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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Location and Geologic Setting for the Three U.S. Mars Landers

Super resolution of the horizon at both Viking landing sites has revealed "new" features we use for triangulation, similar to the approach used during the Mars Pathfinder Mission. We propose alternative landing site locations for both landers for which we believe the confidence is very high. Super resolution of VL-1 images also reveals some of the drift material at the site to consist of gravel-size deposits. Since our proposed location for VL-2 is NOT on the Mie ejecta blanket, the blocky surface around the lander may represent the meter-scale texture of "smooth palins" in the region. The Viking Lander panchromatic images typically offer more repeat coverage than does the IMP on Mars Pathfinder, due to the longer duration of these landed missions. Sub-pixel offsets, necessary for super resolution to work, appear to be attributable to thermal effects on the lander and settling of the lander over time. Due to the greater repeat coverage (particularly in the near and mid-fields) and all-panchromatic images, the gain in resolution by super resolution processing is better for Viking than it is with most IMP image sequences. This enhances the study of textural details near the lander and enables the identification rock and surface textures at greater distances from the lander. Discernment of stereo in super resolution im-ages is possible to great distances from the lander, but is limited by the non-rotating baseline between the two cameras and the shorter height of the cameras above the ground compared to IMP. With super resolution, details of horizon features, such as blockiness and crater rim shapes, may be better correlated with Orbiter images. A number of horizon features - craters and ridges - were identified at VL-1 during the misison, and a few hils and subtle ridges were identified at VL-2. We have added a few "new" horizon features for triangulation at the VL-2 landing site in Utopia Planitia. These features were used for independent triangulation with features visible in Viking Orbiter and MGS MOC images, though the actual location of VL-1 lies in a data dropout in the MOC image of the area. Additional information is contained in the original extended abstract.

Parker, T. J.↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2025

Electrochemically modulated single-molecule localization microscopy for in vitro imaging cytoskeletal protein structures

A new concept of electrochemically modulated single-molecule localization super-resolution imaging is developed. Applications of single-molecule localization super-resolution microscopy have been limited due to insufficient availability of qualified fluorophores with favorable low duty cycles. The key for the new concept is that the “On” state of a redox-active fluorophore with unfavorable high duty cycle could be driven to “Off” state by electrochemical potential modulation and thus become available for single-molecule localization imaging. The new concept was carried out using redox-active cresyl violet with unfavorable high duty cycle as a model fluorophore by synchronizing electrochemical potential scanning with a single-molecule localization microscope. The two cytoskeletal protein structures, the microtubules from porcine brain and the actins from rabbit muscle, were selected as the model target structures for the conceptual imaging in vitro. The super-resolution images of microtubules and actins were obtained from precise single-molecule localizations determined by modulating the On/Off states of single fluorophore molecules on the cytoskeletal proteins via electrochemical potential scanning. Importantly, this method could allow more fluorophores even with unfavorable photophysical properties to become available for a wider and more extensive application of single-molecule localization microscopy.

electrochemical modulation↗
DOE OSTI2025

Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degree and 1 degree, which serve as high, intermediate and low-resolution inputs respectively. For each of the SRCNN and SRGAN network, we use a two-step resolution enhancement, the first step generates 4x refinement from 1 degree to 0.25 degree and the second step generates 6x refinement from 0.25 degree to 1/24 degree (~4km). We downscale daily scale precipitation, maximum temperature and minimum temperature for the six CMIP6 GCMs for 1980 to 2019 in the historical period and 2020 to 2059 in the near-term future under the shared socioeconomic pathway 585 and 245 (SSP585 and SSP245) emission scenarios. We also perform double bias-correction with Daymet observations using a quantile mapping approach, first for GCMs prior to making predictions at 1 degree grid and second after making final predictions at ~4km.

13 HYDRO ENERGY↗
NASA NTRS2005

Reaction Control System Thruster Cracking Consultation: NASA Engineering and Safety Center (NESC) Materials Super Problem Resolution Team (SPRT) Findings

The shuttle orbiter s reaction control system (RCS) primary thruster serial number 120 was found to contain cracks in the counter bores and relief radius after a chamber repair and rejuvenation was performed in April 2004. Relief radius cracking had been observed in the 1970s and 1980s in seven thrusters prior to flight; however, counter bore cracking had never been seen previously in RCS thrusters. Members of the Materials Super Problem Resolution Team (SPRT) of the NASA Engineering and Safety Center (NESC) conducted a detailed review of the relevant literature and of the documentation from the previous RCS thruster failure analyses. It was concluded that the previous failure analyses lacked sufficient documentation to support the conclusions that stress corrosion cracking or hot-salt cracking was the root cause of the thruster cracking and lacked reliable inspection controls to prevent cracked thrusters from entering the fleet. The NESC team identified and performed new materials characterization and mechanical tests. It was determined that the thruster intergranular cracking was due to hydrogen embrittlement and that the cracking was produced during manufacturing as a result of processing the thrusters with fluoride-containing acids. Testing and characterization demonstrated that appreciable environmental crack propagation does not occur after manufacturing.

MacKay, Rebecca A.↗
NASA NTRS2022

Performance of Flash Lidar with Real-time Image Enhancement Algorithm for Landing Hazard Avoidance

Performance of a 3-D imaging flash lidar employing a novel super-resolution algorithm is characterized by a series of static and dynamic tests. A gantry test of this lidar in which the lidar was placed in an instrumented moving basket above a calibrated hazard field proved an excellent demonstration of hazard avoidance capabilities. Results of the gantry test are reported and potential of flash lidar utilizing super-resolution algorithm for future landing missions are explained.

Hazard Detection↗
NASA NTRS2001

An Approach to Application of Multispectral Sensors, using AVIRIS Data

High spatial resolution multispectral/hyperspectral sensors are being developed by private industry with science/research customers as end users. With an increasingly wide range of sensor choices, it is important for the remote sensing science community and commercial community alike to understand the trade-offs between ground sample distance (GSD), spectral resolution, and signal-to-noise ratio (SNR) in selecting a sensor that will best meet their needs. High spatial resolution hyperspectral imagery and super resolution multispectral charge-coupled device imagery can be used to develop prototypes of proposed data acquisition systems without building new systems or collecting large sets of additional data. By using these datasets to emulate proposed and existing systems, imaging systems may be optimized to meet customer needs in a virtual environment. This approach also enables one to determine, a priori, whether an existing dataset will be useful for a given application.

Warner, Amanda↗
NASA NTRS2016

High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

0↗
NASA NTRS2018

Improving Imaging Instrument Spatial Resolution Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.

Image Processing↗
NASA NTRS2018

Improving the Spatial Resolution of Imaging Instruments Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.

Image Processing↗
DOE OSTI2025

Removal of Homogeneous Broadening from 1 H-Detected Multidimensional Solid-State NMR Spectra

1 H-detected magic-angle spinning (MAS) NMR experiments have revolutionized the NMR studies of biological and inorganic solids by providing unparalleled sensitivity and resolution. Despite these gains, homogeneous broadening, originating from the incomplete removal of homonuclear dipolar interactions under fast MAS, remains highly prevalent and limits the achievable resolution. In direct analogy to super-resolution microscopy methods, we show that resolution beyond that currently achievable by fast MAS alone can be obtained by experiment-driven deconvolution. Following the acquisition of a single 2D NMR spectrum to measure the frequency-dependent homogeneous lineshapes, any number of 1 H-detected spectra can be enhanced in resolution, yielding comparable spectra as obtained with twice the MAS frequency. In conclusion, the versatility of this approach is demonstrated in the enhancement of single- and double-quantum homonuclear correlation spectra, in addition to heteronuclear correlation spectra acquired on a surface organometallic complex and the protein GB1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.

3D temporal sequences↗
DOE OSTI2025

Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting

Neural Operators are fast and accurate surrogates for nonlinear mappings between functional spaces within training domains. Extrapolation beyond the training domain remains a grand challenge across all application areas. We present Time-Conditioned UNet (TC-UNet) as an operator learning method to solve time-dependent PDEs continuously in time without any temporal discretization, including in extrapolation scenarios. TC-UNet incorporates the temporal evolution of the PDE into its architecture by combining a parameter conditioning approach with the attention mechanism from the Transformer architecture. After training, TC-UNet makes real-time inferences on an arbitrary temporal grid. We demonstrate its extrapolation capability on a climate problem by estimating the global temperature for several years and also for inviscid hypersonic flow around a double cone. We propose different training strategies involving temporal bundling and sub-sampling. We demonstrate performance improvements for several benchmarks, performing extrapolation for long time intervals and zero-shot super-resolution time.

Deep learning↗
DOE OSTI2025

Characterizing Absolute Orientations in DNA Self-Assembly of Single Molecules

DNA self-assembly of single molecules (i.e., dyes) with deterministic orientations is a powerful approach for engineering mo-lecular excitons. However, current determination methods of the dye orientation relative to DNA do not account for the orien-tation of the dye plane, which is a missing degree of freedom needed to define absolute three-dimensional orientations. In this work, we combine fluorescence-detected linear dichroism, defocused dipole imaging, and DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) super-resolution microscopy to determine the absolute three-dimensional orientations of single Cy5 dyes relative to host DNA duplexes which includes the dye plane orientation. The data revealed that the absorption and emission dipoles are perpendicular to the DNA duplex, and the mean dye plane is parallel to the DNA bases, which supports the notion that Cy5 dyes intercalate between DNA base pairs. The presented methodology will inspire the investigation of the dye plane orientation for controlling dye arrangement configurations beyond spontaneous π-stacking between dyes as well as achieving novel dye-DNA arrangements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DNA-PAINT Imaging with Hydrogel Imprinting and Clearing

Hydrogel-embedding is a versatile technique in fluorescence microscopy, offering stabilization, optical clearing, and the physical expansion of biological specimens. DNA-PAINT is a super-resolution microscopy approach based on the diffusion and transient binding of fluorescently labeled oligos, but its feasibility in hydrogels has not yet been explored. In this study, we demonstrate that polyacrylamide hydrogels support sufficient diffusion for effective DNA-PAINT imaging. Using acrydite-anchored oligonucleotides imprinted from patterned DNA origami nanostructures and microtubule filaments in fixed cells, we find that hydrogel embedding preserves docking strand positioning at the nanoscale. Sample clearing via protease treatment had minor structural effects on the microtubule structure and enhanced diffusion and accessibility to hydrogel-imprinted docking strands. Our work demonstrates promising potential for diffusion and binding-based fluorescence imaging applications in hydrogel-embedded samples.

DNA origami↗
NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗