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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 OSTI2024

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

We present Data-Driven Invertible Neural Surrogates of Atmospheric transmission, or DINSAT. DINSAT is a novel framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned -- by virtue of autodifferentiation and differentiable programming -- to construct a surrogate atmospheric profile to model the observed data. The framework has utility in (i) performing atmospheric correction, (ii) recasting spectral data between various modalities (e.g. radiance and reflectance at the surface and at the sensor), and (iii) inferring atmospheric transmission profiles, such as absorbing bands and their relative magnitudes. We demonstrate the utility of these methods by performing a canonical atmospheric correction task for the purposes of further analysis - in this case, target detection within a scene.

Koch, James V.↗
DOE OSTI2020

Heat Flux Analysis From IR Imaging on Proto-MPEX

The Prototype Material Plasma Exposure eXperiment (Proto-MPEX) is a linear device used to develop the source and heating concept for MPEX, an experiment to study plasma–material interactions for future fusion reactors. This paper highlights recent analysis and results on Proto-MPEX using an infrared (IR) camera, a key tool for imaging heat fluxes measured at the material target. The target heat fluxes are crucial to understanding the source and heating concepts. The recent analysis includes the application of homography and 3-D finite-element methods to provide between-shot measurements of the 2-D target heat flux profile. The IR camera inferred heat flux is shown to correlate with Langmuir probe inferred heat fluxes for a range of parameters in helicon only and helicon + electron cyclotron heated discharges. Results show both desirable central and undesirable edge heat flux for helicon source and electron heating on the target. These heat flux measurements are shown to be important for better understanding of the source and heating physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Pressure Profiles of Distant Galaxy Clusters in the Planck Catalogue

Successive releases of Planck data have demonstrated the strength of the Sunyaev–Zeldovich (SZ) effect in detecting hot baryons out to the galaxy cluster peripheries. To infer the hot gas pressure structure from nearby galaxy clusters to more distant objects, we developed a parametric method that models the spectral energy distribution and spatial anisotropies of both the Galactic thermal dust (GTD) and the cosmic microwave background (CMB), which are combined with the cluster SZ and dust signals. Taking advantage of the best angular resolution of the High Frequency Instrument channels (5 arcmin) and using X-ray priors in the innermost cluster regions that are not resolved with Planck, this modeling allowed us to analyze a sample of 61 nearby members of the Planck Catalogue of SZ sources (0<z<0.5, z-tilde =0.15) using the full mission data, as well as to examine a distant sample of 23 clusters (0.5<z<1, z-tilde =0.56) that have been recently followed-up with XMM-Newton and Chandra observations. We find that (i) the average shape of the mass-scaled pressure profiles agrees with results obtained by the Planck Collaboration in the nearby cluster sample, and that (ii) no sign of evolution is discernible between averaged pressure profiles of the low- and high-redshift cluster samples. In line with theoretical predictions for these halo masses and redshift ranges, the dispersion of individual profiles relative to a self-similar shape stays well below 10% inside r {sub 500} but increases in the cluster outskirts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Machine Learning Framework to Predict Images of Edge-on Protoplanetary Disks

The physical structure and properties of protoplanetary disks are typically derived from spatially resolved disk images. Edge-on disks in particular provide an important view point on the vertical structure and degree of settling of disks. Such analyses rely on radiative transfer (RT) calculations that are generally computationally intensive due to the high optical depth of disks. Here we present a machine learning framework that has the potential to dramatically speed up the forward modeling process by approximating the results of RT calculations. This framework, trained on an initial set of RT calculations, utilizes an autoencoder neural network to enable the generation of synthetic scattered light images of edge-on disks directly from a set of physical parameters. We demonstrate that this framework generates synthetic images 2–3 orders of magnitude faster than using RT calculations. These machine learning-generated images appear to approximate the RT images well, in particular preserving their size and shape. We also find a strong correlation between the latent space representations of the generated disk images and several of their associated physical parameters. Finally, we discuss potential changes to the framework, such as methods to further improve the image quality, extending the framework to multiple wavelengths, and inverting the process to infer physical parameters from observed images. Overall, these new tools have the potential to enable a more efficient and uniform analysis of edge-on disk properties and the initial conditions of planet formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

SAGAbg. I. A Near-unity Mass-loading Factor in Low-mass Galaxies via Their Low-redshift Evolution in Stellar Mass, Oxygen Abundance, and Star Formation Rate

Measuring the relation between star formation and galactic winds is observationally difficult. In this work we make an indirect measurement of the mass-loading factor (the ratio between the mass outflow rate and star formation rate) in low-mass galaxies using a differential approach to modeling the low-redshift evolution of the star-forming main sequence and mass–metallicity relation. We use Satellites Around Galactic Analogs (SAGA) background galaxies, i.e., spectra observed by the SAGA Survey that are not associated with the main SAGA host galaxies, to construct a sample of 11,925 spectroscopically confirmed low-mass galaxies from 0.01 ≲ z ≤ 0.21 and measure auroral line metallicities for 120 galaxies. The crux of the method is to use the lowest-redshift galaxies as the boundary condition of our model, and to infer a mass-loading factor for the sample by comparing the expected evolution of the low-redshift reference sample in stellar mass, gas-phase metallicity, and star formation rate against the observed properties of the sample at higher redshift. We infer a mass-loading factor of η m = $0.92^{+1.76}_{-0.74}$, which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different sets of assumptions needed for each approach. While our estimate of the mass-loading factor is in good agreement with recent galaxy simulations that focus on resolving the dynamics of the interstellar medium, it is smaller by over an order of magnitude than the mass-loading factor produced by many contemporary cosmological simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Information theory and the earth's density distribution

The present paper argues for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia leads to a density distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

Rubincam, D. P.↗
NASA NTRS1979

Mean sea level determination from satellite altimetry

The primary experiment on the Geodynamics Experimental Ocean Satellite-3 (GEOS-3) is the radar altimeter. This experiment's major objective is to demonstrate the utility of measuring the geometry of the ocean surface; i.e., the geoid. Results obtained from this experiment so far indicate that the planned objectives of measuring the topography of the ocean surface with an absolute accuracy of + or - 5 m can be met and perhaps exceeded. The GEOS-3 satellite altimeter measurements have an instrument precision in the range of + or - 25 cm to + or - 50 cm when the altimeter is operating in the 'short pulse' mode. After one year's operations of the altimeter, data from over 5000 altimeter passes have been collected. With the mathematical models developed and the altimeter data presently available, mapping of local areas of ocean topography has been realized to the planned accuracy levels and better. This paper presents the basic data processing methods employed and some interesting results achieved with the early data. Plots of mean sea surface heights as inferred by the altimeter measurements are compared with a detailed 1 by 1 deg gravimetric geoid.

Kahn, W. D.↗
NASA NTRS1979

Information Theory and the Earth's Density Distribution

An argument for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia lead to a density distribution which was surprisingly close to the optimum distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

Rubincam, D. P.↗
NASA NTRS1984

Strain analysis of the Leoville chondrite and conditions in asteroidal interiors

Deformed chondrules in the Leoville carbonaceous chondrite define a preferred orientation, also recognized previously in other CV3 chondrites. Identifying the strain mechanism is necessary for understanding the conditions which caused this compressional shortening. Luckly, the major mineralogical constituent of CV3 meteorites is olivine, for which experimental data on strain mechanisms exist. The conditions that caused strain in the olivine were obviously applicable to the whole meteorite. Possible mechanisms of olivine deformation are cataclasis, low temperature plasticity, power low creep, and diffusional flow, each of which occurs under different combinations of temperature and deviatoric stress. Cataclasis can be ruled out on the basis of its high deviatoric stress requirements; some fractures occur in chondrule olivines, but these may have formed during excavation or impact. Low temperature plasticity and power low creep occur by means of various types of lattice dislocations. However, methods of thermal decoration and chemical etching of dislocations failed to indicate evidence for either mechanism. Thus, the inference is that diffusional flow, characteristic of low to moderately high temperatures and low deviatoric stress, was the major deformation mechanism.

Cain, P. M.↗
NASA NTRS2004

Toward a Global Map of Raindrop Size Distributions: Rain-Type Classification and Its Implications for Validating Global Rainfall Products - Part 1

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Z(sub dr) between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere-Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

L'Ecuyer, Tristan S.↗
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↗
NASA NTRSDate not supplied

On the Probabilistic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

neural nets↗
DOE OSTI2016

INFERRING THE CORONAL DENSITY IRREGULARITY FROM EUV SPECTRA

Understanding the density structure of the solar corona is important for modeling both coronal heating and the solar wind. Direct measurements are difficult because of line-of-sight integration and possible unresolved structures. We present a new method for quantifying such structures using density-sensitive extreme ultraviolet line intensities to derive a density irregularity parameter, a relative measure of the amount of structure along the line of sight. We also present a simple model to relate the inferred irregularities to physical quantities, such as the filling factor and density contrast. For quiet-Sun regions and interplume regions of coronal holes, we find a density contrast of at least a factor of 3–10 and corresponding filling factors of about 10%–20%. Our results are in rough agreement with other estimates of the density structures in these regions. The irregularity diagnostic provides a useful relative measure of unresolved structure in various regions of the corona.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Limits on Subcritical Reactivity Determination using Rossi-α and Related Methods

The subcritical reactivity, ρ, of a neutron multiplying system is of interest to many factions of the nuclear community. The subcritical reactivity in a nuclear system can be inferred from measurements of the prompt neutron decay constant, α. Near critical, the reactivity is linearly proportional with α, but the linearity between these quantities does not hold in systems with low multiplication or significantly above prompt critical. This work examines a large range of subcritical prompt neutron decay constant measurements on a thermal HEU metal system. This system is designed to mimic a solution system and to have the optimal H/ 235 U ratio to minimize the critical mass. The reactivity of these measurements varies between 0$\$$ and 70$\$$ below critical. In addition to experimental measurements on the thermal system, simulations are performed on both the thermal HEU system and a fast HEU metal system. Both the fast and thermal results are compared to baseline simulations performed using criticality eigenvalue (KCODE) mode in MCNP®6.2. These simulations are used for comparisons on the linearity between α and ρ. Prompt neutron decay constant measurements in this work are performed using the Rossi-α method. The measurements are taken using a 3 He neutron detection system and a Los Alamos National Laboratory designed list-mode data acquisition system. A specially designed computer program determines α from list-mode data. The α-eigenvalue, or α at delayed critical, for the thermal HEU system is -199:4±4:4 s -1 as measured during this work. The α-eigenvalue for the fast HEU system was previously measured to be -8:5 x 10 -5 s -1 . The results from the thermal HEU system study suggest that the relationship between ρ and α is linear when k eff > 0:80 based on the simulation results. The experimental results also agree with this result, but have less fidelity. No experimental results are available for the fast HEU system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2016

Detection of enhancement in number densities of background galaxies due to magnification by massive galaxy clusters

We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE-) inferred masses in a sample of 19 galaxy clusters with median redshift z similar or equal to 0.42 selected from the South Pole Telescope SPT-SZ survey. These clusters are observed by the Megacam on the Magellan Clay Telescope though gri filters. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts zmedian similar or equal to 0.9 (low-z background) and z(median) similar or equal to 1.8 (high-z background). Stacking these populations, we detect the magnification bias effect at 3.3 sigma and 1.3 sigma for the low-and high-z backgrounds, respectively. We fit Navarro, Frenk and White models simultaneously to all observed magnification bias profiles to estimate the multiplicative factor. that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in. resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting. for the combined background populations with 1 sigma uncertainties is 0.83 +/- 0.24(stat) +/- 0.074(sys), indicating good consistency between the lensing and the SZE-inferred masses. We use our best-fitting eta to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. This work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST

Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants’ times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST. The inference of the time of infection is a critical parameter in testing the efficacy of clinical interventions in protecting against HIV-1 infection. For example, in clinical trials evaluating the efficacy of passively delivered antibodies (Abs) for preventing infections, accurate time of infection data are essential for discerning levels of the Abs required to confer protection, given the natural Ab decay rate in the human body. In such trials, genetic sequences from early in the infection are regularly sampled from study participants, generally prior to immune selection, when the viral population is still expanding and genetic diversity is low. In this particular setting of early viral growth, the Poisson method is superior to the alternative approach based on coalescent methods. This approach can also be applied in human vaccine trials, where accurate estimates of infection times help ascertain if vaccine-elicited immune protection wanes over time.

59 BASIC BIOLOGICAL SCIENCES↗