodynamics (QCD). In particular, its radiative decay width, ? p? Ñ ??q, is an important quantity that can be predicted in the framework of Chiral Perturbation Theory. A precision measurement of this quantity would provide critical inputs to understanding the mixing of the ? and ?1 mesons and extracting constants with wide-ranging applications in low-energy QCD. This decay width has been measured in the past using two different experimental techniques. The more popular technique utilized e`e´ collisions to produce ? mesons through electromagnetic interactions. Today, the Particle Data Group (PDG) averages the results of five such experiments to obtain their currently-accepted value of the decay width as: 0.515?0.018 keV. However the first measurement of this quantity was obtained from a fixed-target experiment that measured the cross section for photoproduction of ? mesons on a nuclear target via the Primakoff effect. Their result of 0.324?0.046 keV shows strong tension with the average of the collider measurements, motivating a new, high precision measurement using the Primakoff method. For this purpose, the PrimEx-eta experiment was conducted in Hall D of the Thomas Jefferson National Accelerator Facility (Jefferson Lab or JLab). The data is currently being analyzed to measure the differential cross section for the photoproduction of ? mesons on a liquid, 4He target. Preliminary results obtained from the analysis of the first phase of the PrimEx-eta experiment show reasonable agreement with the currently-accepted PDG value of the radiative decay width. However, as will be discussed, there are many challenges to this precision measurement which must be studied before any results can be finalized and compared with previous measurements. In parallel to the ? decay width measurement, the PrimEx-eta experiment measured the total cross section for the fundamental, Quantum Electrodynamics (QED) process of Compton scattering from the atomic electrons inside the target. The results obtained from this measurement are in strong agreement with the next-to-leading order QED calculations, and the total combined uncertainties are below 3% for incident photon energies between 7-10 GeV. In addition to providing the first precision measurement of the total Compton scattering cross section within this beam energy range, this measurement verifies the capability of the PrimEx-eta experimental setup to perform absolute cross section measurements at forward angles, and serves as a reference process for the calibration of systematic uncertainties.

Smith, Andrew↗

DOE OSTI2024

Measurement of the branching fraction of the decay J / ψ → p p ¯ η

A high-precision measurement of the branching fraction of the decay J / ψ → p p ¯ η is performed using ( 10 087 ± 44 ) × 10 6 J / ψ events recorded by the BESIII detector at the BEPCII storage ring. The branching fractions of the two decays J / ψ → p p ¯ η ( η → γ γ ) and J / ψ → p p ¯ η ( η → π + π − π 0 ) are measured individually to be B ( J / ψ → p p ¯ η ( η → γ γ ) ) = ( 1.480 ± 0.001 ± 0.024 ) × 10 − 3 and B ( J / ψ → p p ¯ η ( η → π + π − π 0 ) ) = ( 1.557 ± 0.003 ± 0.038 ) × 10 − 3 , where the first uncertainties are statistical and the second systematic. Both results are compatible within their uncorrelated systematic uncertainties. The combined result is B ( J / ψ → p p ¯ η ) = ( 1.495 ± 0.001 ± 0.023 ) × 10 − 3 , where the first uncertainty is the combined statistical uncertainty and the second one the combined systematic uncertainty of both analyses, incorporating correlations between them. In addition, the p p ¯ threshold region is investigated for a potential threshold enhancement, and no evidence for one is observed. Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2024

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

60 APPLIED LIFE SCIENCES↗