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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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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2026

Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses

Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to the observed data vector, moving it away from the true cosmological signal without affecting its statistical properties, so that analysts cannot infer the true result until the analysis is frozen and the blinding is lifted. The package computes these shifts using Firecrown likelihoods applied to data vectors stored in the SACC format, ensuring that the theoretical model used for blinding is identical to that used for inference whilst remaining agnostic to the specific observable being blinded. To prevent accidental unblinding, the original SACC file, containing the true cosmology, is encrypted. Although developed for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Smokescreen is applicable to any experiment using Firecrown likelihoods and the SACC data format.

Loureiro, Arthur [Stockholm U., OKC; Imperial Coll↗
DOE OSTI2024

The Modeled Seasonal Cycles of Surface N 2 O Fluxes and Atmospheric N 2 O

Nitrous oxide (N 2 O) is a greenhouse gas and stratospheric ozone-depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N 2 O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N 2 O (aN 2 O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N 2 O Model Intercomparison Project with tropospheric transport modeling to simulate aN 2 O at eight remote air sampling sites for modern and pre-industrial periods. Models show general agreement on the seasonal phasing of zonal-average N 2 O fluxes for most sites, but seasonal peak-to-peak amplitudes differ several-fold across models. The modeled seasonal amplitude of surface aN 2 O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN 2 O, inferred by the difference between the surface-troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ~1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre-industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN 2 O seasonality. Considering the large model spread, in situ aN 2 O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon-nitrogen cycles under ongoing global warming.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

A semiempirical model for the red supergiant's wind in Zeta Aurigae systems

A semiempirical model for the wind in four Zeta Aur binary systems is developed, basing the analyses on the velocity function, nonthermal velocities, and upper limits to Ne/N(H) inferred from observations of the winds from the cool supergiants. The model assumes that the energy fluxes needed to drive the wind are supplied by Alfven waves, since the acoustic wave flux estimated from line broadening is too low. The damping scale length of the waves, is found to change from very small values near the star to larger values of the order of the local radius further out in the wind. The temperature structure and hydrogen ionization are derived for these models. Limits to the stellar magnetic field are found, as well as a necessary constraint for the existence of cool massive winds, suggesting that the dividing line in the H-R diagram is where this constraint is violated.

Kuin, N. P. M.↗
DOE OSTI2016

Statistical tests of simple earthquake cycle models: Testing Earthquake Cycle Models

A central goal of observing and modeling the earthquake cycle is to forecast when a particular fault may generate an earthquake: a fault late in its earthquake cycle may be more likely to generate an earthquake than a fault early in its earthquake cycle. Models that can explain geodetic observations throughout the entire earthquake cycle may be required to gain a more complete understanding of relevant physics and phenomenology. Previous efforts to develop unified earthquake models for strike-slip faults have largely focused on explaining both preseismic and postseismic geodetic observations available across a few faults in California, Turkey, and Tibet. An alternative approach leverages the global distribution of geodetic and geologic slip rate estimates on strike-slip faults worldwide. Here we use the Kolmogorov-Smirnov test for similarity of distributions to infer, in a statistically rigorous manner, viscoelastic earthquake cycle models that are inconsistent with 15 sets of observations across major strike-slip faults. We reject a large subset of two-layer models incorporating Burgers rheologies at a significance level of α = 0.05 (those with long-term Maxwell viscosities η M <~ 4.0 × 10 19 Pa s and η M >~ 4.6 × 10 20 Pa s) but cannot reject models on the basis of transient Kelvin viscosity η K . Finally, we examine the implications of these results for the predicted earthquake cycle timing of the 15 faults considered and compare these predictions to the geologic and historical record.

58 GEOSCIENCES↗
DOE OSTI2024

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within 1$\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Signs of early-stage disk growth revealed with ALMA

We present ALMA 1.3 mm continuum, {sup 12}CO, C{sup 18}O, and SO data for the Class 0 protostars Lupus 3 MMS, IRAS 15398−3559, and IRAS 16253−2429 at resolutions of ∼100 au. By measuring a rotational profile in C{sup 18}O, a 100 au Keplerian disk around a 0.3 M{sub ⊙} protostar is observed in Lupus 3 MMS. No 100 au Keplerian disks are observed in IRAS 15398−3559 and IRAS 16253−2429. Nevertheless, embedded compact (<30 au) continuum components are detected. The C{sup 18}O emission in IRAS 15398−3559 shows signatures of infall with a constant angular momentum. IRAS 16253−2429 exhibits signatures of infall and rotation, but its rotational profile is unresolved. By fitting the C{sup 18}O data with our kinematic models, the protostellar masses and the disk radii are inferred to be 0.01 M{sub ⊙} and 20 au in IRAS 15398−3559, and 0.03 M{sub ⊙} and 6 au in IRAS 16253−2429. By comparing the specific angular momentum profiles from 10,000 au to 100 au in eight Class 0 and I protostars, we find that the evolution of envelope rotation can be described with conventional inside-out collapse models. In comparison with a sample of 18 protostars with known disk radii, our results reveal signs of disk growth, with the disk radius increasing as M{sub ∗}{sup 0.8±0.14} or t{sup 1.09±0.37} in the Class 0 stage, where M{sub *} is the protostellar mass and t is the age. The disk growth rate slows down in the Class I stage. In addition, we find a hint that the mass accretion rate declines as t{sup −0.26±0.04} from the Class 0 to the Class I stages.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The Mean Metal-line Absorption Spectrum of Damped Ly α Systems in BOSS

We study the mean absorption spectrum of the Damped Ly α (DLA) population at z ∼ 2.6 by stacking normalized, rest-frame-shifted spectra of ∼27,000 DLA systems from the DR12 of the Baryon Oscillation Spectroscopic Survey (BOSS)/SDSS-III. We measure the equivalent widths of 50 individual metal absorption lines in five intervals of DLA hydrogen column density, five intervals of DLA redshift, and overall mean equivalent widths for an additional 13 absorption features from groups of strongly blended lines. The mean equivalent width of low-ionization lines increases with N {sub H} {sub i}, whereas for high-ionization lines the increase is much weaker. The mean metal line equivalent widths decrease by a factor ∼1.1–1.5 from z ∼ 2.1 to z ∼ 3.5, with small or no differences between low- and high-ionization species. We develop a theoretical model, inspired by the presence of multiple absorption components observed in high-resolution spectra, to infer mean metal column densities from the equivalent widths of partially saturated metal lines. We apply this model to 14 low-ionization species and to Al iii, S iii, Si iii, C iv, Si iv, N v, and O vi. We use an approximate derivation for separating the equivalent width contributions of several lines to blended absorption features, and infer mean equivalent widths and column densities from lines of the additional species N i, Zn ii, C ii*, Fe iii, and S iv. Several of these mean column densities of metal lines in DLAs are obtained for the first time; their values generally agree with measurements of individual DLAs from high-resolution, high signal-to-noise ratio spectra when they are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation

Here we present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

THE SWIFT GRB HOST GALAXY LEGACY SURVEY. II. REST-FRAME NEAR-IR LUMINOSITY DISTRIBUTION AND EVIDENCE FOR A NEAR-SOLAR METALLICITY THRESHOLD

We present rest-frame near-IR (NIR) luminosities and stellar masses for a large and uniformly selected population of gamma-ray burst (GRB) host galaxies using deep Spitzer Space Telescope imaging of 119 targets from the Swift GRB Host Galaxy Legacy Survey spanning 0.03 < z < 6.3, and we determine the effects of galaxy evolution and chemical enrichment on the mass distribution of the GRB host population across cosmic history. We find a rapid increase in the characteristic NIR host luminosity between z ∼ 0.5 and z ∼ 1.5, but little variation between z ∼ 1.5 and z ∼ 5. Dust-obscured GRBs dominate the massive host population but are only rarely seen associated with low-mass hosts, indicating that massive star-forming galaxies are universally and (to some extent) homogeneously dusty at high redshift while low-mass star-forming galaxies retain little dust in their interstellar medium. Comparing our luminosity distributions with field surveys and measurements of the high-z mass–metallicity relation, our results have good consistency with a model in which the GRB rate per unit star formation is constant in galaxies with gas-phase metallicity below approximately the solar value but heavily suppressed in more metal-rich environments. This model also naturally explains the previously reported “excess” in the GRB rate beyond z ≳ 2; metals stifle GRB production in most galaxies at z < 1.5 but have only minor impact at higher redshifts. The metallicity threshold we infer is much higher than predicted by single-star models and favors a binary progenitor. Our observations also constrain the fraction of cosmic star formation in low-mass galaxies undetectable to Spitzer to be small at z < 4.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

A dependency-based modelling mechanism for problem solving

The paper develops a technique of dependency net modeling which relies on an explicit representation of justifications for beliefs held by the problem solver. Using these justifications, the modeling mechanism is able to determine the relevant lines of inference to pursue during problem solving. Three particular problem-solving difficulties which may be handled by the dependency-based technique are discussed: (1) subgoal violation detection, (2) description binding, and (3) maintaining a consistent world model.

London, P.↗
NASA NTRS2004

Modeling of Isotope Fractionation in Stratospheric CO2, N2O, CH4, and O3: Investigations of Stratospheric Chemistry and Transport, Stratosphere-Troposphere Exchange, and Their Influence on Global Isotope Budgets

We investigated the isotopic fractionation of CH4 and hydrogen (H2) in the stratosphere by incorporating isotope-specific rate coefficients into the Lawrence Livermore National Laboratory (LLNL) 2D model and comparing the model results with new observations from the NASA ER-2 aircraft (funded through a separate task under the Upper Atmosphere Research Program). The model results reveal that fractionation which occurs in the stratosphere has a significant influence on isotope compositions in the free troposphere, an important point which had previously been ignored, unrecognized or unquantified for many long-lived trace gases, including CH4 and H2 which we have focused our efforts on to date. Our analyses of the model results and new isotope observations have also been used to test how well the kinetic isotope effects are known, at least to within the uncertainties in model chemistry and transport. Overall, these results represent an important step forward in our understanding of isotope fractionation in the atmosphere and demonstrate that stratospheric isotope fractionation cannot be ignored in modeling studies which use isotope observations in the troposphere to infer the global budgets of CH4 (an important greenhouse gas) and of H2 (a gas whose atmospheric budget must be better quantified, particularly before a large human perturbation from fuel cell use is realized). Our analyses of model results and observations from the NASA ER-2 aircraft are briefly summarized separately below for CH4, H2, and H2O and for the contribution of these modeling studies to date to our understanding of isotope fractionation for N2O, CO2, and O3 as well.

Boering, Kristie A.↗
DOE OSTI2024

VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.

Conditional VAEs↗
NASA NTRS1985

Theoretical interpretation of the observed interplanetary magnetic field radial variation in the outer solar system

Observations of the azimuthal component of the IMF are evaluated through the use of an MHD model which shows the effect of magnetic flux tubes opening in the outer solar system. It is demonstrated that the inferred meridional transport of magnetic flux is consistent with predictions by the MHD model. The computed azimuthal and radial magnetic flux deficits are almost identical to the observations. It is suggested that the simplest interpretation of the observations is that meridional flows are created by a direct body force on the plasma. This is consistent with the analytic model of Nerney and Suess (1975), in which such flux deficits in the IMF arise naturally from the meridional gradient in the spiralling field.

Suess, S. T.↗
NASA NTRS2003

Three-Dimensional MHD Modeling of The Solar Corona and Solar Wind: Comparison with The Wang-Sheeley Model

We present simulation results from a tilted-dipole steady-state MHD model of the solar corona and solar wind and compare the output from our model with the Wang-Sheeley model which relates the divergence rate of magnetic flux tubes near the Sun (inferred from solar magnetograms) to the solar wind speed observed near Earth and at Ulysses. The boundary conditions in our model specified at the coronal base and our simulation region extends out to 10 AU. We assumed that a flux of Alfven waves with amplitude of 35 km per second emanates from the Sun and provides additional heating and acceleration for the coronal outflow in the open field regions. The waves are treated in the WKB approximation. The incorporation of wave acceleration allows us to reproduce the fast wind measurements obtained by Ulysses, while preserving reasonable agreement with plasma densities typically found at the coronal base. We find that our simulation results agree well with Wang and Sheeley's empirical model.

Usmanov, A. V.↗

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