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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Runaway electron plateau current profile reconstruction from synchrotron imaging and Ar-II line polarization angle measurements in DIII-D

Abstract Current profile reconstructions are obtained for high current ( I p ≃ 550 kA) post-disruption runaway electron (RE) plateau plasmas in DIII-D. Two novel methods of measuring the RE current profile in high-current RE plateaus are introduced and compared: localization of the q = 2 rational surface using visible synchrotron emission (SE) imaging and the measurement of the polarization angle of line-integrated Ar-II line emission. The two methods are found to be consistent with each other within the data uncertainties. Different simulations of the RE current profile are compared with the measurements: the toroidal fluid RE model is found to best fit the data, within the measurement uncertainties. In addition to introducing two novel methods to measure the RE current profile and validating present simulation capabilities, this work demonstrates that instabilities can grow at q = 2 and q = 1 surfaces without necessarily causing a RE final loss instability. Numerical simulations are also presented to elucidate the role of these instabilities on synchrotron emission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Quenching factor measurements of neon nuclei in neon gas

The NEWS-G collaboration uses spherical proportional counters (SPCs) to search for weakly interacting massive particles (WIMPs). Here, in this paper, we report the first measurements of the nuclear quenching factor in neon gas at 2 bar using an SPC deployed in a neutron beam at the TUNL facility. The energy-dependence of the nuclear quenching factor is modeled using a simple power law: $αE^{β}_{nr}$; we determine its parameters by simultaneously fitting the data collected with the detector over a range of energies. We measured the following parameters in Ne : CH 4 at 2 bar: α= 0.2801±0.0050 (fit) ±0.0045 (sys) and β=0.0867±0.020 (fit) ±0.006 (sys). Our measurements do not agree with expected values from SRIM or Lindhard theory. We demonstrated the feasibility of performing quenching factor measurements at sub-keV energies in gases using SPCs and a neutron beam.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Heterogeneous multiphase flow properties of volcanic rocks and implications for noble gas transport from underground nuclear explosions

Of interest to the Underground Nuclear Explosion Signatures Experiment are patterns and timing of explosion-generated noble gases that reach the land surface. The impact of potentially simultaneous flow of water and gas on noble gas transport in heterogeneous fractured rock is a current scientific knowledge gap. This article presents field and laboratory data to constrain and justify a triple continua conceptual model with multimodal multiphase fluid flow constitutive equations that represents host rock matrix, natural fractures, and induced fractures from past underground nuclear explosions (UNEs) at Aqueduct and Pahute Mesas, Nevada National Security Site, Nevada, USA. Capillary pressure from mercury intrusion and direct air–water measurements on volcanic tuff core samples exhibit extreme spatial heterogeneity (i.e., variation over multiple orders of magnitude). Petrographic observations indicate that heterogeneity derives from multimodal pore structures in ash-flow tuff components and post-depositional alteration processes. Comparisons of pre- and post-UNE samples reveal different pore size distributions that are due in part to microfractures. Capillary pressure relationships require a multimodal van Genuchten (VG) constitutive model to best fit the data. Relative permeability estimations based on unimodal VG fits to capillary pressure can be different from those based on bimodal VG fits, implying the choice of unimodal vs. bimodal fits may greatly affect flow and transport predictions of noble gas signatures. The range in measured capillary pressure and predicted relative permeability curves for a given lithology and between lithologies highlights the need for future modeling to consider spatially distributed properties.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Improving resbos for the precision needs of the LHC

The resummation calculation (esos) is a widely used tool for the simulation of single vector boson production at colliders. In this work, we develop a significant improvement over the esos code by increasing the accuracy from NNLL + NLO to N 3 LL + NNLO and release the esos v2.0 code. Furthermore, we propose a new nonperturbative function that includes information about the rapidity of the system (IFY). The IFY functional form was fitted to data from fixed target experiments, the Tevatron, and the LHC. We find that the nonperturbative function has mild rapidity dependence based on the results of the fit. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Method for determining a histogram of variable sample rate waveforms

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining a plurality of bins; defining an array of elements; dividing the sampled data points into a plurality of sections; assigning a plurality of polynomial equations, one polynomial equation to each section, each polynomial equation having a waveform that fits the data points of the associated section; determining a plurality of section bin times, one section bin time for each bin in each section, each section bin time determined using the polynomial equation and indicating an amount of time that the waveform has values in the range of one of the bins; and adding the section bin time for each bin in each section to the histogram data in the array element pointed to by the number of the bin.

Tohlen, Michael Aaron↗
DOE OSTI2026

High Pressure X-ray Diffraction and Equation of State of Hydrazine

Synchrotron X-ray diffraction has been used to investigate the structure and equation of state (EOS) of hydrazine (N 2 H 4 ) up to 54.3 GPa at 298 K. The diffraction patterns could be fit to a monoclinic unit-cell structure and put strong constraints on previously reported phase transitions documented by vibrational spectroscopy over this pressure range. Pressure–volume ( P–V ) data were fit using a Vinet EOS, yielding parameters: V 0 = 45.2 Å 3 /molecule (fixed), K 0 = 11.8(7) GPa, and K 0 ′ = 6.5(2). Previously measured high-pressure vibrational frequency shifts were used to estimate the vibrational free energy and model P–V–T isotherms from 0 to 1200 K. The results of the P–V–T isotherms are compared to existing shock Hugoniot data on hydrazine and 298 K isotherms for assemblages of possible decomposition products. This comparison suggests dissociation at high density under shock loading. Good correspondence was found between the static lattice EOS as calculated by the model and the previously reported EOS as calculated by density functional theory. Finally, these results resolve existing uncertainties about the EOS and crystal symmetry of hydrazine at high pressure and provide valuable baseline information on this important energetic material.

diffraction↗
DOE OSTI2021

Physics-Informed Neural Networks for Heat Transfer Problems

Abstract Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multitask learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Engineering↗
DOE OSTI2022

A New 1-step Arrhenius Decomposition Model for PBX 9501: Calibration with Isochoric-Adiabatic High-Activation-Energy Asymptotics and Uncertainty Quantification

Here we present a new 1-step Arrhenius thermal decomposition model of PBX 9501. As opposed to previous studies, we use high-energy asymptotic theory, and previously published equations of state to parameterize the model to experimental critical-time data. The model parameters were fit to experimental data using a bootstrapping procedure to capture the effects of experimental uncertainty. Subsequent implementation of the model in a chemicalkinetics ODE solver shows good agreement of times to ignition vs initial temperature in isochoric thermal explosion calculations with the results of the high-energy asymptotic theory. We also compare the resultant ZND structures to those of one of our previously calibrated burn models.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

A Template-based Approach to the Photometric Classification of SN 1991bg-like Supernovae in the SDSS-II Supernova Survey

The use of SNe Ia to measure cosmological parameters has grown significantly over the past two decades. However, there exists a significant diversity in the SN Ia population that is not well understood. Overluminous SN 1991T-like and subluminous SN 1991bg-like objects are two characteristic examples of peculiar SNe. The identification and classification of such objects is an important step in studying what makes them unique from the remaining SN population. With the upcoming Vera C. Rubin Observatory promising on the order of a million new SNe over a 10 year survey, spectroscopic classifications will be possible for only a small subset of observed targets. As such, photometric classification has become an increasingly important concern in preparing for the next generation of astronomical surveys. Using observations from the Sloan Digital Sky Survey II (SDSS-II) SN Survey, we apply here an empirically based classification technique targeted at the identification of SN 1991bg-like SNe in photometric data sets. By performing dedicated fits to photometric data in the rest-frame redder and bluer bandpasses, we classify 16 previously unidentified 91bg-like SNe. Using SDSS-II host galaxy measurements, we find that these SNe are preferentially found in host galaxies with an older average stellar age than the hosts of normal SNe Ia. We also find that these SNe are found at a further physical distance from the center of their host galaxies. We find no statistically significant bias in host galaxy mass or specific star formation rate for these targets.

Astronomy & Astrophysics↗
DOE OSTI2021

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks

Abstract Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PayamDiba/CoNSEPT

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference.

Dibaeinia, Payam↗
DOE OSTI2021

An Experimental Study of the Solubility of Rare Earth Chloride Salts (La, Nd, Er) in HCl Bearing Water Vapor from 350 – 425 °C

In this work, the solubilities of the rare earth chlorides REECl 3 , where REE = (La, Nd, Er), were measured in HCl bearing water vapor from 350 – 425°C with water partial pressures ranging from 8 – 170 bar. Solubility data were fit to the Pitzer-Pabalan quasi-chemical model in order to extract thermodynamic parameters for the formation of the gaseous REE-chloride-water clusters REECl 3 (H 2 O) n . The data show that the solubility of the REE chlorides are orders of magnitude higher than salts such as NaCl or CuCl at low water fugacities, despite their sublimation energies being substantially higher. This enhanced solubility is likely due to the high enthalpy associated with binding a single water molecule to form the species REECl 3 (H 2 O), with derived enthalpies ranging from –378 to –465 kJ/mol. Addition of further water molecules to form higher order clusters (n > 1) involves enthalpy changes of ~ -20 kJ/mol, and are in effect thermodynamically suppressed over the temperature range 350 – 425°C. Despite the enhanced solubility of small REECl 3 water clusters, simulations of boiling processes demonstrate that the REE show highly conservative behavior, partitioning strongly into the dense aqueous phase. Not surprisingly, the presence of phosphates in the system makes this effect even more pronounced, completely immobilizing the REE. This would reduce transport in both the vapor and aqueous phase to negligible levels. However, we suggest that vapor phase transport of the REE may play a significant role in systems having a relatively low partial pressure of water (below the saturation point), where the relatively high stability of the first hydrated REE chloride clusters (REECl 3 (H 2 O) and REECl 3 (H 2 O) 2 ) will give a preference for gas transport of the REE relative to other elements. This can likely happen in systems involving a gas/melt exchange in fumarolic exhalations, where water vapor discharges at relatively low (close to atmospheric) pressures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Constraining neutrino oscillation and interaction parameters with the NOvA Near Detector and Far Detector data using Markov Chain Monte Carlo

This thesis reports a constraint of the neutrino oscillation parameters $\Delta m^{2}_{32}$, $\sin^2 \theta_{23}$, and $\delta_{CP}$ using the NuMI Off-Axis $\nu$ Appearance (NOvA) experiment's Near Detector (ND) data and Far Detector (FD) fake data set simultaneously. This thesis also reports a constraint on NOvA's systematic uncertainty model solely with its Near Detector data. The Hamiltonian Monte Carlo algorithm is used to estimate Bayesian Credible Intervals for the oscillation and interaction parameters. The $1\sigma$ Credible Intervals for $\sin^2 \theta_{23}$ are $(0.44, 0.512)$ $\cup$ $(0.536, 0.56)$, for $\Delta m^{2}_{32}$ $(2.41 \times 10^{-3}$ eV$^2,\ 2.52 \times 10^{-3}$ eV$^2)$, and for $\delta_{CP}$ $(0.74\pi,\ 1.1\pi)$ $\cup$ $(1.38\pi,\ 1.58\pi)$. The statistical power of the ND data constrains NOvA's interaction parameters, while the FD fake data constrains the oscillation parameters. This is the first analysis within NOvA to constrain the ND and FD prediction sim ultaneously, and to investigate the neutrino interaction modeling in the context of constraining the oscillation parameters. To constrain the ND data requires a sophisticated understanding of the neutrino interaction modeling and its uncertainties. The interested reader is advised to focus on Chapters 4 and 6, which discuss the ND selection, uncertainties, and ND-only fits to data. The reader interested in oscillation parameter constraints will find this in Chapter 7.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Model-independent description of B → D π ℓ ν decays

We introduce a new parametrization of B → D π ℓ ν form factors using a partial-wave expansion and derive bounds on the series coefficients using analyticity and unitarity. This is the first generalization of the model-independent formalism developed by Boyd, Grinstein, and Lebed for B → D ℓ ν to semileptonic decays with multihadron final states, and enables data-driven form-factor determinations with robust, systematically improvable uncertainties. Using this formalism, we extract the form-factor parameters for B → D 2 * ( → D π ) ℓ ν decays in a model-independent way from fits of data from the Belle Experiment. We find that the semileptonic data are compatible with the presence of two poles in the D π S-wave channel, which is the scenario preferred by nonleptonic decays and unitarized chiral perturbation theory. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Rejection Sampling with Autodifferentiation -- Case study: Fitting a Hadronization Model

We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Advanced Method Optimization for Sampling and Analysis Instrumentation

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. In conclusion, this strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical technique.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

GLEAM: Galaxy Line Emission & Absorption Modeling

We present Galaxy Line Emission & Absorption Modeling (gleam), a Python tool for fitting Gaussian models to emission and absorption lines in large samples of 1D extragalactic spectra. gleam is tailored to work well in batch mode without much human interaction. With gleam, users can uniformly process a variety of spectra, including galaxies and active galactic nuclei, in a wide range of instrument setups and signal-to-noise regimes. gleam also takes advantage of multiprocessing capabilities to process spectra in parallel. With the goal of enabling reproducible workflows for its users, gleam employs a small number of input files, including a central, user-friendly configuration in which fitting constraints can be defined for groups of spectra and overrides can be specified for edge cases. For each spectrum, gleam produces a table containing measurements and error bars for the detected spectral lines and continuum and upper limits for nondetections. For visual inspection and publishing, gleam can also produce plots of the data with fitted lines overlaid. In the present paper, we describe gleam’s main features, the necessary inputs, expected outputs, and some example applications, including thorough tests on a large sample of optical/infrared multi-object spectroscopic observations and integral field spectroscopic data. gleam is developed as an open-source project hosted at https://github.com/multiwavelength/gleam and welcomes community contributions.

79 ASTRONOMY AND ASTROPHYSICS↗