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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. Here we introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

We report the Boltzmann equation with the Bhatnagar-Gross-Krook collision model (Boltzmann-BGK equation) has been employed to describe multiscale flows, i.e., from the hydrodynamic limit to free molecular flow. In this study we employ physics-informed neural networks (PINNs) to solve forward and inverse problems via the Boltzmann-BGK formulation (PINN-BGK), enabling PINNs to model flows in both the continuum and rarefied regimes. In particular, the PINN-BGK is composed of three sub-networks, i.e., the first for approximating the equilibrium distribution function, the second for approximating the non-equilibrium distribution function, and the third one for encoding the Boltzmann-BGK equation as well as the corresponding boundary/initial conditions. By minimizing the residuals of the governing equations and the mismatch between the predicted and provided boundary/initial conditions, we can approximate the Boltzmann-BGK equation for both continuous and rarefied flows. For forward problems, the PINN-BGK is utilized to solve various benchmark flows given boundary/initial conditions, e.g. Kovasznay flow, Taylor-Green flow, cavity flow, and micro Couette flow for Knudsen number up to 5. For inverse problems, we focus on rarefied flows in which accurate boundary conditions are difficult to obtain. We employ the PINN-BGK to infer the flow field in the entire computational domain given a limited number of interior scattered measurements on the velocity without using the (unknown) boundary conditions. Results for the two-dimensional micro Couette and micro cavity flows with Knudsen numbers ranging from 0.1 to 10 indicate that the PINN-BGK can infer the velocity field in the entire domain with good accuracy. Finally, we also present some results on using transfer learning to accelerate the training process. Specifically, we can obtain a three-fold speedup comparted to the standard training process (e.g., Adam plus L-BFG-B) for the two-dimensional flow problems considered in our work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments

The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.

Aney, Noah [Fermilab; U. Chicago (main)]↗
NASA NTRS1976

Secular acceleration of Phobos and Q of Mars

Reductions of Mariner 9 TV data of Phobos and Deimos tend to corroborate the existence of a secular acceleration of Phobos commensurate with two recently reported values based on a reprocessing of earth-based data. These values of secular acceleration have been used together with Mariner 9 data on the physical size of Phobos and earth-based photoelectric observations which infer a carbonaceous composition for Phobos to place upper and lower bounds of 50 and 150, respectively, on the tidal dissipation function of Mars. The corresponding upper and lower bounds on the tidal lag angle are 0.19 and 0.57 deg.

Smith, J. C.↗
NASA NTRS2016

On the Connection Between Microbursts and Nonlinear Electronic Structures in Planetary Radiation Belts

Using a dynamical-system approach, we have investigated the efficiency of large-amplitude whistler waves for causing microburst precipitation in planetary radiation belts by modeling the microburst energy and particle fluxes produced as a result of nonlinear wave-particle interactions. We show that wave parameters, consistent with large amplitude oblique whistlers, can commonly generate microbursts of electrons with hundreds of keV-energies as a result of Landau trapping. Relativistic microbursts (greater than 1 MeV) can also be generated by a similar mechanism, but require waves with large propagation angles Theta (sub k)B greater than 50 degrees and phase-speeds v(sub phi) greater than or equal to c/9. Using our result for precipitating density and energy fluxes, we argue that holes in the distribution function of electrons near the magnetic mirror point can result in the generation of double layers and electron solitary holes consistent in scales (of the order of Debye lengths) to nonlinear structures observed in the radiation belts by the Van Allen Probes. Our results indicate a relationship between nonlinear electrostatic and electromagnetic structures in the dynamics of planetary radiation belts and their role in the cyclical production of energetic electrons (E greater than or equal to 100 keV) on kinetic timescales, which is much faster than previously inferred.

plasmas↗
NASA NTRS1977

Soft electrons as a possible heat source for Jupiter's thermosphere

The 850 K exospheric temperature inferred for Jupiter from the radio-occultation experiments on Pioneers 10 and 11 is shown to imply a heat input of 0.25-0.5 erg/sq cm/sec. One possible source of this energy is precipitation of electrons from a warm plasma (temperature corresponding to energies of the order of 30-500 eV). A mechanism is suggested wherein the presence of this plasma can be accounted for by centrifugal acceleration and adiabatic compression of ionospheric electrons and protons. Present ideas of the source strength of ionospheric plasma, however, give heating rates that are too small by one to two orders of magnitude, although inferences from direct plasma measurements suggest that the required plasma is indeed present.

Hunten, D. M.↗
DOE OSTI2026

Supercharging simulation-based inference for Bayesian optimal experimental design

Abstract Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Electron Energization by Inertial Alfvén Waves in Density Depleted Flux Tubes at Jupiter

Juno satellite observations have illustrated substantial examples of broadband electron energization up to 10 5 –10 6 eV levels. In order to explain these observations, we use a hybrid gyrofluid kinetic-electron model in an untilted dipolar topology to illustrate energization to high levels in weak current conditions by inertial Alfvén waves close to the Jupiter ionosphere for ambient plasma densities and magnetic field perturbations inferred from Juno satellite observations. The key to the high energization is the extremely low densities evident in the observations which necessitates the acceleration of electrons to very high velocities in order to carry the field-aligned current.

58 GEOSCIENCES↗
NASA NTRS1987

DE 1 observations of theta aurora plasma source regions and Birkeland current charge carriers

Detailed analyses of the DE 1 high-altitude plasma instrument electron and ion data have been performed for four passes during which theta auroras were observed. The data indicate that the theta auroras occur on what appear to be closed field lines with particle signatures and plasma parameters that are quite similar to those of the magnetospheric boundary plasma sheet. The field-aligned currents computed from particle fluxes in the energy range 18-13 keV above the theta auroras are observed to be generally downward on the dawnside of the arcs with a narrower region of larger (higher density) upward currents on the duskside of the arcs. These currents are carried predominantly by field-aligned beams of accelerated cold electrons. Of particualr interest in regions of upward field-aligned current are downward electron beams at energies less than the inferred potential drop above the spacecraft.

Menietti, J. D.↗
DOE OSTI2024

Improvement and generalization of ABCD method with Bayesian inference

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors, such as the capabilities and the performance of the accelerator and detectors, the use and exploitation of the available information, the design of search strategies and observables, as well as the proposal of new models. We focus on the use of the information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning techniques and tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by pp\to hh\to b\bar b b \bar b p p → h h → b b ‾ b b ‾ , selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with 1% and 0.5% true signal fractions in the dataset. We also show that the method is robust against the absence of signal. We discuss potential prospects for taking this Bayesian data-driven paradigm into more realistic scenarios.

Alvarez, Ezequiel↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
NASA NTRS1971

Velocities of fragments from bursting gas reservoirs.

A solution is obtained from a simplified approach for the problem of the motion of two fragments driven into a vacuum after rupture of a container filled with gas. Following rupture, the two container fragments are driven in opposite directions. From the separation developed between the moving fragments, the originally contained gas escapes to the vacuum, perpendicular to the direction of motion of the fragments, and with locally sonic velocity. The present solution removes two restrictions of an earlier similar approach: (1) the speed of the gas within the original volume (and consequently, by inference, that of the fragments) is small relative to the sonic escape velocity, and (2) the volume between the separating fragments while they are accelerating undergoes negligible change from the original volume.

Taylor, D. E.↗
NASA NTRS1972

Radiation-pressure-driven mass loss from quasi-stellar objects.

It is shown that radiation pressure acting on the material in the outer envelopes of QSOs will cause mass outflow unless the mass M of the QSO exceeds a critical value Ms. If the QSOs are at cosmological distances, Ms is on the order of 10 to 100 billion solar masses, and otherwise scales according to intrinsic luminosity. Radiation-pressure-driven mass loss could be responsible for the absorption-line spectra observed in many QSOs. If so, the presence of blueshifted absorption lines in some but not all QSOs indicates that typically M is of the same order as Ms. It is also suggested that under radiative acceleration certain ratios of emission-line to absorption-line redshifts may be preferred as a result of a line-locking mechanism. Inferences are made concerning the magnitude of the gravitational component of the redshift. Possible tests of various aspects of the model are discussed.

Mushotzky, R. F.↗
NASA NTRS1980

Geophysical parameters of the earth-moon system

Doppler tracking data from Lunar Orbiter 4 have been combined with laser ranging data from lunar retroreflectors to yield a number of geophysical and geodetic parameters for the earth and moon. This joint solution gives values of (1) the lunar principal polar moment C/M R squared = 0.3905 plus or minus 0.0023, (2) GM(E) = 398600.461 plus or minus 0.026 cu km/sq s, and (3) an earth/moon mass ratio at 81.300587 plus or minus 0.000049. Also determined are the harmonics of a complete lunar gravity field through degree and order 5, the obliquity of the lunar pole, selenocentric coordinates of the lunar retroreflectors, geocentric coordinates of the McDonald Observatory, and the lunar secular acceleration. The lunar potential Love number is weakly determined at 0.022 plus or minus 0.013, and a suprisingly large dissipation of rotational energy is inferred, though either solid body tidal dissipation or liquid core mantle interactions could be causes.

Ferrari, A. J.↗
NASA NTRS1999

Modelling Hard Gamma-Ray Emission from Supernova Remnants

The observation by the CANGAROO (Collaboration of Australia and Nippon Gamma Ray Observatory at Outback) experiment of TeV emission from SN 1006, in conjunction with several instances of non-thermal X-ray emission from supernova remnants, has led to inferences of super-TeV electrons in these extended sources. While this is sufficient to propel the theoretical community in their modelling of particle acceleration and associated radiation, the anticipated emergence in the next decade of a number of new experiments probing the TeV and sub-TeV bands provides further substantial motivation for modellers. In particular, the quest for obtaining unambiguous gamma-ray signatures of cosmic ray ion acceleration defines a "Holy Grail" for observers and theorists alike. This review summarizes theoretical developments in the prediction of MeV-TeV gamma-rays from supernova remnants over the last five years, focusing on how global properties of models can impact, and be impacted by, hard gamma-ray observational programs, thereby probing the supernova remnant environment. Properties of central consideration include the maximum energy of accelerated particles, the density of the unshocked interstellar medium, the ambient magnetic field, and the relativistic electron-to-proton ratio. Criteria for determining good candidate remnants for observability in the TeV band are identified.

Baring, Matthew G.↗
NASA NTRS2000

Modelling Hard Gamma-Ray Emission from Supernova Remnants

The observation by the CANGAROO experiment of TeV emission from SN 1006, in conjunction with several instances of non-thermal X-ray emission from supernova remnants, has led to inferences of super-TeV electrons in these extended sources. While this is sufficient to propel the theoretical community in their modelling of particle acceleration and associated radiation, the anticipated emergence in the next decade of a number of new experiments probing the TeV and sub-TeV bands provides further substantial motivation for modellers. In particular, the quest for obtaining unambiguous gamma-ray signatures of cosmic ray ion acceleration defines a "Holy Grail" for observers and theorists alike. This review summarizes theoretical developments in the prediction of MeV-TeV gamma-rays from supernova remnants over the last five years, focusing on how global properties of models can impact, and be impacted by, hard gamma-ray observational programs, thereby probing the supernova remnant environment. Properties of central consideration include the maximum energy of accelerated particles, the density of the unshocked interstellar medium, the ambient magnetic field, and the relativistic electron-to-proton ratio. Criteria for determining good candidate remnants for observability in the TeV band are identified.

Baring, Matthew↗
NASA NTRS2018

Using a Knowledge Graph to Discover Earth Science Information

Knowledge graphs link key entities within a specific domain to other entities via relationships. Researchers are able to mine these relationships from numerous sources to infer new knowledge. Text extraction from peer-reviewed papers and scientific reports are untapped resources that can be leveraged by knowledge graphs to accelerate scientific discovery.

Freitag, Brian↗
DOE OSTI2023

Advanced data analysis in inertial confinement fusion and high energy density physics

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.

47 OTHER INSTRUMENTATION↗