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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1970

Data acquisition and reduction for the University of Virginia superconducting magnetic suspension and balance facility

The problems associated with data acquisition and reduction in the U. Va. superconducting magnetic suspension and balance facility are similar to those in free-flight ranges (or tunnels). The model undergoes a quasi-six-degree-of-freedom motion which must be monitored both in position and angular orientation from which the aerodynamics must be inferred. The data acquisition problem is made more difficult because geometric constraints prevent direct visual access to the model in the Mach 3 wind tunnel. The methods, accuracies, and problems associated with the acquisition of data are discussed.

Jacobson, I. D.↗
DOE OSTI2026

Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting

Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed z = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ$^{2}$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
NASA NTRS1981

On the growth of turbulent regions in laminar boundary layers

Turbulent spots evolving in a laminar boundary layer on a nominally zero pressure gradient flat plate are investigated. The plate is towed through an 18 m water channel, using a carriage that rides on a continuously replenished oil film giving a vibrationless tow. Turbulent spots are initiated using a solenoid valve that ejects a small amount of fluid through a minute hole on the working surface. A novel visualization technique that utilizes fluorescent dye excited by a sheet of laser light is employed. Some new aspects of the growth and entrainment of turbulent spots, especially with regard to lateral growth, are inferred from the present experiments. To supplement the information on lateral spreading, a turbulent wedge created by placing a roughness element in the laminar boundary layer is also studied both visually and with probe measurements. The present results show that, in addition to entrainment, another mechanism is needed to explain the lateral growth characteristics of a turbulent region in a laminar boundary layer. This mechanism, termed growth by destabilization, appears to be a result of the turbulence destabilizing the unstable laminar boundary layer in its vicinity. To further understand the growth mechanisms, the turbulence in the spot is modulated using drag-reducing additives and salinity stratification.

Gad-El-hak, M.↗
NASA NTRS1984

The asteroidal source region of ordinary chondrites

The final, Earth-impacting orbits of ordinary chondritic meteorites have a very special distribution. By use of visual radiant and time of fall data, as well as photographic fireball orbits (1) it is inferred that chondrite perihelia are concentrated near 1 A.U., eccentricities are usually rather high (approximately 0.5), and inclinations are low (approximately 10 deg). Velocity selection resulting from atmospheric ablation plays a significant role in determining this orbital distribution, but by no means suffices to explain it. The observed distribution is a fragile one, and can easily be destroyed by Earth and Venus perturbations. This places severe constraints on the location of the original source bodies, of which these meteorites are fragments. New calculations were made of the expected distribution of final orbits from a range of initial sources, taking into consideration close encounter planetary perturbations, secular resonance, destruction by collision in space, and atmospheric ablation.

Wetherill, G. W.↗
NASA NTRS1991

The perception of surface layout during low level flight

Although it is fairly well established that information about surface layout can be gained from motion cues, it is not so clear as to what information humans can use and what specific information they should be provided. Theoretical analyses tell us that the information is in the stimulus. It will take more experiments to verify that this information can be used by humans to extract surface layout from the 2D velocity flow field. The visual motion factors that can affect the pilot's ability to control an aircraft and to infer the layout of the terrain ahead are discussed.

Perrone, John A.↗
NASA NTRS2013

Preliminary Iron Distribution on Vesta

The distribution of iron on the surface of the asteroid Vesta was investigated using Dawn's Gamma Ray and Neutron Detector (GRaND) [1,2]. Iron varies predictably with rock type for the howardite, eucrite, and diogenite (HED) meteorites, thought to be representative of Vesta. The abundance of Fe in howardites ranges from about 12 to 15 wt.%. Basaltic eucrites have the highest abundance, whereas, lower crustal and upper mantle materials (cumulate eucrites and diogenites) have the lowest, and howardites are intermediate [3]. We have completed a mapping study of 7.6 MeV gamma rays produced by neutron capture by Fe as measured by the bismuth germanate (BGO) detector of GRaND [1]. The procedures to determine Fe counting rates are presented in detail here, along with a preliminary distribution map, constituting the necessary initial step to quantification of Fe abundances. We find that the global distribution of Fe counting rates is generally consistent with independent mineralogical and compositional inferences obtained by other instruments on Dawn such as measurements of pyroxene absorption bands by the Visual and Infrared Spectrometer (VIR) [4] and Framing Camera (FC) [5] and neutron absorption measurements by GRaND [6].

Mittlefehldt, David W.↗
NASA NTRS1992

Application of multipoint measurements for flow characterization

Some of the possibilities for inferring the structure of complicated flows from simultaneous measurements at many points are reviewed. Conditional sampling, pseudoflow visualization, stochastic estimation, and the proper orthogonal decomposition are briefly reviewed and illustrated by example. Resolution criteria for multipoint spatial arrays are proposed that minimize the possibilities for misinterpreting the data.

Glauser, Mark N.↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
NASA NTRS1993

The infrared light curve of Periodic Comet Halley 1986 III and its relationship to the visual light curve, C2, and water production rates

The near-IR light curve of Periodic Comet Halley 1986 III is analyzed and compared with C2 production, water production, and the visual light curve. This is the most complete IR light curve compiled to date for any comet. The scattering phase function at small sun-comet-earth angles is shown to affect the slope of near-IR light curve significantly. P/Halley's dust production, as inferred from the IR light curve showed an increased production rate near perihelion which appears to be correlated with the onset of significant jet activity. The near-IR light curve, visual light curve, C2, and water production rates displayed different heliocentric variations, suggesting that one parameter cannot be accurately estimated from another. This is particularly true of the early preperihelion visual light curve. A peak of 0.3-0.5 magnitude in the visual magnitude, representing the integrated brightness of the comet's visible coma, lagged the other parameters by about a day. The near-IR color, J-H, was less red during periods of strong dust activity.

Morris, Charles S.↗
DOE OSTI2025

CCQE-like $\nu_{e}$ Selection in SBND using Convolutional Visual Network

Neutrinos from the Booster Neutrino Beam (BNB) at Fermilab interact with argon in a Liquid Argon Time Projection Chamber (LArTPC) differently based on their flavour. By examining the particles produced in a charged-current interaction, both the interaction type and the neutrino flavour can be inferred. The Short Baseline Near Detector has the largest neutrino-argon cross section data to date, motivating in-depth studies of various cross-section channels and topologies. This project aims to select electron neutrino quasi-elastic-like (QE-like) interactions in SBND using Convolutional Visual Network (CVN) scores. The CVN is a neural network that processes visual information from an event and assigns scores corresponding to its likelihood of being each interaction type. An inclusive study of electron neutrino charged current interactions using CVN has already been conducted. This analysis aims to build on this study, further utilizing CVN scores to isolate electron neutrino QE-like interactions characterized by the presence of an electron and one or more protons ($N > 0$) in the final state. The project s goal is to contribute to the overall cross-section measurement efforts within the SBN program at Fermilab.

Breen, Genevieve [Mt. Holyoke Coll.]↗
DOE OSTI2025

Reno

Reno is a tool for creating, visualizing, and analyzing system dynamics models in Python. It additionally has the ability to convert models to PyMC, allowing Bayesian inference on models with variables that include prior probability distributions.

Martindale, Nathan [Oak Ridge National Laboratory ↗
NASA NTRS2015

Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing

In order to determine the required visual frame rate (FR) for minimizing prediction errors with out-the-window video displays at remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic fidelity simulations of landing aircraft and decided whether aircraft would stop as if to be able to make a turnoff or whether a runway excursion would be expected. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. The required frame rate was estimated using Bayes inference on prediction errors by linear FRextrapolation of event probabilities conditional on predictions (stop, no-stop). Furthermore estimates were obtained from exponential model fits to the parametric and non-parametric perceptual discriminabilities d' and A (average area under ROC-curves) as dependent on FR. Decision errors are biased towards preference of overshoot and appear due to illusionary increase in speed at low frames rates. Both Bayes and A - extrapolations yield a framerate requirement of 35 < FRmin < 40 Hz. When comparing with published results [12] on shooter game scores the model based d'(FR)-extrapolation exhibits the best agreement and indicates even higher FRmin > 40 Hz for minimizing decision errors. Definitive recommendations require further experiments with FR > 30 Hz.

deceleration perception↗
NASA NTRS1989

Star formation and the interstellar medium in two peculiar, nonspiral galaxies - NGC 1569 and NGC 3593

This paper discusses far-IR and optical observations aimed at investigating the far-IR energy distribution of two peculiar galaxies without spiral arms which are actively forming stars: NGC 1569, a Magellanic irregular galaxy, and NGC 3593, a dusty S0/a galaxy. The data are used to determine the characteristic temperatures of the dust and to infer dust and molecular gas masses which are combined with other data to explore the characteristics of the interstellar media. Visual-wavelength continuum and H-alpha images are presented and used to estimate current and past star formation rates and the efficiency of stellar creation.

Hunter, Deidre A.↗
NASA NTRS2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Non-NASA Center↗
NASA NTRS1981

Numerical models for Halley dust environments

The algebra, parameter values, and results of a reference model for the dust environment of Comet P/Halley are presented. The model employs simplified treatments of the icy conglomerate nucleus and of the hydrodynamic outflow of gas and dust. The model includes a gas production rate derived from observations of Halley's visual magnitude in 1910, gas and dust flux nonuniformly distributed over the nuclear surface, a dust distribution function consistent with large particle inferences from comet antitails, dust density variable with mass, and an approximation to the distribution of particles near the dust envelope. Many parameter values are derived by analogy with the results for other comets. The features are incorporated in a computer program which calculates the concentration, flux, and fluence of dust particles as functions of time, position and particle mass.

Divine, N.↗
NASA NTRS1985

Human factors of intelligent computer aided display design

Design concepts for a decision support system being studied at NASA Langley as an aid to visual display unit (VDU) designers are described. Ideally, human factors should be taken into account by VDU designers. In reality, although the human factors database on VDUs is small, such systems must be constantly developed. Human factors are therefore a secondary consideration. An expert system will thus serve mainly in an advisory capacity. Functions can include facilitating the design process by shortening the time to generate and alter drawings, enhancing the capability of breaking design requirements down into simpler functions, and providing visual displays equivalent to the final product. The VDU system could also discriminate, and display the difference, between designer decisions and machine inferences. The system could also aid in analyzing the effects of designer choices on future options and in ennunciating when there are data available on a design selections.

Hunt, R. M.↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗