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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Efficient many-jet event generation with flow matching

We apply for the first time, to the best of our knowledge, the flow matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the scattering matrix elements as implemented in the portable partonic event generator pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, which computationally are the most relevant ones. We find that the unweighting efficiencies improve by factors of 184 and 25, respectively, when compared to the standard approach of using a vegas-based optimization. We also compare continuous normalizing flows trained with flow matching against the previously studied normalizing flows based on coupling layers and find that the former leads to better results, faster training and a better scaling behavior across the studied multiplicity range, while the latter evaluate faster. When combining the advantages of both methods using the regflow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest final-state multiplicities.

Bothmann, E. [CERN; Gottingen U.] (ORCID:000000016↗
DOE OSTI2023

A Prototype Thick-Target Bremsstrahlung Model with Angularly-Dependent Emission in the MCNP6 ® Code

This document summarizes the current thick-target bremsstrahlung (TTB) model in MCNP and provides test results for an alternative implementation to improve the accuracy with reduced cost compared to full electron transport. It has been observed that the current TTB model produces inaccurate results in problems where the medium is thick with respect to electrons, but the photon distribution in the problem has a strong directionality. An example of such a simulation is detectors surrounding a metal target irradiated with a radiographic beam of high energy photons. The primary cause of this discrepancy is the current TTB method emits all bremsstrahlung photons in the same direction as the primary electron produced from each (γ, e ± ) interaction, leading to artificially forward peaked photon distributions for intermediate to high-energy incident photons. To improve the TTB model, we have implemented an angularly-dependent TTB model in a developer version of the MCNP6 ® code; for developers, this was done on the branch prototype/angular_ttb in the mcnp6 repo on bitbucket. The angularly-dependent TTB model accounts for the energy and scattering of electrons as they slow down in the current material, but does not sample the computationally expensive energy straggling, secondary electron events, and tracking electrons; this approach is significantly less computationally expensive than full electron transport and can be comparable to the original TTB method for problems with sufficiently complex materials and geometry. To evaluate the method, we have modeled a simple problem of a beam of 5 MeV photons incident on a sphere of plutonium surrounded by detectors at different deflection angles. For this problem, the angularly-dependent TTB produces a photon flux within 8.0% for a 90 degree deflection angle and 0.8% along the beam axis, as compared to the electron transport solution. This is an improvement compared to a 43% and 81% discrepancy with the original TTB method, respectively. The rest of this work includes the following: the first section details the current TTB treatment in MCNP, which has not been well documented elsewhere. Then, the modified TTB algorithm is detailed and the approximations compared to the condensed history algorithm are compared. Results are given comparing the two TTB methods to the condensed history transport algorithm. The appendix includes details for code developers on relevant electron transport implementation details and potential code improvements for future work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗
DOE OSTI2024

Analytic Sensitivity Coefficients for General Multigroup Infinite Medium k-Eigenvalue Problems

The general multigroup infinite medium k-eigenvalue neutron transport equation is used to derive analytic expressions for the infinite medium k-eigenvalue, the scalar neutron flux and adjoint, and the sensitivity of $k$ ∞ to perturbations in the multigroup nuclear data of a single species isotropic elastic scattering material. In the appendix, we present the multigroup nuclear data for U-235 and U-238 along with the corresponding k-eigenvalue, flux, adjoint, and sensitivity profiles, which include the sensitivity of $k$ ∞ to the total, fission, capture, and scattering macroscopic cross sections as well as to the group-to-group scattering cross section matrix, group neutron production, and the unconstrained and constrained fission neutron energy distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

EXPANSE: A time-of-flight EXPanded Angle Neutron Spin Echo spectrometer at the Second Target Station of the Spallation Neutron Source

EXPANSE, an EXPanded Angle Neutron Spin Echo instrument, has been proposed and selected as one of the first suite of instruments to be built at the Second Target Station of the Spallation Neutron Source at the Oak Ridge National Laboratory. This instrument is designed to address scientific problems that involve high-energy resolution (neV– μeV) of dynamic processes in a wide range of materials. The wide-angle detector banks of EXPANSE provide coverage of nearly two orders of magnitude in scattering wavenumbers, and the wide wavelength band affords approximately four orders of magnitude in Fourier times. This instrument will offer unique capabilities that are not available in the currently existing neutron scattering instruments in the United States. Specifically, EXPANSE will enable direct measurements of slow dynamics in the time domain over wide Q-ranges simultaneously and will also enable time-resolved spectroscopic studies. The instrument is expected to contribute to a diverse range of science areas, including soft matter, polymers, biological materials, liquids and glasses, energy materials, unconventional magnets, and quantum materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Discovery of enhanced lattice dynamics in a single-layered hybrid perovskite

Layered hybrid perovskites exhibit emergent physical properties and exceptional functional performances, but the coexistence of lattice order and structural disorder severely hinders our understanding of these materials. One unsolved problem regards how the lattice dynamics are affected by the dimensional engineering of the inorganic frameworks and their interaction with the molecular moieties. Here, we address this question by using a combination of spontaneous Raman scattering, terahertz spectroscopy, and molecular dynamics simulations. This approach reveals the structural dynamics in and out of equilibrium and provides unexpected observables that differentiate single- and double-layered perovskites. While no distinct vibrational coherence is observed in double-layered perovskites, an off-resonant terahertz pulse can drive a long-lived coherent phonon mode in the single-layered system. This difference highlights the dramatic change in the lattice environment as the dimension is reduced, and the findings pave the way for ultrafast structural engineering and high-speed optical modulators based on layered perovskites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

General Relativistic Implicit Monte Carlo Radiation-hydrodynamics

Abstract We report on a new capability added to our general relativistic radiation-magnetohydrodynamics code, Cosmos++ : an implicit Monte Carlo (IMC) treatment for radiation transport. The method is based on a Fleck-type implicit discretization of the radiation-hydrodynamics equations, but generalized for both Newtonian and relativistic regimes. A multiple reference frame approach is used to geodesically transport photon packets (and solve the hydrodynamics equations) in the coordinate frame, while radiation–matter interactions are handled either in the fluid or electron frames then communicated via Lorentz boosts and orthonormal tetrad bases attached to the fluid. We describe a method for constructing estimators of radiation moments using path-weighting that generalizes to arbitrary coordinate systems in flat or curved spacetime. Absorption, emission, scattering, and relativistic Comptonization are among the matter interactions considered in this report. We discuss our formulations and numerical methods, and validate our models against a suite of radiation and coupled radiation-hydrodynamics test problems in both flat and curved spacetimes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Halogenation-Dependent Effects of the Chlorosulfolipids of Ochromonas danica on Lipid Bilayers

The chlorosulfolipids are amphiphilic natural products with stereochemically complex patterns of chlorination and sulfation. Despite their role in toxic shellfish poisoning, potential pharmacological activities, and unknown biological roles, they remain understudied due to the difficulties in purifying them from natural sources. The structure of these molecules, with a charged sulfate group in the middle of the hydrophobic chain, appears incompatible with the conventional lipid bilayer structure. Questions about chlorosulfolipids remain unanswered partly due to the unavailability of structural analogues with which to conduct structure–function studies. Here, we approach this problem by combining enantioselective total synthesis and membrane biophysics. Using a combination of Langmuir pressure–area isotherms of lipid monolayers, fluorescence imaging of vesicles, mass spectrometry imaging, natural product isolation, small-angle X-ray scattering, and cryogenic electron microscopy, we show that danicalipin A (1) likely inserts into lipid bilayers in the headgroup region and alters their structure and phase behavior. Specifically, danicalipin A (1) thins the bilayer and fluidizes it, allowing even saturated lipid to form fluid bilayers. Lipid monolayers show similar fluidizing upon insertion of danicalipin A (1). Furthermore, we show that the halogenation of the molecule is critical for its membrane activity, likely due to sterically controlled conformational changes. Synthetic unchlorinated and monochlorinated analogues do not thin and fluidize lipid bilayers to the same extent as the natural product. Overall, this study sheds light on how amphiphilic small molecules interact with lipid bilayers and the importance of stereochemistry and halogenation for this interaction.

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

Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar

Abstract. Low-powered commercially available coherent Doppler lidar (CDL) wind profilers provide continuous measurement of vertical profiles of wind in the lower troposphere, usually close to or up to the top of the planetary boundary layer. The vertical extent of these wind profiles is limited by the availability of scatterers and thus varies substantially throughout the day and from one day to the next. This makes it challenging to develop continuous products that rely on CDL-observed wind profiles. In order to overcome this problem, we have developed a new method for wind profile retrievals from CDL that combines the traditional velocity-azimuth display (VAD) technique with optimal estimation (OE) to provide continuous wind profiles up to 3 km. The new method exploits the level-to-level covariance present in the wind profile to fill in the gaps where the signal-to-noise ratio of the CDL return is too low to provide reliable results using the traditional VAD method. Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. This method was tested using yearlong CDL measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility in 2019. Comparison with the ARM operational CDL wind profile product and collocated radiosonde wind measurements shows excellent agreement (R2>0.99) with no degradation in results where the traditional VAD provided a valid solution. In the region where traditional VAD does not provide results, the OE wind speed and wind vector have uncertainties of 3.44 and 4.33 m s−1, respectively. As a result, the new method provides additional information over the standard technique and increases the effective range of existing CDL systems without the need for additional hardware.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

VERA neutronics high-fidelity benchmark for a modern PWR core design

Nearly 20 years since the origin of the infamous 'Kord Smith Challenge', and after 10 years and hundreds of millions of dollars invested by the US Department of Energy into the development of the Virtual Environment for Reactor Applications (VERA), the capability to accurately simulate a modern pressurized water reactor (PWR) fuel cycle at the fuel rod level with high-fidelity Monte Carlo (MC) stochastic transport methods still evades us. The deterministic methods in VERA make approximations that allow it to be successfully benchmarked against measured data from hundreds of PWR fuel cycles, but none of these benchmarks provided information at the local fuel rod level or allowed quantification of these approximations' impact for modern PWR reload designs. Using MPACT to establish the estimated isotopic distributions and thermal hydraulic conditions in a recent and challenging reactor design, a 2D quarter-core benchmark problem was established and solved consistently with both MPACT, the 51-energy group deterministic transport solver in VERA, and Shift, the continuous-energy MC transport solver in VERA. Impacts of energy groups, ray spacing, and scattering treatment were evaluated. Though MPACT has been previously shown to perform well compared to MC methods for initial Cycle 1 cores with only fresh fuel, this is the first benchmark of MPACT for a challenging modern reload core with depleted fuel and burnable absorbers. This work establishes additional credibility for the methods in MPACT and highlights the efficiency of these methods relative to those in use by MC-based tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Momentum informed muon scattering tomography for monitoring spent nuclear fuels in dry storage cask

Development of an effective monitoring method for spent nuclear fuel (SNF) in a dry storage cask (DSC) is important to meet the increasing demand for dry storage investigations. The DSC investigation should provide information about the quantity of stored SNF, and quality assurance of materials should be possible without opening the cask. However, traditional nondestructive examination (NDE) methods such as x-rays are difficult to deploy for DSC investigation because a typical DSC is intentionally designed to shield against radiation. To address this challenge, cosmic ray muons (CRMs) are used as an alternative NDE radiation probe because they can easily penetrate an entire DSC system; however, a wide application of muons is often hindered due to the naturally low CRM flux (~10 4 muons/m 2 /min). This paper introduces a newly proposed imaging algorithm, momentum-informed muon scattering tomography (MMST) , and presents how a limitation of the current muon scattering tomography technique has been addressed by measuring muon momentum. To demonstrate its functionality, a commercial DSC with 24 pressurized light water reactor fuel assemblies (FAs) and the MMST system were designed in GEANT4. Three noticeable improvements were observed for MMST system as a DSC investigation tool: (1) a signal stabilization, (2) an enhanced capability to differentiate various materials, and (3) statistically increased precision to identify and locate missing FAs. The results show that MMST improves the investigation accuracy from 79 to 98% when one FA is missing and 51% to 88% when one-half FA is missing. The advancement of the NDE technique using CRM for DSC verification is expected to resolve long-standing problems in increasing demand for DSC inspections and nuclear security.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Large area position sensitive detector for thermal neutrons

Large area thermal neutron detectors are applied in many fields including industrial imaging, nuclear safeguarding, neutron scattering, and fundamental science. Historically, these detectors were based on 3 He gas proportional counters despite the limitations of 3 He detectors such as high cost, limited supply, non-uniform spatial resolution, and depth of absorption problems. Two alternatives to 3 He detectors are 6 Li-loaded glass scintillators, and powdered ZnS(Ag) scintillators mixed with 6LiF neutron converters. The 6 LiF/ZnS(Ag) scintillator has advantages over 6 Li glass as it is less expensive and can be produced in larger areas, although its self-absorption presents a problem. In this work, we developed a large area thermal neutron detector based on 6 LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding. We further modified and improved the method by 2D segmentation of the detector using modular multichannel readout electronics. This segmentation approach allows for a combination of large detector area, improved spatial resolution, and increased count rate. Furthermore, spatial resolution can be variable across the detector area by adjusting the segment size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

High-resolution neutron spectroscopy using backscattering and neutron spin-echo spectrometers in soft and hard condensed matter

The instruments best suited to performing high-energy-resolution neutron spectroscopy are spin-echo spectrometers and backscattering spectrometers. The development of these experimental techniques dates back almost half a century, and most major neutron scattering facilities operate mature spectrometers of one or both classes. Recent advances in instrumentation and neutron sources are enhancing their performance and expanding their capabilities, with the objective of enabling researchers to tackle new and more complex problems. Finally, in this Technical Review, we assess the current state of the art in high-energy-resolution neutron spectrometers, showcasing their role in the study of nanoscale dynamics in soft and biological materials, as well as disordered magnets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Benchmark calculations of infinite neutron matter with realistic two- and three-nucleon potentials

In this study, we present the equation of state of infinite neutron matter as obtained from highly realistic Hamiltonians that include nucleon-nucleon and three-nucleon coordinate-space potentials. We benchmark three independent many-body methods: Brueckner-Bethe-Goldstone (BBG), Fermi hypernetted chain/single-operator chain (FHNC/SOC), and auxiliary-field diffusion Monte Carlo (AFDMC). We find them to provide similar equations of state when the Argonne v 18 and the Argonne v' 6 nucleon-nucleon potentials are used in combination with the Urbana IX three-body force. Only at densities larger than about 1.5 the nuclear saturation density ( ρ 0 =0.16 fm ⁻3 ) the FHNC/SOC energies are appreciably lower than the other two approaches. The AFDMC calculations carried out with all of the Norfolk potentials fitted to reproduce the experimental trinucleon ground-state energies and nd doublet scattering length yield unphysically bound neutron matter, associated with the formation of neutron droplets. Including tritium β decay in the fitting procedure, as in the second family of Norfolk potentials, mitigates but does not completely resolve this problem. An excellent agreement between the BBG and AFDMC results is found for the subset of Norfolk interactions that do not make neutron-matter collapse, while the FHNC/SOC equations of state are moderately softer.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Coupled-channel approach to isotensor π π π scattering from lattice QCD

The quest to understand three-body dynamics from first-principle QCD includes the study of nonresonant and resonant systems. The isospin I = 2 system is of particular interest having no three-body resonance but featuring a resonance in a subchannel, while also being a coupled-channel problem. In this study, we calculate the finite-volume spectrum from lattice QCD at two different pion masses, map the amplitude to the infinite volume through a generalized Finite-Volume Unitarity three-body quantization condition, investigate the limit of a narrow ρ , and compare with an effective Lagrangian prediction at leading order. Chiral extrapolations between different pion masses are performed.

Feng, Yuchuan [The George Washington University] (↗