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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unifying simulation and inference with normalizing flows

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. Published by the American Physical Society 2025

Hadronic calorimiters↗
NASA NTRS2026

In-Space Demonstration of Spray-on Solar White Paint

Discs coated with spray-on solar white and two similar thermal control coatings were flown in low earth orbit for roughly five hours. While in orbit, the coated discs were exposed to direct sunlight and to deep space for short intervals. Measurements of disc temperatures showed that the spray-on solar white coating had the lowest absorption of sunlight. Numerical modeling was used to infer that spray-on solar white had a solar absorptance between 0.02 and 0.04 and a thermal emittance between 0.93 and 0.99, both of which are consistent with terrestrial laboratory measurements.

Solar White↗
DOE OSTI2024

Elucidating the formation mechanisms of zeolites using data-driven modelling and in-situ characterization

Zeolites are the main solid catalysts used by the chemical industry. The use of zeolites in separations and as shape selective catalysts requires control of the width and connectivity of their pores. 235 distinct zeolite frameworks have been synthesized to date, of over 2 million that have been proposed. Recent work indicates that the limitation is in large part kinetic: new synthetic pathways are required to access new zeolites. Organic cations are used to direct the synthesis towards specific zeolites. However, the molecular mechanisms by which cations direct the nucleation towards specific zeolites is not known. Elucidating these mechanisms is key to realize new zeolites for catalysis and separations, and is the focus of this project. This project developed and implemented a synergistic, data-driven computational and experimental approach to resolve the molecular pathways of nucleation, growth, and polymorph selection of zeolites and the role of organic cations in directing their formation. The project developed computationally efficient and accurate models for the study of the nucleation and growth of pure silica zeolites in molecular simulations, using machine learning with data from experiments. Simulations with these models were integrated with scanning tunneling electron microscopy, computer vision, and deep learning to unveil the molecular pathways of formation of a zeolite. Of particular interest in this project was to elucidate the role of amorphous precursors in the nucleation of the zeolite. Previous experiments indicate that zeolites are born within non-crystalline aggregates in which the silicates and organic cations have local and medium range order similar to that of the zeolite. The organic cations that direct the formation of zeolites and those that direct the formation of ordered mesoporous silicas are similar. We hypothesized that the frustrated attraction that for large organic cations leads to the formation of stable mesophases that direct the synthesis of mesoporous silicas, could promote the formation of metastable mesophases that can assist in the nucleation and polymorph selection of zeolites. The simulations resolved how structure directing agents build crystalline order and showed that mesoscopic pre-ordering occurs is not required to facilitate the nucleation of zeolites, because the synthesis occurs at high driving forces, where the barriers for nucleation are negligible. This project unveiled that polymorph selection in zeolite synthesis occurs after nucleation, opening a distinct area of control through the kinetics of growth and not through nucleation barriers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Initial Field Testing in the Deep Subsurface for the Thermal Breakout Project for Measuring In Situ Stress

The current state-of-the-art technology for in situ stress measurements involves an integrated approach that combines borehole breakout observations, drilling-induced tensile fractures, and hydraulic fracturing tests (i.e., mini-fracs); however, this methodology has several limitations that often prevent successful in situ stress measurements. One major limitation is that breakouts do not appear in every borehole and are generally a natural occurrence that cannot easily be controlled. Because borehole breakouts are used to measure the maximum horizontal in situ stress magnitude, the absence of borehole breakouts presents a major data gap for in situ stress measurements. As a response to this data gap a new thermal breakout technology is being developed that will provide a method for thermally inducing borehole breakouts and obtaining consistent measurements of the maximum horizontal stress magnitude. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which is directly correlated to the maximum horizontal stress magnitude. An initial component of the thermal breakout project involves developing a prototype downhole tool and performing field testing in the deep subsurface. The primary objective of the initial tool development and field testing is to provide a physical proof of concept for the thermal breakout technology. Three thermal breakout field tests were performed at approximately 4850 feet deep in the Sanford Underground Research Facility. Each test was performed in a borehole that had been previously used for overcoring stress measurements. The third and final test successfully created two diametrically opposed breakouts after deploying the thermal breakout tool for approximately 3 hours of continuous heating. Post-analysis of the thermally induced breakouts confirmed that the thermal breakout orientation directly corresponds to the known in situ stress orientation and magnitude.

Voegeli, Sam↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
NASA NTRS1983

SEASAT observations of lithospheric flexure seaward of trenches

Lithospheric flexure seaward of deep ocean trenches in SEASAT altimeter observations of the marine geoid. In fact, mechanical models of lithospheric flexure can be tested directly on the SEASAT altimeter data. A simple elastic model was used for the oceanic lithosphere and, after least squares adjustments, estimates of model parameters were recovered including Outer Rise (OR) amplitude, OR wavelength, and effective lithospheric thickness. Effective lithospheric thickness was recovered for five regions: the Mariana, the Kuril, the Philippine, the Aleutian and the Middle America OR. These results support the suggestion of Bodine et al. (1981) that effective thickness, T, increased with age of lithosphere in approximate accord with the relation T approximately equals x age to the 1/2 power where C approximately equals 4 km x my to the -1/2 power. Altimetric results agree more closely with this relation than do published results based on bathymetric data. The close agreement with the thickness-age relation suggests that there is no longer any need to assume that significant horizontal compression acts across the Kuril, Marianas and Izo-Bonin trenches. This thickness-age relation implies that flexural strength of the oceanic lithosphere is temperature controlled.

Mcadoo, D. C.↗
NASA NTRS1990

Rough surface scattering results based on bandpass autocorrelation forms

Surface-height autocorrelation forms such as Gaussian and exponential are often used in studies of near-normal incidence rough-surface scattering. Such models require the existence of a constant, or DC, value in the spectrum. The consequences of autocorrelation forms that correspond to spectral processes that are essentially bandpass in nature are examined. One such process is that of ocean wind waves. In this case, the spectral components do not extend down to zero frequency. The physical optics backscatter theory is reexamined relative to such autocorrelation functions. Experimental results obtained from a wavetank are compared to the autocorrelation model used in the analysis. The analysis indicates that Gaussian correlation length or mean-square slope is not an appropriate parameter for narrowband conditions and that significant slope is a more relevant parameter. Inherent in the deep-phase assumption is some form of slope dependency. The analysis given (and variants thereof) can be used to provide insight into the physical effects of separate spectral components and of spectral directionality.

Miller, Lee S.↗
DOE OSTI2023

Collaborative Proposal: Improving Understanding of the Internal Structure and Dynamics of Deep Convection Using ARM Observations and Large Eddy Simulations (Final progress report)

This is the final technical report for the DOE Atmospheric System Research funded project entitled "Collaborative Proposal: Improving Understanding of the Internal Structure and Dynamics of Deep Convection Using ARM Observations and Large Eddy Simulations". This project addressed several science questions related to the structure and behavior of deep convective clouds in the atmosphere. It applied knowledge gained from this work to improve the deep cumulus convection parameterization in the Community Earth System Model (CESM). Specific outcomes related to the project include an improved understanding of: (1) how convective cloud environments regulate the size of thermals reaching the upper troposphere, and hence the shallow-to-deep convective transition; (2) the role of dynamic pressure forcing in thermal and convective cloud evolution; and (3) the role of vertical wind shear on the behavior of moist thermals and the transition from shallow to deep convection. Although the primary motivation of this project was to improve basic understanding of thermal dynamics in atmospheric convection, an important component of this work was using this improved understanding to modify the representation of entrainment and vertical velocity profiles in the Zhang-McFarlane cumulus parameterization used in CESM. This work also had a strong observational component, with analyses of Atmospheric Radiation Measurement (ARM) observations to characterize thermal characteristics and behavior. Observations collected during the ARM-supported CACTI field project were used with high-resolution simulations to explore environmental controls on deep convection initiation. Data from the ARM-supported MC3E field project were also used to evaluate the representation of updraft vertical velocities in the Zhang-McFarlane cumulus parameterization. This project directly supported the training of two post-doctoral researchers and led to 16 peer-reviewed published papers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improving the Physical Basis for Updraft Dynamics in Deep Convection Parameterizations

This article presents a new deep convective parameterization that determines cloud characteristics based on a specified cloud size distribution. The vertical profiles of cloud properties are determined by analytic equations, which formulate entrainment with an inverse relationship to cloud width. In line with recent studies of large eddy simulations (LES), cloud widths are assumed to be constant with height and vertical mass flux (M) characteristics of the clouds are therefore regulated by the vertical velocity profile. The parameterization is configured to work with existing cloud base M closure formulations, with the closure predicting the total cloud area rather than the cloud base M directly. Analytic formula are also used to connect the vertical wind shear magnitude to the cloud size distribution, wherein larger shear magnitudes result in more numerous large updrafts than weaker shear magnitudes, which is in line with recent research results. The parameterization is compared against 10 deep convective LES with varying thermodynamic and vertical wind shear profiles. Results show dramatic improvements in the prediction of normalized M, detrainment, and the properties of detrained air over the existing Zhang and McFarlane (1995) scheme. In particular, the new model is able to correctly portray the transition from a bottom-heavy M profile in weakly sheared environments, to a top-heavy M profile in strongly sheared environments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

8.4 M Deep Space Habitat– Medical Bay Concept Design and Human Factors Engineering Analysis

Maintaining crew health throughout long duration deep space missions presents significant new design challenges. Since communication with Earth will be limited and it is not possible to return in the event of a medical emergency it is critical that medical care on a deep space mission be as autonomous as possible. The goal of this project was to design and assess a medical system concept for the Deep Space Habitat Concept Demonstrator derived from the core stage of the Space Launch System. The Medical Bay was designed as a primary location to support the medical care of4 crew members on a1000-daymission. It includes workspace, stowage, and direct access to all necessary medical equipment and supplies. A Human Factors Engineering Analysis was conducted to test ergonomic effectiveness and system requirement compliance. This initial design will continue to be used by the Human Factors Engineering Team and the Advanced Concepts Office at NASA’s Marshall Spaceflight Center for further demonstration, analysis, and design conception for deep space travel.

Mary Claire Mancl↗
NASA NTRSDate not supplied

NASA Centennial Challenges Deep Space Food Challenge Competition to Incentivize Innovation in Food Systems for Long-Duration Space Exploration Missions

The Centennial Challenges (CC) program, currently part of NASA’s Space Technology Mission Directorate (STMD), is one of the vehicles NASA uses to develop and execute public prize competitions. Since opening its first challenge in 2005, the CC program has initiated more than 20 challenges in a variety of technology areas. This paper provides the background, development and execution of the Deep Space Food Challenge as one approach to fulfilling NASA’s Space Policy Directive 1 (“To the Moon, then Mars”). Specifics about the CC program’s accomplishments will also be discussed. The Deep Space Food Challenge (DSF) was developed in collaboration with the Canadian Space Agency (CSA) to create novel food production technologies with minimal inputs, and maximum safe, nutritious, palatable food outputs for long-duration space missions, which have potential to benefit people on Earth. When humans return to the lunar surface in the mid-2020s, the early missions will use prepackaged foods similar to those in use on the International Space Station (ISS) today. However, extending the duration of lunar missions requires reducing resupply dependency on Earth. Testing a sustainable system on the Moon that meets lunar crews’ needs is a fundamental step for lunar sustainability and future Mars exploration. NASA and CSA are focused on how to furnish crew members with a viable food system for long duration space missions that provides all daily nutritional needs through a variety of palatable, safe food with limited resource requirements and no dependency on resupply from Earth; and enables acceptable, safe and quick preparation methods. On Earth, technology solutions for food systems could also be used to produce nutritional sources for urban and rural environments; potentially leading to a reduced impact on our Earth’s resources. Challenges, such as the DSF, are an embodiment of NASA’s continuing commitment to technological advancement and innovation through non-traditional programs.

Centennial Challenges↗
DOE OSTI2021

No Galaxy Left Behind: Measuring the Transfer Function of the Dark Energy Survey with Balrog

In this dissertation, we describe a calibration and diagnostic framework called Balrog which was used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) dataset. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with traditional generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant "source" galaxies and magnification biases of nearer "lens" galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the fiducial Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ~1-8 millimagnitudes, but for a small subset of objects we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. Finally, we discuss approaches to extend the current methodology to capture more aspects of the transfer function for future analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

High-Fidelity Simulation Aerodynamics Dataset of NACA 0012 and 0021 Airfoils

The repository contains time series data of pressure, viscous and moment forces for the NACA 0012 and 0021 airfoils at angles of attack (AOA), 5, 17, 30, 45, 60 and 90 degrees. The data was generated during the high-fidelity simulations of the airfoils using the open-source CFD code, Nalu-Wind (https://github.com/Exawind/nalu-wind). The research objective was to study the effects of mesh resolution and turbulence model on the three-dimensional deep stall aerodynamics of the airfoils, which has been published in the Journal of Turbulence (https://doi.org/10.1080/14685248.2023.2225141). Several meshes of the O-grid type were generated using the modified Pointwise Glyph script of Carrigan (https://github.com/pointwise/AirfoilMesh) by varying the wall normal and spanwise resolutions. Note that, the geometry was extruded by four chord lengths in the spanwise direction. In the paper, we showed that Improved Delayed Detached Eddy Simulation (IDDES) hybrid RANS/LES turbulence model with a minimum of 24 cells per chord length in the spanwise direction is necessary to correctly predict the loads in the deep stall regime. Under the NACA 0012 airfoil directory, the data for several combinations of wall-normal and spanwise resolutions are presented in each of the AOA subdirectories. The corresponding experimental data for the lift and drag polars can be found in the "exp_data" subdirectory. The directory for the NACA 0021 airfoil consists of CFD force data and airfoil polars for Reynolds numbers 2.7e5 and 2.0e6.

17 WIND ENERGY↗
NASA NTRS2005

The Visible Nulling Coronagraph--Architecture Definition and Technology Development

This paper describes the advantages of visible direct detection and spectroscopy of Earth-like extrasolar planets using a nulling coronagraph instrument behind a moderately sized single aperture space telescope. Our concept synthesizes a nulling interferometer by shearing the telescope pupil, with the resultant producing a deep null. We describe nulling configurations that also include methods to mitigate stellar leakage, such as spatial filtering by a coherent array of single mode fibers, and post-starlight suppression wavefront sensing and control. With diffraction limited telescope optics and similar quality components in the optical train (lambda/20), suppression of the starlight to 1e-10 is readily achievable. We describe key features of the architecture and analysis, present latest results of laboratory measurements demonstrating achievable null depth and component development, and discuss future key technical milestones.

nulling interferometer↗
DOE OSTI2022

Dark Energy Survey Year 3 Results: Measuring the Survey Transfer Function with Balrog

Abstract We describe an updated calibration and diagnostic framework, Balrog , used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) data set. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant “source” galaxies and magnification biases of nearer “lens” galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ∼1–8 mmag, but for a small subset of objects, we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. We discuss approaches to extend the current methodology to capture more aspects of the transfer function and reach full coverage of the survey footprint for future analyses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

Architecture for Cognitive Networking within NASAs Future Space Communications Infrastructure

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

space networks↗
NASA NTRS2016

Architecture for Cognitive Networking within NASA's Future Space Communications Infrastructure

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, GEO, MEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes an architecture enabling the development and deployment of cognitive networking capabilities into the envisioned future NASA space communications infrastructure. We begin by discussing the need for increased automation, including inter-system discovery and collaboration. This discussion frames the requirements for an architecture supporting cognitive networking for future missions and relays, including both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture, and results of implementation and initial testing of a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

space networks↗