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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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NASA NTRS2006

Lightweight optical mirrors formed in single crystal substrate

This invention is directed to a process for manufacturing a lightweight mirror from a single crystal material, such as single crystal silicon. As a near perfect single crystal material, single crystal silicon has much lower internal stress than a conventional material. This means much less distortion of the optical surface during the light weighting process. After being ground and polished, a single crystal silicon mirror is light weighted by removing material from the back side using ultrasonic machining. After the light weighting process, the single crystal silicon mirror may be used as-is or further figured by conventional polishing or ion milling, depending on the application and the operating wavelength.

Bly, Vincent T.↗

NASA NTRS1972

The data-handling problem with television recording of spectra.

A television tube is a useful way to record a many-element picture such as is obtained from an echelle spectrograph. The signal on the tube target is scanned and relayed to the data-processing center where it must be reconstituted into the scene that was present in the focal plane of the spectrograph camera. A typical television frame will contain four million bits of information which must be processed to remove transmission errors and random noise pulses, to remove periodic noise, to correct for optical and electrical distortion, to remove sensitivity variations across the face of the tube, to calibrate from the photoelectrons back to incident energy, and to provide a wavelength scale. Results of a study of these problems is presented.

Underhill, A. B.↗

NASA NTRS1985

Acousto-optic Extensometer for Bolts

Bolt torque or tension deduced from non contacting measurements. Pulsed Laser induces shockwave in bolthead while continuous-wave acousto-optic detector monitors distortion produced by shock. Shock travel time measured to determine bolt length. Solid-state position detector relatively inexpensive and keeps down cost of extensometer.

Maram, J.↗

NASA NTRS1984

Thermal contacts between metal and glass for use at cryogenic temperatures

Thermal contacts designed to cool a 50-cm diam fused-silica mirror to 5 K without serious distortion of the optical surfaces were tested. Rubber cement and copper-filled rubber cement were found to be well suited for maintaining thermal contact between the fused-silica and copper straps. A variety of other adhesives proved to be unreliable because they spalled the glass. The thermal resistances of the rubber cement and copper-filled rubber cement were determined. For the copper-filled rubber cement, the thermal resistances varied from 570 K/W at 60 K to 15,000 K/W at 10 K.

Obrien, K.↗

NASA NTRS1991

Astrometry using the Hubble Space Telescope fine guidance sensors

Plans for the use of the three fine guidance sensors (FGSs) of the NASA Hubble Space Telescope for direct astrometric observations are reviewed. Topics addressed include the optical elements and detectors of the FGSs, the basic and astrometric operating modes of the FGSs, spacecraft pointing control during astrometric observations, and the astrometric data-reduction software provided to HST users. Particular attention is given to the calibration procedures for optical-field angle distortion, the plate scale, filter-wedge errors, lateral color effects, secular changes, velocity aberration, and transfer function. Also discussed are the steps being taken to verify the HST orbit. It is pointed out that the performance of the interferometric FGSs should not be affected by the focusing problems of the main HST instrument.

Duncombe, Raynor L.↗

NASA NTRS1992

Surface finish quality of the outer AXAF mirror pair based on x ray measurements of the VETA-I

We employ the X-ray measurements of the VETA-I taken at the X-Ray Calibration Facility (XRCF) of the Marshall Space Flight Center (MSFC) to extract information about the surface finish quality of the outermost pair of AXAF mirrors. The particular measurements we consider are one dimensional scans of the core of the point response function (PRF) (full width half maximum (FWHM) scans), the encircled energy as a function of radius, and one dimensional scans of the wings of the PRF. We discuss briefly our ray trace model which incorporates the numerous effects present in the VETA-I test, such as the finite source distance, the size and shape of the X-ray source, the residual gravitational distortions of the optic, the despace of the VETA-I, and particulate contamination. We show how the data constrain the amplitude of mirror surface deviations for spatial frequencies greater than about 0.1 mm(exp -1). Constraints on the average amplitude of circumferential slope errors are derived as well.

Hughes, John P.↗

NASA NTRS1993

Surface finish quality of the outer AXAF mirror pair based on X-ray measurements of the VETA-I

We employ the X-ray measurements of the VETA-I taken at the X-Ray Calibration Facility (XRCF) of the Marshall Space Flight Center (MSFC) to extract information about the surface finish quality of the outermost pair of AXAF mirrors. The particular measurements we consider are 1D scans of the core of the point response function (PRF) (FWHM scans), the encircled energy as a function of radius, and 1D scans of the wings of the PRF. We discuss briefly our raytrace model which incorporates the numerous effects present in the VETA-I test, such as the finite source distance, the size and shape of the X-ray source, the residual gravitational distortions of the optic, the despace of the VETA-I, and particulate contamination. We show how the data constrain the amplitude of mirror surface deviations for spatial frequencies greater than about 0.1/mm. Constraints on the average amplitude of circumferential slope errors are derived as well.

Hughes, John P.↗

NASA NTRS1994

The photometric properties of the HST astrometer Fine Guidance Sensor

This paper presents the results of the photometric calibration of the F583W filter in the astrometer Fine Guidance Sensor on the Hubble Space Telescope. Dozens of observations of the 9.58 mag Fine Guidance Sensor (FGS) transfer mode reference star Upgren 69 (in the cluster NGC 188) have been utilized to verify the consistency and demonstrate the temporal stability of the photomultipliers. The measurements which provided the material for a transformation from the FGS instrumental system to the Johnson V magnitude consisted of the extensive position mode observations performed during the Optical Field Angle Distortion calibration. A total of 588 measurements of 92 stars in the galactic cluster M35 were performed. Johnson V band photometry with a precision (i.e., random errors) of plus or minus 0.05 mag is available with an accuracy (i.e., systematic errors) of 0.05 mag over 2 yr.

Bucciarelli, B.↗

NASA NTRS2003

High Resolution Imaging from the Stratosphere: Atmospheric Seeing and Tether Dynamics

A balloon-borne telescope that is capable of imaging planets orbiting nearby stars requires that the flatness and tilt of the wavefront of the light entering that telescope meet certain stringent conditions. The atmosphere through which the light propagates distorts the wavefront due to turbulence in the atmosphere and due to the disturbances caused by the balloon itself The magnitude of these effects may be estimated, but no direct measurements have been made at the level of precision necessary for designing a telescope as demanding as we envision. Therefore, under this grant we carried out a study of techniques that could be used to make an in situ measurement of the distortion of the optical wavefront.

Ford, Holland↗

NASA NTRS2017

Wide-Field Infrared Survey Telescope (WFIRST) Integrated Modeling

Contents: introduction to WFIRST (Wide-Field Infrared Survey Telescope) and integrated modeling; WFIRST stability requirement summary; instability mitigation strategies; dynamic jitter results; STOP (structural-thermal-optical performance) (thermal distortion) results; STOP and jitter capability limitations; model validation philosophy.

Integrated Modelin↗

NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗

NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗

NASA NTRS1994

Capacitive Edge Sensor Design for Selene Segmented Primary Mirror

The objective of this project is to produce a proof-of-concept demonstration of an edge sensor for the Space Laser Energy (SELENE) program. In the SELENE telescope, the primary mirror design sonsists of more than 200,000 segments which must be precisely aligned to correct for distortions in the optical train

SELENE↗

DOE OSTI2025

Discorpy : algorithms and software for camera calibration and correction

Camera or lens-based detector calibration is essential for spatial accuracy in applications like dimensional tomography, optical metrology, and computer vision. Many methods and software exist yet there is still a lack of approaches that achieve both high accuracy and robustness while being easy to use and capable of handling a wide range of distortions. Radial lens distortion is common in high-resolution X-ray detector optics used in parallel-beam tomography at synchrotrons. Achieving sub-pixel accuracy requires calibrating with an optical target image. Although methods for characterizing radial distortion are well established, acquired images often also include perspective distortion and optical center offset. Here, we present our approaches to individually characterize and correct both types of distortion using a single calibration image, implemented in the Discorpy software.

36 MATERIALS SCIENCE↗

NASA NTRS2026

Improvement in the Thermal-to-Structural Model Mapping Process for Integrated Modeling for the Roman Space Telescope

Integrated Modeling has been a key component of verifying optical requirements for the Nancy Grace Roman Space Telescope (RST) that are either impossible or impractical to verify exclusively through ground testing. Two major areas for integrated Modeling are Jitter and Thermal Distortion that require the exchanges of model performance predictions across disciplines. In both cases, distortions are impressed on optical models to evaluate the impact on boresight alignment and wave front error. In the case of Jitter, the disturbances are driven by reactions to motions most often from actuators; however, in the case of thermal distortion, the motions are driven by thermal expansion or contraction as a result of changing temperatures. This then requires a link further upstream to the thermal model, which is used to predict the thermal performance and temperature gradients and stability. The process for mapping temperatures from a thermal model to a corresponding structural model has been performed numerous times through the RST project lifecycle, with improvements in the accuracy, verification, and effort sought throughout. This paper describes some of the recent improvements to the process, including: capture of the visualization parameters, automatic generation of the mapped images for both the thermal and structural model groupings, and reduction in the effort to assemble the full set of mapped temperatures. These upgrades have greatly reduced the manual effort associated with thermal mapping and allowed for faster turn-around of Integrated Modeling predictions.

Thermal Mapping↗

NASA NTRS1977

Procedures for dealing with certain types of noise and systematic errors common to many Hadamard transform optical systems

Sources of noise and error correcting procedures characteristic of Hadamard transform optical systems were investigated. Reduction of spectral noise due to noise spikes in the data, the effect of random errors, the relative performance of Fourier and Hadamard transform spectrometers operated under identical detector-noise-limited conditions, and systematic means for dealing with mask defects are among the topics discussed. The distortion in Hadamard transform optical instruments caused by moving Masks, incorrect mask alignment, missing measurements, and diffraction is analyzed and techniques for reducing or eliminating this distortion are described.

Harwit, M.↗

NASA NTRS1989

Spectral calibration analysis of the airborne oceanographic lidar

Efforts were concentrated on the spectral resolution of the Airborne Oceanographic Lidar (AOL). This year's work was targeted towards the analysis of calibration techniques to enable the AOL to measure absolute radiances of both passive and active modes of operation. Absolute spectral calibration of the AOL is necessary in order to fully understand and monitor the sensitivity and stability of the total system. Calibration is also needed to obtain valid surface truth data, with which to improve the accuracy of satellite-borne oceanic color scanners. In particular, accurate measurements of oceanic chlorophyll concentrations rests upon reliable irradiance calibrations of both laser induced and solar induced chlorophyll fluoresence. An analysis was performed on the spectral calibration methods used by the AOL. The optical path of the instrumentation was examined to study how the radiance from a calibration sphere was influenced. Ray tracing analysis was performed, including the Cassegrain-telescope optics. It was determined that the calibration radiance was significantly effected by optical-defocusing, due to close positioning of the calibration sphere with respect to the telescope. Since the multi-mode usages of the AOL require varying altitudes and trajectories, a computational algorithm was developed to compensate for image distortions of the telescope optics. Secondary mirror blockage, secondary vignetting, and beam divergence was determined, in order to account for the actual amount of calibrated flux received at the spectral sensors.

Rousey, Carlton E.↗