Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Physical quantities”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Low and moderate x gluon contribution to exclusive Compton scattering processes

We revisit the high energy semi-classical description of the exclusive processes DVCS, TCS, and Double DVCS by explicitly keeping track of the Feynman x dependence in both the hard and the hadronic matrix elements. This is achieved by a modification of the standard shock wave approximation to derive the effective Feynman rules, which leads to a generic expression on which we then perform a partial twist expansion to get rid of quantities suppressed by the proper physical scales. We obtain a compact factorized master formula that can be used to investigate the Bjorken limit at leading twist. In particular, we recover the full one-loop result in the collinear limit for pure gluon exchange with the target. Finally, we discuss the subtleties in taking the simultaneous collinear and small x limit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Bi-fidelity variational auto-encoder for uncertainty quantification

Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-VAE) designed to estimate the uncertainty associated with a QoI from low-fidelity (LF) and high-fidelity (HF) samples of the QoI. Here, this model allows for the approximation of the statistics of the HF QoI by leveraging information derived from its LF counterpart. Specifically, we design a bi-fidelity auto-regressive model in the latent space which is integrated within the VAE’s probabilistic encoder–decoder structure. An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost. Additionally, we introduce the concept of the bi-fidelity information bottleneck (BF-IB) to provide an information-theoretic interpretation of the proposed BF-VAE model. Our numerical results demonstrate that the BF-VAE leads to considerably improved accuracy, as compared to a VAE trained using only HF data, when limited HF data is available.

42 ENGINEERING↗
DOE OSTI2022

Spatial resolution in secondary-electron microscopy

Here, we first review the significance of resolution and contrast in electron microscopy, and the effect of the electron optics on these two quantities. We then outline the physics of the generation of secondary electrons (SE) and their transport and emission from the surface of a specimen. Contrast and resolution are discussed for different kinds of SE imaging in scanning (SEM) and scanning-transmission (STEM) instruments, with some emphasis on the observation of individual atoms and atomic columns in a thin specimen. The possibility of achieving atomic resolution from a bulk specimen at SEM energies is also considered.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Model-independent determination of the strong-phase difference between D 0 and D ¯ 0 → π + π − π + π − decays

Measurements of the strong-phase difference between D 0 and D ¯ 0 → π + π − π + π − are performed in bins of phase space. The study exploits a sample of quantum-correlated D D ¯ mesons collected by the BESIII experiment in e + e − collisions at a center-of-mass energy of 3.773 GeV, corresponding to an integrated luminosity of 2.93 fb − 1 . Here, D denotes a neutral charm meson in a superposition of flavor eigenstates. The reported results are valuable for measurements of the C P -violating phase γ (also denoted ϕ 3 ) in B ± → D K ± , D → π + π − π + π − decays, and the binning schemes are designed to provide good statistical sensitivity to this parameter. The expected uncertainty on γ arising from the precision of the strong-phase measurements, when applied to very large samples of B -meson decays, is around 1.5° or 2°, depending on the binning scheme. The binned strong-phase parameters are combined to give a value of F + 4 π = 0.746 ± 0.010 ± 0.004 for the C P -even fraction of D 0 → π + π − π + π − decays, which is around 30% more precise than the previous best measurement of this quantity. Published by the American Physical Society 2024

Ablikim, M.↗
DOE OSTI2025

Nucleon tensor form factors at large N c

We investigate nucleon tensor form factors in the large- N c limit. In this picture, the nucleon emerges as a state of the N c valence quarks, which were bound by pion mean fields that were created by the presence of the valence quarks self-consistently. We find that the tensor charge ( g T u − d = 0.99 ) and the anomalous tensor magnetic moment ( κ T u + d = 7.61 ) are dominated by valence quarks, while the tensor quadrupole moment ( Q T u − d = − 7.02 ) shows significant sea quark effects. We examine how these quantities vary as the average size of the pion mean field is changed, showing interpolation between nonrelativistic quark and Skyrme limits. We also observe that g T u − d and κ T u + d depend weakly on the pion mass. In contrast, Q T u − d exhibits strong enhancement near the chiral limit. The numerical results are in good agreement with available lattice quantum chromodynamics (QCD) data and provide predictions for unmeasured quantities. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Systematic uncertainties from higher-twist corrections in DIS at large x

We investigate the systematic uncertainties and potential biases arising from the inclusion of large- x corrections to proton and deuteron deep inelastic scattering (DIS) data in global quantum chromodynamics (QCD) analyses. Using the CTEQ-JLab framework, we examine various approaches to implementing higher-twist corrections in nucleon structure functions and off-shell PDF modifications in deuteron targets. We analyze how these components interact and influence the determination of the d -quark PDF and the neutron structure function at large x . We find that it is very important to consider isospin-dependent higher-twist corrections in order to minimize implementation biases in the extracted quantities. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Modification of Quark-Gluon Distributions in Nuclei by Correlated Nucleon Pairs

We extend the QCD Parton Model analysis using a factorized nuclear structure model incorporating individual nucleons and pairs of correlated nucleons. Our analysis of high-energy data from lepton deep-inelastic scattering, Drell-Yan, and W and Z boson production simultaneously extracts the universal effective distribution of quarks and gluons inside correlated nucleon pairs, and their nucleus-specific fractions. Such successful extraction of these universal distributions marks a significant advance in our understanding of nuclear structure properties connecting nucleon- and parton-level quantities. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2021

Evolution of the Thermodynamic Properties of Clusters of Galaxies out to Redshift of 1.8

The thermodynamic properties of the hot plasma in galaxy clusters retains information on the processes leading to the formation and evolution of the gas in their deep, dark matter potential wells. These processes are dictated not only by gravity but also by gas physics, e.g. AGN feedback and turbulence. Here, we study the thermodynamic properties, e.g. density, temperature, pressure, and entropy, of the most massive and the most distant (z > 1.2) SPT-selected clusters, and compare them with those of the nearby clusters (z < 0.1) to constrain their evolution as a function of time and radius. We find that thermodynamic properties in the outskirts of high redshift clusters are remarkably similar to the low redshift clusters, and their evolution follows the prediction of the self-similar model. Their intrinsic scatter is larger, indicating that the physical properties that lead to the formation and virialization of cluster outskirts show evolving variance. On the other hand, thermodynamic properties in the cluster cores deviates significantly from self-similarity indicating that the processes that regulate the core are already in place in these very high redshift clusters. This result is supported by the unevolving physical scatter of all thermodynamic quantities in cluster cores.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Semi-Lagrangian nodal discontinuous Galerkin method for the BGK model

In this work, we propose a semi-Lagrangian (SL) nodal discontinuous Galerkin (DG) solver for the BGK equation. The BGK model was introduced by Bhatnagar, Gross, and Krook [1] as a relaxation model for the fundamental Boltzmann equation [5], which describes the kinetic dynamic of rarefied gases with a probability distribution function. The challenges of designing efficient numerical schemes for the Boltzmann equation mainly come from its high dimensionality and complicated nonlinear collision operator. The BGK model gains interests since it has much lower computational cost, due to the relatively simple structure of the relaxation operator in replacement of the collision operator, while simultaneously preserving several important physical properties, such as macroscopic quantities and dissipation of entropy.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Statistics of the cosmic Mach number from numerical simulations of a cold dark matter universe

Results are presented of an analysis of the cosmic Mach number, M, the ratio of the streaming velocity, v, to the random velocity dispersion, sigma, of galaxies in a given patch of the universe, which was performed on the basis of hydrodynamical simulations of the cold dark matter scenario. Galaxy formation is modeled by application of detailed physical processes rather than by the ad hoc assumption of 'bias' between dark matter and galaxy fluctuations. The correlation between M and sigma is found to be very weak for both components. No evidence is found for a physical 'velocity bias' in the quantities which appear in the definition of M. Standard cold-dark-matter-dominated universes are in conflict, at a statistically significant level, with the available observation, in that they predict a Mach number considerably lower than is observed.

Suto, Yasushi↗
NASA NTRS2006

Development of a Boundary Layer Property Interpolation Tool in Support of Orbiter Return To Flight

A new tool was developed to predict the boundary layer quantities required by several physics-based predictive/analytic methods that assess damaged Orbiter tile. This new tool, the Boundary Layer Property Prediction (BLPROP) tool, supplies boundary layer values used in correlations that determine boundary layer transition onset and surface heating-rate augmentation/attenuation factors inside tile gouges (i.e. cavities). BLPROP interpolates through a database of computed solutions and provides boundary layer and wall data (delta, theta, Re(sub theta)/M(sub e), Re(sub theta)/M(sub e), Re(sub theta), P(sub w), and q(sub w)) based on user input surface location and free stream conditions. Surface locations are limited to the Orbiter s windward surface. Constructed using predictions from an inviscid w/boundary-layer method and benchmark viscous CFD, the computed database covers the hypersonic continuum flight regime based on two reference flight trajectories. First-order one-dimensional Lagrange interpolation accounts for Mach number and angle-of-attack variations, whereas non-dimensional normalization accounts for differences between the reference and input Reynolds number. Employing the same computational methods used to construct the database, solutions at other trajectory points taken from previous STS flights were computed: these results validate the BLPROP algorithm. Percentage differences between interpolated and computed values are presented and are used to establish the level of uncertainty of the new tool.

Greene, Francis A.↗
NASA NTRSDate not supplied

Comparison of Artemis 2 and Artemis 5 Model Outcomes Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

probabilistic risk assessment↗
DOE OSTI2020

Evolution of the Thermodynamic Properties of Clusters of Galaxies out to Redshift of 1.8

The thermodynamic properties of the hot plasma in galaxy clusters retains information on the processes leading to the formation and evolution of the gas in their deep, dark matter potential wells. These processes are dictated not only by gravity but also by gas physics, e.g. AGN feedback and turbulence. In this work, we study the thermodynamic properties, e.g. density, temperature, pressure, and entropy, of the most massive and the most distant ($z > 1.2$) SPT-selected clusters, and compare them with those of the nearby clusters ($z<0.1$) to constrain their evolution as a function of time and radius. We find that thermodynamic properties in the outskirts of high redshift clusters are remarkably similar to the low redshift clusters, and their evolution follows the prediction of the self-similar model. Their intrinsic scatter is larger, indicating that the physical properties that lead to the formation and virialization of cluster outskirts show evolving variance. On the other hand, thermodynamic properties in the cluster cores deviates significantly from self-similarity indicating that the processes that regulate the core are already in place in these very high redshift clusters. This result is supported by the unevolving physical scatter of all thermodynamic quantities in cluster cores.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Calibration of the DUNE Far Detector Using Cosmic-ray Muon Events

The Deep Underground Neutrino Experiment (DUNE) aims to set new limits on parameters associated with neutrino oscillations, neutrino astrophysics, and beyond the Standard Model (SM) searches such as nucleon decay. DUNE will quantify the magnitude of CP violation in the lepton sector, and determine the neutrino mass ordering. These benefit highly from the large target mass and excellent imaging, tracking, and particle identification capabilities of Liquid Argon Time Projection Chambers (LArTPCs). Detector calibration is essential to make precise physics measurements. For instance, accurate energy reconstruction is necessary for measuring many of the aforementioned quantities with the precision required for discovering new physics and fully exploiting the capabilities of the detector. Cosmic muons are a freely available natural source of calorimetric data and can be used for calibrating various detector parameters. This thesis provides an analysis of simulated cosmic-ray muon events generated with the Muon Simulation Underground (MUSUN) generator in the DUNE horizontal drift (HD) far detector (FD). The study focuses on analysing the energy and angular distribution of various classes of muon events, as well as characterising the different particles produced by cosmic muon interactions. The analysis of π0 → 2γ events within the cosmic-ray muon sample is presented in this thesis with a detailed study of reconstructing electromagnetic showers. The π0 mass is reconstructed within the DUNE FD, yielding a value of (136 ± 7) MeV/c2. Additionally, the thesis introduces methods for dE/dx calibration using simulated and reconstructed muon tracks. A calibration constant Ccal = (5.469 ± 0.003) × 10−3 ADC × tick/e is obtained through a model-dependent calibration process, where 1 tick corresponds to 500 ns of sampling time of an ADC. Furthermore, a calibration technique is presented, demonstrating precise translation from dQ/dx to dE/dx. This calibration method is applied to stopping muons, charged pions, and protons in the DUNE FD, addressing the measurement of energy loss in the detector volume. These are important calibrations of the DUNE FD and will contribute to achieving the exciting physics goals of the experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1985

Laser Systems

Tunable diode lasers are employed as radiation sources in high resolution infrared spectroscopy to determine spectral characteristics of gaseous compounds. With other laser systems, they are produced by Spectra-Physics, and used to monitor chemical processes, monitor production of quantity halogen lamps, etc. The Laser Analytics Division of Spectra-Physics credits the system's reliability to a program funded by Langley in the 1970s. Company no longer U.S.-owned. 5/22/97

Source record↗
DOE OSTI2024

Kaon mixing beyond the standard model with physical masses

We present nonperturbative results for beyond the standard model kaon mixing matrix elements in the isospin symmetric limit ( m u = m d ) of QCD, including a complete estimate of all dominant sources of systematic error. Our results are obtained from numerical simulations of lattice QCD with N f = 2 + 1 flavors of dynamical domain wall fermions. For the first time, these quantities are simulated directly at the physical pion mass m π ∼ 139 MeV for two different lattice spacings. We include data at three lattice spacings in the range a = 0.11 – 0.07 fm and with pion masses ranging from the physical value up to 450 MeV. Compared to our earlier work, we have added both direct calculations at physical quark masses and a third lattice spacing making the removal of discretization effects significantly more precise and eliminating the need for any significant mass extrapolation beyond the range of simulated data. We renormalize the lattice operators nonperturbatively using RI-SMOM off-shell schemes. These schemes eliminate the need to model and subtract nonperturbative pion poles that arises in the RI-MOM scheme and, since the calculations are performed with domain wall fermions, the unphysical mixing between chirality sectors is suppressed. Our results for the bag parameters in the MS ¯ scheme at 3 GeV are B K ≡ B 1 = 0.5240 ( 17 ) ( 54 ) , B 2 = 0.4794 ( 25 ) ( 35 ) , B 3 = 0.746 ( 13 ) ( 17 ) , B 4 = 0.897 ( 02 ) ( 10 ) and B 5 = 0.6882 ( 78 ) ( 94 ) , where the first error is from lattice uncertainties and the second is the uncertainty due to the perturbative matching to MS ¯ . Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Applying Physics-Informed Neural Networks to Solve Navier–Stokes Equations for Laminar Flow around a Particle

In recent years, Physics-Informed Neural Networks (PINNs) have drawn great interest among researchers as a tool to solve computational physics problems. Unlike conventional neural networks, which are black-box models that “blindly” establish a correlation between input and output variables using a large quantity of labeled data, PINNs directly embed physical laws (primarily partial differential equations) within the loss function of neural networks. By minimizing the loss function, this approach allows the output variables to automatically satisfy physical equations without the need for labeled data. The Navier–Stokes equation is one of the most classic governing equations in thermal fluid engineering. This study constructs a PINN to solve the Navier–Stokes equations for a 2D incompressible laminar flow problem. Flows passing around a 2D circular particle are chosen as the benchmark case, and an elliptical particle is also examined to enrich the research. The velocity and pressure fields are predicted by the PINNs, and the results are compared with those derived from Computational Fluid Dynamics (CFD). Additionally, the particle drag force coefficient is calculated to quantify the discrepancy in the results of the PINNs as compared to CFD outcomes. The drag coefficient maintained an error within 10% across all test scenarios.

Hu, Beichao (ORCID:0009000163215151)↗