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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
DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗
DOE OSTI2026

Global Multimodal Dataset for Nighttime Light Super-Resolution

The dataset is a collection of spatially and temporally registered high-resolution and low-resolution nighttime light (NTL) images, high-resolution land-use binary masks, and high-resolution road density images from around the world. The NTL images are sourced from the NASA Black Marble product VNP46A2 and the LuoJia1-01 satellite. The land-use binary masks are derived from Google's and the World Resources Institute's DynamicWorld dataset, and the road density images are sourced from OpenStreetMap.

Nighttime lights↗
NASA NTRSDate not supplied

Super-Resolution from Space: Using MERRA-2 and MAIAC Satellite Imagery to Produce Daily Continuous 1 km PM2.5 Estimates

PM2.5 measurements from ground stations are the gold standard when available, but the expense and coverage of such stations limits widespread monitoring. Having accurate PM2.5 estimates outside the range of these stations is important for monitoring this crucial aspect of air quality. The goal of this project is to produce daily 1 km continuous PM2.5 estimates for the contiguous US relying primarily on satellite-derived data sources. This is important because models based on such data can be more easily expanded outside the study area and produce global estimates as well. The temporal availability of such data products is often weekly/daily, unlike land-use products with are often available at a yearly or worse temporal resolution. To achieve our goal, we use a couple of different deep neural network architectures to produce PM2.5 measurements at 10 km and 1 km resolution. We use two model architectures, a UNET-like model and a GAN-based model. We train both models using MERRA-2 data and MAIAC AOD data scaled to 10 km and 1 km for the two different prediction resolutions. MERRA-2 imagery is data rich with a wide range of geospatial variables at 50 km and has long historical availability (beginning in 1980). We’re also using higher spatial resolution MAIAC data at 1 km to provide finer resolution spatial context. This essentially leverages the spatial resolution of MAIAC data and the “wider” information of MERRA-2 data to predict PM2.5. For the target data we’re using a modeled 1 km PM2.5 dataset produced by Harvard to pre-train our models and then fine-tune our models using ground station measurements. Not only are our results comparable with the performance of the Harvard dataset, but can be generalized to any area or time where MERRA-2 and MAIAC data is available.

satellite imagery↗
DOE OSTI2025

Cryosectioning-enhanced super-resolution microscopy for single-protein imaging across cells and tissues

DNA-points accumulation for imaging in nanoscale topography (DNA-PAINT) enables nanoscale imaging with virtually unlimited multiplexing and molecular counting. Here, we address challenges, such as variable imaging performance and target accessibility, that can limit its broader applicability. Specifically, we enhance its capacity for robust single-protein imaging and molecular counting by optimizing the integration of total internal reflection fluorescence microscopy with physical sectioning, in particular, Tokuyasu cryosectioning. Our method, tomographic and kinetically enhanced DNA-PAINT (tkPAINT), achieves 3 nm localization precision across diverse samples, enhanced imager binding, and improved cellular integrity. tkPAINT can facilitate molecular counting with DNA-PAINT inside the nucleus, as demonstrated through its quantification of the in situ abundance of RNA Polymerase II in both HeLa cells as well as mouse tissues. Anticipating that tkPAINT could become a versatile tool for the exploration of biomolecular organization and interactions across cells and tissues, we also demonstrate its capacity to support multiplexing, multimodal targeting of proteins and nucleic acids, and three-dimensional (3D) imaging.

Science & Technology - Other Topics↗
DOE OSTI2024

LITE-SM: A Light Sheet Illuminator Compatible with Super Resolution and Single Molecule Imaging

The LITE-SM (hereinafter referred to as the Tilt-SM) is a light sheet illumination system purpose-built for single molecule (SM) imaging. SM imaging is a fluorescence imaging technique where individual molecules can be tracked in living cells/tissues in real time allowing for unprecedented insights into the intracellular dynamics of these molecules. The innovative all-mirror optical design greatly reduces/eliminates the aberrations that plague lens-based designs. This iteration of the Tilt-SM has significantly better optical performance then its predecessor with over 10x increase in optical power at the sample, allowing for the use of much smaller and more cost-effective lasers then the original Phase 1 system. Excellent STORM imaging as well as SM tracking has been achieved, satisfying the key objectives of this study.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

An Evanescent Microwave Probe for Super-Resolution Nondestructive Imaging of Metals, Semiconductors, Dielectrics, Composites and Biological Specimens

Using evanescent microwaves with decay lengths determined by a combination of microwave wavelength (lambda) and waveguide termination geometry, we have imaged and mapped material non-uniformities and defects with a resolving capability of lambda/3800=79 microns at 1 GHz. In our method a microstrip quarter wavelength resonator was used to generate evanescent microwaves. We imaged materials with a wide range of conductivities. Carbon composites, dielectrics (Duroid, polymers), semiconductors (3C-SiC, polysilicon, natural diamond), metals (tungsten alloys, copper, zinc, steel), high-temperature superconductors, and botanical samples were scanned for defects, residual stresses, integrity of brazed junctions, subsurface features, areas of different film thickness and moisture content. The evanescent microwave probe is a versatile tool and it can be used to perform very fast, large scale mapping of a wide range of materials. This method of characterization compares favorably with ultrasound testing, which has a resolution of about 0.1 mm and suffers from high absorption in composite materials and poor transmission across boundaries. Eddy current methods which can have a resolution on the order of 50 microns are restricted to evaluating conducting materials. Evanescent microwave imaging, with careful choice of operating frequency and probe geometry, can have a resolution of up to 1 micron. In this method we can scan hot and moving objects, sample preparation is not required, testing is non-destructive, non-invasive and non-contact, and can be done in air, in liquid or in vacuum.

Pathak, P. S.↗
NASA NTRS2000

Evanescent Microwave Probes Using Coplanar Waveguide and Stripline for Super-Resolution Imaging of Materials

An evanescent field microwave imaging probe based on half-wavelength, microwave transmission line resonators is described. Optimization of the probe tip design, the coupling gap, and the data analysis has resulted in images of metal lines on semiconductor substrates with 2.6 microns spatial resolution and a minimum detectable line width of 0.4 microns at 1 GHz.

Ponchak, G. E.↗
NASA NTRS1979

Super-resolution imaging system

The resolution of an imaging system is greatly enhanced by radiating an object with a plane wave field from a coherent source variable in either frequency, angle or distance from the object, detecting the wave field transmitted through, or reflected from, the object at some point on the image of the object, with or without heterodyne detection, and with or without a lens system. The heterodyne detected output of the detector is processed to obtain the Fourier transform as a function of the variable for a direct measurement of the amplitude and surface height structure of the object within a resolution cell centered at the corresponding point on the object. In the case of no heterodyne detection, only intensity data is obtained for a Fourier spectrum.

Jain, Atul↗
NASA NTRSDate not supplied

Milli-Arcsecond (MAS) Imaging with SmallSat Enabled Super-resolution

The objective of this project is to demonstrate a new imaging technology with unprecedented capabilities for scientific exploration. This technology innovation will enable extremely high-resolution imaging capability, otherwise unattainable with conventional approaches. It is based on a unique combination of novel diffractive optical sensing (photon sieve), advanced image processing, and small satellite formation flying. The spatial resolution of current solar instruments are more than 100 times coarser than the size of energy dissipation features in coronal heating (<100 km) since conventional optics cannot be figured to near diffraction-limited accuracy at extreme ultraviolet (EUV) wavelengths. This project will demonstrate the feasibility of imaging of a few milli-arcseconds (MAS) to study the structure and development of these dissipation regions.

Farzad Kamalabadi↗