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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
NASA NTRS1988

High-resolution (30 m) lunar radar images

New high-resolution radar images of the lunar surface are being produced using the Haystack Observatory range-Doppler radar system. The goals are to: investigate the decameter-scale properties of the lunar surface, as an aid in the understanding of the geophysical history of the moon; and to improve the understanding of the mechanisms of planetary radar backscattering, to aid in the interpretation of the coarser-resolution images which were and will be obtained from planetary probe missions and other earth-based observations.

Zisk, Stanley H.↗
NASA NTRS1995

An Analysis of Neptune's Stratospheric Haze Using High-Phase-Angle Voyager Images

We have inverted high-phase-angle Voyager images of Neptune to determine the atmospheric extinction coefficient as a function of altitude and the scattering phase function at a reference altitude. Comparisons between theoretical model and observations help separate the contributions from molecular Rayleigh and aerosol scattering and help determine the variation of the aerosol size, concentration, and scattering properties with altitude. Further comparisons between models and data allow us to place constraints on the location and composition of the hazes, the concentration and downward flux of certain condensible hydrocarbon gases, the eddy diffusion coefficient in the lower stratosphere, and the thermal profile in parts of Neptune's stratosphere. We find that a distinct stratospheric haze layer exists near 12(sub -1, sup +1) mbar in Neptune's lower stratosphere, most probably due to condensed ethane. The derived stratospheric haze production rate of 1.0(sub -0.3, sup +0.2) x 10(exp -15) g cm(exp -2) sec(exp -1) is substantially lower than photochemical model predictions. Evidence for hazes at higher altitudes also exists. Unlike the situation on Uranus, large particles (0.08-0.11 microns) may be present at high altitudes on Neptune (e.g., near 0.5 mbar), well above the region in which we expect the major hydrocarbon species to condense. Near 28 mbar, the mean particle size is about 0.13(sub -0.02, sup +0.02) microns with a concentration of 5(sub -3, sup +3) particles cm(exp -3). The cumulative haze extinction optical depth above 15 mbar in the clear filter is approx. 3 x 10(exp -3), and much of this extinction is due to scattering rather than absorption; thus, if our limb-scan sites are typical, the hazes cannot account for the stratospheric temperature inversion on Neptune and may not contribute significantly to atmospheric heating. We compare the imaging results with the results from other observations, including those of the Voyager Photopolarimeter Subsystem, and discuss differences between Neptune and Uranus.

Moses, Julianne I.↗
NASA NTRS2010

High-Speed PLIF Imaging of Hypersonic Transition over Discrete Cylindrical Roughness

In two separate test entries, advanced laser-based instrumentation has been developed and applied to visualize the hypersonic flow over cylindrical protrusions on a flat plate. Upstream of these trips, trace quantities of nitric oxide (NO) were seeded into the boundary layer. The protuberances were sized to force laminar-to-turbulent boundary layer transition. In the first test, a 10-Hz nitric oxide planar laser-induced fluorescence (NO PLIF) flow visualization system was used to provide wide-field-of-view, high-resolution images of the flowfield. The images had sub-microsecond time resolution. However these images, obtained with a time separation of 0.1 sec, were uncorrelated with each other. Fluorescent oil-flow visualizations were also obtained during this test. In the second experiment, a laser and camera system capable of acquiring NO PLIF measurements at 1 million frames per second (1 MHz) was used. This system had lower spatial resolution, and a smaller field of view, but the images were time correlated so that the development of the flow structures could be observed in time.

Danehy, P. M.↗
DOE OSTI2020

Method for conducting in situ high-temperature digital image correlation with simultaneous synchrotron measurements under thermomechanical conditions

This work presents a novel method of obtaining in situ strain measurements at high temperature by simultaneous digital image correlation (DIC), which provides the total strain on the specimen surface, and synchrotron x-ray diffraction (XRD), which provides lattice strains of crystalline materials. DIC at high temperature requires specialized techniques to overcome the effects of increased blackbody radiation that would otherwise overexpose the images. The technique presented herein is unique in that it can be used with a sample enclosed in an infrared heater, remotely and simultaneously with synchrotron XRD measurements. The heater included a window for camera access, and the light of the heater lamps is used as illumination. High-temperature paint is used to apply a random speckle pattern to the sample to allow the tracking of displacements and the calculation of the DIC strains. An inexpensive blue theatrical gel filter is used to block interfering visible and infrared light at high temperatures. This technique successfully produces properly exposed images at 870 °C and is expected to perform similarly at higher temperatures. The average strains measured by DIC were validated by an analytical calculation of the theoretical strain. Simultaneous DIC and XRD strain measurements of Inconel 718 (IN718) tensile test specimens were performed under thermal and mechanical loads and evaluated. Finally, this approach uses the fact that with DIC, the total strain is measured, including plastic strain, while with XRD, only elastic strain is captured. The observed differences were discussed with respect to the effective deformation mechanisms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
NASA NTRS1986

Solid state imagers and their applications; Proceedings of the Meeting, Cannes, France, November 26, 27, 1985

Topics treated include the use of semiconductor imagers in high energy particle physics, an X-ray image sensor based on an optical TDI-CCD imager, and an electron-sensitive CCD readout array for a circular-scan streak tube. Papers are presented on the pan-imager, high resolution linear arrays, the reduction of reflection losses in solid-state image sensors, a high resolution CCD imager module with swing operation, large area CCD image sensors for scientific applications, and new readout techniques for frame transfer CCDs. Consideration is given to advanced optoelectronical sensors for autonomous rendezvous/docking and proximity operations in space, the testing and characterization of CCDs for the Rosat star sensors, an advanced radial camera for the Hubble Space Telescope, and scanning or staring infrared imagers.

Declerck, Gilbert J.↗
DOE OSTI2023

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Enhanced quantum efficiency of high-purity silicon imaging detectors by ultralow temperature surface modification using Sb doping

A low temperature process for Sb doping of silicon has been developed as a backsurface treatment for high-purity n-type imaging detectors. Molecular beam epitaxy (MBE) is used to achieve very high dopant incorporation in a thin, surface-confined layer. The growth temperature is kept below 450 °C for compatibility with Al-metallized devices. Imaging with MBE-modified 1kx1k charge coupled devices ((CCDs) operated in full depletion has been demonstrated. Dark current is comparable to the state-of-the-art process, which requires a high temperature step. Quantum efficiency is improved, especially in the UV, for thin doped layers placed closer to the backsurface. Near 100% internal quantum efficiency has been demonstrated in the ultraviolet for a CCD with a 1.5 nm silicon cap layer.

Nikzad, Shouleh↗
NASA NTRS2005

Enhanced quantum efficiency of high-purity silicon imaging detectors by ultralow temperature surface modification using Sb doping

A low temperature process for Sb doping of silicon has been developed as a backsurface treatment for high-purity n-type imaging detectors. Molecular beam epitaxy (MBE) is used to achieve very high dopant incorporation in a thin, surface-confined layer. The growth temperature is kept below 450 (deg)C for compatibility with Al-metallized devices. Imaging with MBE-modified 1kx1k charge coupled devices (CCDs) operated in full depletion has been demonstrated. Dark current is comparable to the state-of-the-art process, which requires a high temperature step. Quantum efficiency is improved, especially in the UV, for thin doped layers placed closer to the backsurface. Near 100% internal quantum efficiency has been demonstrated in the ultraviolet for a CCD with a 1.5 nm silicon cap layer.

charged coupled devices (CCDs)↗
NASA NTRS1993

Observations of the quasi 2-day wave from the High Resolution Doppler Imager on UARS

A strong westward traveling oscillation, with a period of 2 days and zonal wave number 3, is observed in the mesospheric and lower thermospheric winds from the High Resolution Doppler Imager (HRDI) on the Upper Atmosphere Research Satellite (UARS). The important events happen in January, July, and September/October, of which the occurrence in January is the strongest with an amplitude over 60 m/s. Detailed analyses for the periods of January 1992 and January 1993 reveal a cause-and-effect relationship in the wave developing process at 95 km. The global structures of the wave amplitude and phase are also presented.

Wu, D. L.↗
NASA NTRS2000

Force Limited Vibration Test of HESSI Imager

The High Energy Solar Spectroscopic Imager (HESSI) is a solar x-ray and gamma-ray observatory scheduled for launch in November 2000. Vibration testing of the HESSI imager flight unit was performed in August 1999. The HESSI imager consists of a composite metering tube, two aluminum trays mounted to the tube on titanium flexure mounts, and nine modulation grids mounted on each tray. The vibration tests were acceleration controlled and force limited, in order to prevent overtesting. The force limited strategy reduced the shaker force and notched the acceleration at resonances. The test set-up, test levels, and results are presented. The development of the force limits is also discussed. The imager successfully survived the vibration testing.

Amato, Deborah↗
DOE OSTI2022

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1996

Determination of Shed Ice Particle Size Using High Speed Digital Imaging

A full scale model of an aircraft engine inlet was tested at NASA Lewis Research Center's Icing Research Tunnel. Simulated natural ice sheds from the engine inlet lip were studied using high speed digital image acquisition and image analysis. Strategic camera placement integrated at the model design phase allowed the study of ice accretion on the inlet lip and the resulting shed ice particles at the aerodynamic interface plane at the rear of the inlet prior to engine ingestion. The resulting digital images were analyzed using commercial and proprietary software to determine the size of the ice particles that could potentially be ingested by the engine during a natural shedding event. A methodology was developed to calibrate the imaging system and insure consistent and accurate measurements of the ice particles for a wide range of icing conditions.

Broughton, Howard↗
NASA NTRS1999

AXAF-1 High Resolution Assembly Image Model and Comparison with X-Ray Ground Test Image

The x-ray ground test of the AXAF-I High Resolution Mirror Assembly was completed in 1997 at the X-ray Calibration Facility at Marshall Space Flight Center. Mirror surface measurements by HDOS, alignment results from Kodak, and predicted gravity distortion in the horizontal test configuration are being used to model the x-ray test image. The Marshall Space Flight Center (MSFC) image modeling serves as a cross check with Smithsonian Astrophysical observatory modeling. The MSFC image prediction software has evolved from the MSFC model of the x-ray test of the largest AXAF-I mirror pair in 1991. The MSFC image modeling software development is being assisted by the University of Alabama in Huntsville. The modeling process, modeling software, and image prediction will be discussed. The image prediction will be compared with the x-ray test results.

Zissa, David E.↗
DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗