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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Nearly optimal state preparation for quantum simulations of lattice gauge theories

Here, we present several improvements to the recently developed ground-state preparation algorithm based on the quantum eigenvalue transformation for unitary matrices (QETU), apply this algorithm to a lattice formulation of U(1) gauge theory in (2+1) dimensions, as well as propose an alternative application of QETU, a highly efficient preparation of Gaussian distributions. The QETU technique was originally proposed as an algorithm for nearly optimal ground-state preparation and ground-state energy estimation on early fault-tolerant devices. It uses the time-evolution input model, which can potentially overcome the large overall prefactor in the asymptotic gate cost arising in similar algorithms based on the Hamiltonian input model. We present modifications to the original QETU algorithm that significantly reduce the cost for the cases of both exact and Trotterized implementation of the time evolution circuit. We use QETU to prepare the ground state of a U(1) lattice gauge theory in two spatial dimensions, explore the dependence of computational resources on the desired precision and system parameters, and discuss the applicability of our results to general lattice gauge theories. We also demonstrate how the QETU technique can be utilized for preparing Gaussian distributions and wave packets in a way which outperforms existing algorithms for as little as n q ≳ 2–5 qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid

In this study, several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
DOE OSTI2022

The Chocolate Chip Cookie Model: Dust Geometry of Milky Way–like Disk Galaxies

We present a new two-component dust geometry model, the Chocolate Chip Cookie model, where the clumpy nebular regions are embedded in a diffuse stellar/interstellar medium disk, like chocolate chips in a cookie. By approximating the binomial distribution of the clumpy nebular regions with a continuous Gaussian distribution and omitting the dust scattering effect, our model solves the dust attenuation process for both the emission lines and stellar continua via analytical approaches. Our Chocolate Chip Cookie model successfully fits the inclination dependence of both the effective dust reddening of the stellar components derived from stellar population synthesis and that of the emission lines characterized by the Balmer decrement for a large sample of Milky Way–like (MW-like) disk galaxies selected from the main galaxy sample of the Sloan Digital Sky Survey. Our model shows that the clumpy nebular disk is about 0.55 times thinner and 1.6 times larger than the stellar disk for MW-like galaxies, whereas each clumpy region has a typical optical depth of τ cl,V ~ 0.5 in the V band. After considering the aperture effect, our model prediction on the inclination dependence of dust attenuation is also consistent with observations. Not only that, in our model, the dust attenuation curve of the stellar population naturally depends on the inclination, and its median case is consistent with the classical Calzetti law. As the modeling constraints are from the optical wavelengths, our model is unaffected by the optically thick dust component, which however could bias the model's prediction of the infrared emissions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bulk synthesis of radiation resistant W – Ti – Cr – V compositionally complex alloys

Refractory compositionally complex alloys are candidate material systems for next generation advanced nuclear reactors. This work showcases the first successful bulk synthesis of low activation W–Ti based refractory compositionally complex alloys using arc melting and provides insights on using additive manufacturing for these compositions using directed energy deposition. Both techniques produce equiaxed grains composed of a tungsten matrix with Ti–V–Cr dendritic boundaries. The arc melted specimen possesses a multi-modal grain size distribution, while the directed energy deposition specimen possesses a more gaussian distribution of grain size. Both arc melted and directed energy deposition specimens demonstrate high thermal stability up to 900 °C, as well as promising radiation resistance with low loop formation and the presence of homogeneously distributed helium cavities maintaining small diameters at ≥10 dpa under simultaneous light (helium) and heavy (krypton) ion irradiation at 900 °C.

Arc melting (AM)↗
DOE OSTI2024

Reconciling calculations and measurements of inverse bremsstrahlung absorption

It was recently shown that the use of Coulomb logarithms appropriate for bremsstrahlung radiation (rather than transport processes) along with corrections for the Langdon effect and ion screening reproduced measurements of collisional absorption in well-characterized underdense plasmas [D. Turnbull et al., Phys. Rev. Lett. 130, 145103 (2023)]. However, it was recognized at the time that the use of the standard absorption-reduction factor from Langdon's seminal paper was inconsistent with the use of Coulomb logarithms that are thermally averaged over a Maxwellian electron-velocity distribution function. A more accurate approach would be to average over the expected super-Gaussian distribution function while accounting for the Gaunt factor's velocity dependence, which somewhat mitigates the Langdon effect; however, at that time, this theory matched the data less well. This conflict is now eliminated with the additional insight that the ionization state of our mid-Z ion species (when present) was lower than had been assumed, as evidenced by the Thomson-scattering data and time-dependent Cretin simulations. We are now able to show that an improved treatment of the Langdon effect provides the best match to data. Otherwise, the prior conclusions remain unchanged. We also show an example of the substantial expected impact to the absorption rate in calculations of indirect-drive hohlraums.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

LANL-TAMUS Graduate Fellowship Report

The goal of this project is to conduct analysis of 147 Sm neutron transmission (n,tot) data at DICER alongside previously collected neutron capture (n,γ) data and report updated resonance parameters and spins. Measurement and analysis of neutron-capture data, without neutron transmission data, has been conducted before and showed that the gamma widths, Γ γ , and neutron widths, Γn, both deviate from the distributions predicted by well-established theory. Γ γ is expected to follow a narrow Gaussian distribution independent of energy but instead a broadening of the distribution around 350 eV incident neutron energy was observed (P. E. Koehler et al. Phys. Rev. C 108, 142502 (2012)). Γ n is predicted to follow a chi-squared distribution with one degree of freedom known as the Porter-Thomas distribution but this prediction also failed above 350 eV incident neutron energy (P. E. Koehler et al. Phys. Rev. C 76, 025804 (2007)). This project seeks to reassess this past measurement by including neutron transmission data and simultaneously analyzing them alongside the neutron capture data to extract the most accurate measure of Γ γ and Γ n ever taken for the resonances of 147 Sm and see if theory holds or fails under a more refined measurement with newer data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Single- and multi-photon-induced ultraviolet excitation and photodissociation of CH 3 I probed by coincident ion momentum imaging

The UV-induced photodissociation dynamics of iodomethane (CH 3 I) in its A-band are investigated by time-resolved coincident ion momentum imaging using strong-field ionization as a probe. The delay-dependent kinetic energy distribution of the photofragments resulting from double ionization of the molecule maps the cleavage of the carbon–iodine bond and shows how the existence of a potential well in the di-cationic potential energy surfaces shapes the observed distribution at small pump–probe delays. Furthermore, the competition between single- and multi-photon excitation and ionization of the molecule is studied as a function of the intensity of the UV-pump laser pulse. Two-photon excitation to Rydberg states is identified by tracking the transformation of the delay-dependent singly-charged iodomethane yield from a pure Gaussian distribution at low intensity to a Gaussian with an exponentially decaying tail at higher intensities. In conclusion, dissociative ionization induced by absorption of three UV photons is resolved as an additional delay-dependent feature in the kinetic energy of the fragment ions detected in coincidence.

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

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings

Seismic fragility functions can be evaluated using the cloud analysis method with linear regression which makes three fundamental assumptions about the relation between structural response and seismic intensity: log-linear median relationship, constant standard deviation, and Gaussian distributed errors. While cloud analysis with linear regression is a popular method, the degree to which these individual and compounded assumptions affect the fragility and the risk of mid-rise buildings needs to be systematically studied. This paper conducts such a study considering three building archetypes that make up a bulk of the building stock: RC moment frame, steel moment frame, and wood shear wall. Gaussian kernel methods are employed to capture the data-driven variations in the median structural response and standard deviation and the distributions of residuals with the intensity level. With reference to the Gaussian kernels approach, it is found that while the linear regression assumptions may not affect the fragility functions of lower damage states, this conclusion does not hold for the higher damage states (such as the Complete state). In addition, the effects of linear regression assumptions on the seismic risk are evaluated. For predicting the demand hazard, it is found that the linear regression assumptions can impact the computed risk for larger structural response values. However, for predicting the loss hazard with downtime as the decision variable, linear regression can be considered adequate for all practical purposes.

58 GEOSCIENCES↗
DOE OSTI2025

Photometry of Outer Solar System Objects from the Dark Energy Survey. II. A Joint Analysis of Trans-Neptunian Absolute Magnitudes, Colors, Light Curves and Dynamics

For the 696 trans-Neptunian objects (TNOs) with absolute magnitudes 5.5 < H r < 8.2 detected in the Dark Energy Survey, we characterize the relationships between their dynamical state and physical properties—namely H r , indicating size; colors, indicating surface composition; and flux variation semiamplitude A, indicating asphericity and surface inhomogeneity. We seek “birth” physical distributions that can recreate these parameters in every dynamical class. We show that the observed colors of these TNOs are consistent with two Gaussian distributions in griz space, “near-infrared bright” (NIRB) and “near-infrared faint” (NIRF), presumably an inner and outer birth population, respectively. We find a model in which both the NIRB and NIRF H r and A distributions are independent of current dynamical states, supporting their assignment as birth populations. All objects are consistent with a common rolling p(H r ), but NIRF objects are significantly more variable. Cold classicals (CCs) are purely NIRF, while hot classical (HC), scattered, and detached TNOs are consistent with ≈ 70% NIRB and the resonance NIRB fractions show significant variation. The NIRB components of the HCs and of some resonances have broader inclination distributions than the NIRFs, i.e. their current dynamics retains information about birth location. We find evidence for radial stratification within the birth NIRB population, in that HC NIRBs are on average redder than detached or scattered NIRBs; a similar effect distinguishes CCs from other NIRFs. We estimate total object counts and masses of each class within our H r range. These results will strongly constrain models of the outer solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors

This study presents magnetic resonance velocimetry (MRV) Reynolds Stress measurements in a periodic hill channel with a hill Reynolds number of Re = 29,500. The velocity encoding scheme is based on the ICOSA6 method with six icosahedral encoding directions and multiple encoding values are measured to increase the dynamic range. The full Reynolds stress tensor is obtained from a voxel-wise three-dimensional Gaussian fit using the magnitude data of all acquisitions. The MRV results are compared to a wall-resolved large eddy simulation and laser Doppler velocimetry measurements conducted in the same channel. It is shown that the MRV Reynolds stress data have excellent precision and agree qualitatively with the reference data. However, there are apparent systematic deviations. One of the most prominent error contributions is the signal attenuation caused by higher orders of motion, which leads to an overestimation of the turbulence level. Another fundamental error is identified in the assumption that the turbulence is Gaussian distributed. With the presented reconstruction technique, the MRV data are fitted to a statistical model, and depending on the examined flow setup, the Gaussian model can lead to considerable errors. Possible ways of how to reduce all identified errors are presented. In summary, this technique enables Reynolds stress tensor measurements in complex internal flows with high dynamic range and excellent precision. However, several issues need to be resolved to make the turbulence quantification more accurate.

42 ENGINEERING↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗
DOE OSTI2021

Xenon implantation of nanodiamond films for targeted color center emission at sub-nanosecond time scales

In this work, the lifetime of nitrogen-vacancy color centers within nanodiamonds is reduced from 550±13 ps to 297±10 ps through the implantation of xenon. Coupled-mode analysis is employed to characterize the mechanism responsible for the reduction in emission lifetime. The observed spectral lineshape is found to be consistent with a Voigt profile consisting of two Lorentzian resonant peaks at 637 nm and 811 nm that are inhomogeneously broadened by a Gaussian distribution. A convolution of the frequency-domain Lorentzian output, with linewidths less than 1 nm, from the coupled-mode system of equations with a Gaussian with standard deviation of 85 nm is performed to generate the Voigt profile. The shortened emission lifetime is found to be consistent with a coupled mode theory model incorporating coupling between nitrogen-vacancy and xenon-vacancy color centers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Measuring sub-surface spatially varying thermal conductivity of silicon implanted with krypton

The thermal properties of semiconductors following exposure to ion irradiation are of great interest for the cooling of electronic devices; however, gradients in composition and structure due to irradiation often make the measurement difficult. Furthermore, the nature of spatial variations in thermal resistances due to spatially varying ion irradiation damage is not well understood. In this work, we develop an advancement in the analysis of time-domain thermoreflectance to account for spatially varying thermal conductivity in a material resulting from a spatial distribution of defects. We then use this method to measure the near-surface (≲1 μm) thermal conductivity of silicon wafers irradiated with Kr + ions, which has an approximate Gaussian distribution centered 260 nm into the sample. Our numerical analysis presented here allows for the spatial gradient of thermal conductivity to be extracted via what is fundamentally a volumetric measurement technique. We validate our findings via transmission electron microscopy, which is able to confirm the spatial variation of the sub-surface silicon structure, and provide additional insight into the local structure resulting from the effects of ion bombardment. Thermal measurements found the ion stopping region to have a nearly 50x reduction in thermal conductivity as compared to pristine silicon, while TEM showed the region was not fully amorphized. Our results suggest this drastic reduction in silicon thermal conductivity is primarily driven by structural defects in crystalline regions along with boundary scattering between amorphous and crystalline regions, with a negligible contribution being due to implanted krypton ions themselves.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Spatial Intermittency of Particle Distribution in Relativistic Plasma Turbulence

Abstract Relativistic magnetically dominated turbulence is an efficient engine for particle acceleration in a collisionless plasma. Ultrarelativistic particles accelerated by interactions with turbulent fluctuations form nonthermal power-law distribution functions in the momentum (or energy) space, f ( γ ) d γ ∝ γ − α d γ , where γ is the Lorenz factor. We argue that in addition to exhibiting non-Gaussian distributions over energies, particles energized by relativistic turbulence also become highly intermittent in space. Based on particle-in-cell numerical simulations and phenomenological modeling, we propose that the bulk plasma density has lognormal statistics, while the density of the accelerated particles, n , has a power-law distribution function, P ( n ) dn ∝ n − β dn . We argue that the scaling exponents are related as β ≈ α + 1, which is broadly consistent with numerical simulations. Non-space-filling, intermittent distributions of plasma density and energy fluctuations may have implications for plasma heating and for radiation produced by relativistic turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Energy migration and scintillation kinetics in compositionally complex (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce single crystal scintillator

It is well-established that compositional tuning through binary admixture can improve scintillation performance in several materials systems, including Ce-activated garnets. Although recent work on ternary or quaternary cation admixture shows promise, the impact of this increased compositional complexity on thermal stability and carrier-defect dynamics has not been addressed. Here, we investigate a compositionally complex garnet, (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce (GYTLAG), grown by the Czochralski method using temperature-dependent photoluminescence (PL), PL decay, and thermoluminescence (TL). PL and PL decay measurements support a thermally activated Tb 3+ -Ce 3+ energy transfer, where Tb 3+ emission dominates below 60 K, but Ce 3+ emission increases from 20-300 K. Thermal quenching of Ce 3+ emission occurs around T 50 = 508 K, with an activation energy of 0.6 eV. TL and wavelength-resolved TL spectra from 20–500 K show that GYTLAG contains similar trap groups to LuAG but with a broader distribution of glow peaks below room temperature, possibly caused by quaternary cation mixing. A combination of dose dependence, partial cleaning and initial rise, and glow curve fitting to a first order continuous Gaussian distribution model are used to understand the contribution of electronic point defects to scintillation decay and afterglow at room temperature. Furthermore, these results inform how increased compositional complexity influences recombination dynamics in garnet scintillators.

Compositionally complex↗