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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Inferring surface solar absorption from broadband satellite measurements

An atmospheric solar radiation model and surface albedo models that include wavelength dependence and surface anisotropy are combined to study the possibility of inferring the surface solar absorption from satellite measurements. The model includes ocean, desert, pasture land, savannah, and bog surface categories. Problems associated with converting narrowband measurements to broadband quantities are discussed, suggesting that it would be easier to infer surface solar absorption from broadband measurements directly. The practice of adopting a linear relationship between planetary and surface albedo to estimate surface albedos from satellite measurements is examined, showing that the linear conversion between broadband planetary and surface albedos is strongly dependent on vegetation type. It is suggested that there is a linear slope-offset relationship between surface and surface-atmosphere solar absorption.

Cess, Robert D.↗
DOE OSTI2018

Temporal Coupling of Subsurface and Surface Soil CO 2 Fluxes: Insights From a Nonsteady State Model and Cross-Wavelet Coherence Analysis

Inferences about subsurface CO 2 fluxes often rely on surface soil respiration (R soil ) estimates because directly measuring subsurface microbial and root respiration (collectively, CO 2 production, S Total ), is difficult. To evaluate how well R soil serves as a proxy for S Total , we applied the nonsteady state DEconvolution of Temporally varying Ecosystem Carbon componenTs model (0.01-m vertical resolution), using 6-hourly data from a Wyoming grassland, in six simulations that cross three soil types (clay, sandy loam, and sandy) with two depth distributions of subsurface biota. We used cross-wavelet coherence analysis to examine temporal coherence (localized linear correlation) and offsets (lags) between S Total and R soil and fluxes and drivers (e.g., soil temperature and moisture). Cross-wavelet coherence revealed higher coherence between fluxes and drivers than linear regressions between concurrent variables. Soil texture and moisture exerted the strongest controls over coherence between CO 2 fluxes. Coherence between CO 2 fluxes in all soil types was strong at short (~1 day) and long periods (>8 days), but soil type controlled lags, and rainfall events decoupled the fluxes at periods of 1–8 days for several days in sandy soil, up to 1 week in sandy loam, and for a month or more in clay soil. Concentrating root and microbial biomass nearer the surface decreased lags in all soil types and increased coherence up to 10% in clay soil. The assumption of high temporal coherence between R soil and S Total is likely valid in dry, sandy soil, but may lead to underestimates of short-term STotal in semiarid grasslands with fine-grained and/or wet soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Probabilistic inference of the structure and orbit of Milky Way satellites with semi-analytic modelling

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity v max and the radius r max at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for v max and r max , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. Furthermore, the technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1993

Intraplate deformation, stress in the lithosphere and the driving mechanism for plate motions

The initial research proposed was to use the predictions of geodynamical models of mantle flow, combined with geodetic observations of intraplate strain and stress, to better constrain mantle convection and the driving mechanism for plate motions and deformation. It is only now that geodetic observations of intraplate strain are becoming sufficiently well resolved to make them useful for substantial geodynamical inference to be made. A model of flow in the mantle that explains almost 90 percent of the variance in the observed longwavelength nonhydrostatic geoid was developed.

Albee, Arden L.↗
NASA NTRS2022

Direct-imaging Discovery and Dynamical Mass of a Substellar Companion Orbiting an Accelerating Hyades Sun-like Star with SCExAO/CHARIS

We present the direct-imaging discovery of a substellar companion in orbit around a Sun-like star member of the Hyades open cluster. So far, no other substellar companions have been unambiguously confirmed via direct imaging around main-sequence stars in Hyades. The star HIP 21152 is an accelerating star as identified by the astrometry from the Gaia and Hipparcos satellites. We detected the companion, HIP 21152 B, in multiple epochs using the high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We also obtained the stellar radialvelocity data from the Okayama 188 cm telescope. The CHARIS spectroscopy reveals that HIP 21152 B’s spectrum is consistent with the L/T transition, best fit by an early T dwarf. Our orbit modeling determines the semimajor axis and the dynamical mass of HIP 21152 B to be 17 +7.2 −3.8 and 27.8 +8.4 −5.4 M Jub , respectively. The mass ratio of HIP 21152 B relative to its host is ≈2%, near the planet/brown dwarf boundary suggested by recent surveys. Mass estimates inferred from luminosity-evolution models are slightly higher (33–42 MJup). With a dynamical mass and a well-constrained age due to the system’s Hyades membership, HIP 21152 B will become a critical benchmark in understanding the formation, evolution, and atmosphere of a substellar object as a function of mass and age. Our discovery is yet another key proof of concept for using precision astrometry to select direct-imaging targets.

Masayuki Kuzuhara↗
DOE OSTI2024

Prime VI

SAND2025-03757O Prime VI is a distribution-of-disease outbreak model calibration code based on variational inference. It accompanies a publication for submission to Statistics in Medicine journal, and the code will be maintained for open-source use on Sandia's GitLab. The software provides methods for calibrating an epidemiological model to measured case-count data for a multitude of correlated spatial regions. The code solves a Bayesian inverse problem for model calibration where the posterior over-model parameters are approximated through a custom implementation of variational inference. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗
NASA NTRS2017

Comparative Study of Three Reconnection X Line Models at the Earth's Dayside Magnetopause Using in Situ Observations

This work examines the large-scale aspects of magnetic field reconnection at the Earth's dayside magnetopause. We use two sets of reconnection events, which are identified mostly by the in situ detection of accelerated and Alfvenic plasma flows. We intercompare three analytical models that predict the reconnection X line location and orientation, namely, the Trattner et al. (2007) and Swisdak and Drake (2007) models and also a modified version of the component merging model. In the first set of reconnection observations, we show three fortuitous, quasi-simultaneous dayside magnetopause crossing events where two widely separated spacecraft detect reconnection signatures, and the X line location and orientation can be inferred from the observations. We compare X line model predictions to those inferred from observations. These three reconnection events indicate the presence of an extended (greater than 7 Earth radii in length), component-type reconnection X line on Earth's dayside magnetopause connecting and structuring the reconnection signatures at locations far apart. In the second set of reconnection events, we analyze the X line models' performance in predicting the observed reconnection outflow direction, i.e., its north-south and/or east-west senses, in a total of 75 single, rather than multiple and quasi-simultaneous, magnetopause crossing events, where reconnection-associated plasma flows were clearly present. We found that the Swisdak and Drake's (2007) X line model performs slightly better, albeit not statistically significant, when predicting both accelerated plasma flow north-south and east-west components in 73% and 53% of the cases, respectively, as compared to the Trattner et al. (2007) model (70% north-south and 42% east-west) and the modified component merging model (66% north-south and 50% east-west).

Souza, V. M.↗
NASA NTRS1994

Report on concentrations, lifetimes, and trends of CFCs, halons, and related species

The atmospheric lifetimes of molecules containing chlorine and bromine are the dominant parameters influencing their ability to promote enhanced ozone destruction in the stratosphere. The purpose of this report is to assess the present state of knowledge of the lifetimes of halocarbons using two complementary approaches. First, a time series of measurements of gas concentrations is used together with information on their emissions histories and a computational model of atmospheric circulation and chemistry to infer lifetimes through a mass balance approach. Second, an atmospheric chemical-dynamical model is used with detailed information on the chemistry and spectroscopy of the molecules of interest to calculate lifetimes. The lifetimes determined by these two methods are then compared. Attention is focused most closely on fully halogenated chlorine- and bromine-containing molecules, primarily the chlorofluorocarbons, and the halons, because of their ability to deliver chlorine and bromine to the stratosphere. Some attention will be given to those molecules containing hydrogen, which are subject to removal in the troposphere primarily by reaction with OH and by other processes.

Kaye, J. A.↗
NASA NTRS1991

Modelled and measured strain in mascon basins on the moon

The close association of wrinkle ridges and grabens with mascon basins on the Moon has suggested that the responsible compression and extension resulted from basin subsidence and peripheral flexing of the lithosphere. The distribution of grabens and wrinkle ridges associated with mascon basins has been further used along with elastic plate bending models to constrain the thickness of the lithosphere at the time of their formation. Kinematic models for basin subsidence have also been developed and compared with strains inferred from grabens and wrinkle ridges. Note that kinematic models may be preferable to dynamic models because the strain associated with tectonic features can be compared directly with model predictions and because fewer assumptions are required for their calculations, such as perfect elasticity and specific values of the elastic moduli. Also, if the results from kinematic models compare favorably with the strain estimated across the tectonic features on the Moon, then a global strain field may not be necessary. Herein, the strain inferred for wrinkle ridges and grabens was compared to that calculated from a simple kinematic subsidence model for mascon basins on the Moon.

Golombek, M. P.↗
NASA NTRS2019

Automatic Incorporation of Flaws into Cad and FEA Models Based On Measured NDE Data

Automatic incorporation of non-destructive evaluation (NDE) data into CAD and finite element (FE) models for structural analysis is essential for accurate residual-life and strength analyses of composite structures. Currently, two significant but independent challenges exist for incorporating NDE data in such analyses: interpretation of the NDE signals to locate and characterize the structural defects and the incorporation of that information into structural analysis tools. The first challenge will be addressed through linear inversion of calculated synthetic responses generated by a simulation engine to evaluate flaw size, location, and other characteristics. The second challenge will be addressed by building on prior and parallel defect modeling efforts to integrate suitable flaw models into the CAD/finite-element representation of a specimen. Ultrasonic NDE information is usually represented using C-Scans of time-of-flight or intensity information. Other modalities such as thermography and computed tomography (CT) could also be used to characterize the structure. This information will first be projected into material coordinates and then aligned with the specimen’s finite element mesh. The NDE data can then be incorporated by inserting flaw geometry into the CAD and FE analysis model and automatic modification of local material properties and boundary conditions. This process will enable the use of structural analysis tools even for practical modalities such as ultrasound and thermography which may not directly give accurate-enough geometric information to fully define the flaw. The full flaw characteristics will be inferred using prior expert and modeling knowledge to categorize flaw signatures. Outputs for this project will include demonstrated methods for creating structural analysis geometric models that incorporate flaw characterization information; these models can then be used directly in popular commercial FEA software. In addition, the primary deliverable for the project will include a software that will be able to automatically incorporate different kinds of flaws into an existing FE models. The models will be generated in standard file formats to enable direct import into the analysis software.

Adarsh Krishnamurthy↗
DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Response Surface Modeling Using Multivariate Orthogonal Functions

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Morelli, Eugene A.↗
DOE OSTI2025

Joint cosmic density reconstruction from photometric and spectroscopic samples

ABSTRACT We reconstruct the dark matter density field from spatially overlapping spectroscopic and photometric redshift catalogues through a field-level forward modelling approach. Instead of directly inferring the underlying density field, we find the best-fitting initial Gaussian fluctuations that will evolve into the observed cosmic volume. To account for the substantial uncertainty of photometric redshifts we employ a differentiable continuous Poisson process. As an initial test, we construct a mock based on the upcoming Prime Focus Spectrograph combined with photometric sample modelled on the Subaru Hyper Suprime-Cam. Depending on the statistic of interest, we find improvements in cosmic structure classification equivalent to 50–100 per cent more spectroscopic targets by combining relatively sparse spectroscopic with dense photometric samples.

Horowitz, B.↗
NASA NTRS1995

Radiative flux opens new window on climate research

For several decades, global satellite observations have been made of the rate at which electromagnetic energy (radiative flux) is emerging from the top of the atmosphere of our planet in the spectral range of about 0.2-50.0 microns. At the same time, models have been developed to infer the radiative flux at the surface from the values observed by the satellites at the upper boundary. The balance of incoming and outgoing radiative flux (radiation budget) at both boundaries, determines the net gain or loss of the radiative energy within an atmospheric column. Climate researchers can use the radiative flux as a tool to validate climate models, separate the radiative impact of clouds from surface and atmosphere contributions, and to understand the global hydrological cycle. When applied to physical processes occurring at the surface, information on the radiative flux has the potential to substantially advance our understanding of the transport of heat, moisture, and momentum across the surface/atmosphere interface. Geophysicists of many disciplines stand to benefit from efforts to improve the use of this latter untapped resource. Oceanographers can improve the representation of the selective absorption of radiation in the oceans; biologists and ecologists can improve their models for carbon dioxide exchange and biological heating in oceans; agronomists can model more realistically biomass and crop yields; and environmentalists can obtain better assessment of natural resources of radiation.

Pinker, R. T.↗
DOE OSTI2025

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

human-machine collaboration↗
DOE OSTI2023

Canonical and noncanonical Hamiltonian operator inference

Here, a method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enabling Robust Exoplanet Atmospheric Retrievals with Gaussian Processes

Atmospheric retrievals are essential tools for interpreting exoplanet transmission and eclipse spectra, enabling quantitative constraints on the chemical composition, aerosol properties, and thermal structure of planetary atmospheres. The James Webb Space Telescope (JWST) offers unprecedented spectral precision, resolution, and wavelength coverage, unlocking transformative insights into the formation, evolution, climate, and potential habitability of planetary systems. However, this opportunity is accompanied by challenges: modeling assumptions and unaccounted-for noise or signal sources can bias retrieval outcomes and their interpretation. To address these limitations, we introduce a Gaussian process (GP)-aided atmospheric retrieval framework that flexibly accounts for unmodeled features and correlated noise in exoplanet spectra. We validate this method on synthetic JWST observations, and show that GP-aided retrievals reduce bias in inferred abundances and better capture model–data mismatches than traditional approaches. We also introduce the concept of mean squared error to quantify the trade-off between bias and variance, arguing that this metric more accurately reflects retrieval performance than bias alone. We then reanalyze the NIRISS/SOSS JWST transmission spectrum of WASP-96 b, finding that GP-aided retrievals yield broader constraints on CO 2 and H 2 O, possibly alleviating tension between previous retrieval results and equilibrium predictions. Our GP framework provides precise and accurate constraints while highlighting regions where models fail to explain the data. As JWST matures and future facilities come online, a deeper understanding of the limitations of both data and models will be essential, and GP-enabled retrievals like the one presented here offer a principled path forward.

Rotman, Yoav [Arizona State Univ., Tempe, AZ (Unit↗