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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Supergranule Superrotation Identified as a Projection Effect

Previous measurements of the rotation rate of the supergranule Doppler velocity pattern revealed surprising characteristics: (1) the pattern rotates faster than the plasma at the surface and, at each latitude, it rotates faster than the plasma at any level below the surface (superrotation), (2) larger cells rotate more rapidly than smaller cells, and (3) faster rotation rates are found when using cross-correlation techniques with larger time-lags between Doppler images. We simulate the supergranulation velocity pattern using a spectrum for the cellular flows that matches the observed spectrum but we keep the pattern unchanged and rotating rigidly. Our simulation shows that the superrotation and its dependence upon cell size can be largely reproduced by projection effects on the line-of-sight Doppler velocity signal. The remaining variation in rotation rate with cell size can be attributed to cells smaller than supergranules extending through shallower layers which have slower rotation rates.

Hathaway, D. H.↗
NASA NTRS2006

Correcting STIS CCD Point-Source Spectra for CTE Loss

We review the on-orbit spectroscopic observations that are being used to characterize the Charge Transfer Efficiency (CTE) of the STIS CCD in spectroscopic mode. We parameterize the CTE-related loss for spectrophotometry of point sources in terms of dependencies on the brightness of the source, the background level, the signal in the PSF outside the standard extraction box, and the time of observation. Primary constraints on our correction algorithm are provided by measurements of the CTE loss rates for simulated spectra (images of a tungsten lamp taken through slits oriented along the dispersion axis) combined with estimates of CTE losses for actual spectra of spectrophotometric standard stars in the first order CCD modes. For point-source spectra at the standard reference position at the CCD center, CTE losses as large as 30% are corrected to within approx.1% RMS after application of the algorithm presented here, rendering the Poisson noise associated with the source detection itself to be the dominant contributor to the total flux calibration uncertainty.

Goudfrooij, Paul↗
NASA NTRS2006

Very High-Speed Digital Video Capability for In-Flight Use

digital video camera system has been qualified for use in flight on the NASA supersonic F-15B Research Testbed aircraft. This system is capable of very-high-speed color digital imaging at flight speeds up to Mach 2. The components of this system have been ruggedized and shock-mounted in the aircraft to survive the severe pressure, temperature, and vibration of the flight environment. The system includes two synchronized camera subsystems installed in fuselage-mounted camera pods (see Figure 1). Each camera subsystem comprises a camera controller/recorder unit and a camera head. The two camera subsystems are synchronized by use of an MHub(TradeMark) synchronization unit. Each camera subsystem is capable of recording at a rate up to 10,000 pictures per second (pps). A state-of-the-art complementary metal oxide/semiconductor (CMOS) sensor in the camera head has a maximum resolution of 1,280 1,024 pixels at 1,000 pps. Exposure times of the electronic shutter of the camera range from 1/200,000 of a second to full open. The recorded images are captured in a dynamic random-access memory (DRAM) and can be downloaded directly to a personal computer or saved on a compact flash memory card. In addition to the high-rate recording of images, the system can display images in real time at 30 pps. Inter Range Instrumentation Group (IRIG) time code can be inserted into the individual camera controllers or into the M-Hub unit. The video data could also be used to obtain quantitative, three-dimensional trajectory information. The first use of this system was in support of the Space Shuttle Return to Flight effort. Data were needed to help in understanding how thermally insulating foam is shed from a space shuttle external fuel tank during launch. The cameras captured images of simulated external tank debris ejected from a fixture mounted under the centerline of the F-15B aircraft. Digital video was obtained at subsonic and supersonic flight conditions, including speeds up to Mach 2 and altitudes up to 50,000 ft (15.24 km). The digital video was used to determine the structural survivability of the debris in a real flight environment and quantify the aerodynamic trajectories of the debris.

Corda, Stephen↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Rutishauser, David↗
NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully Integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Epp, Chirold D.↗
NASA NTRSDate not supplied

Landslide Runout Analysis Using Remote Sensing for Lower and Upper Barun Lakes

Landslides are recognized as common triggers of glacial lake outburst floods in high mountain regions, but the remoteness of dangerous glacial lakes makes monitoring and disaster management very difficult. Using a very high resolution digital elevation model produced from a series of stereo satellite images, we simulate debris flows and rock avalanche runout around two very dangerous lakes in high mountain Nepal. We constrain particularly likely sources of these slope failures to simulate 79,800 potential debris flows and rock avalanches of volumes ranging from 1,000 m3 to 1,000,000 m3. This allowed us to identify zones in and around dangerous glacial lakes that are most exposed to landslide runout hazards, along with the most frequent source areas that produced landslides that may run into either of the two glacial lakes. Although our empirical-statistical model presented limitations that numerical models attempt to overcome, our probabilistic approach combined with a very high resolution DEM may shed new light on how to identify sources and impact areas of debris flows and rock avalanches that may cascade into downstream disasters.

landslides↗
DOE OSTI2016

Methods for determining absorbed dose information

The disclosure discusses methods for determining absorbed dose information. A tomography imaging device generates an anatomy image relating to anatomy of a particular patient. A tomography imaging device also generates multiple radioactivity images regarding radioactivity distribution of an internally administered pharmaceutical over time in the particular patient. The radioactivity images related to the radioactivity distribution over time are registered. Each radioactivity image is combined with each anatomy image to create activity images. A Monte Carlo simulation for each activity image is run to obtain absorbed dose-rate images of the pharmaceutical at multiple times. The absorbed dose-rate images are integrated over time to obtain a total absorbed dose image. The absorbed dose-rate images and the total absorbed dose image are used to obtain a biologically effective dose (BED) image. The BED image is used to obtain an equivalent uniform does (EUD) of BED values for a chosen anatomical region.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.

Damish, S.↗
DOE OSTI2025

Accurate Dark Energy Constraints via the Precise Characterization of Galaxy Intrinsic Alignment Coupled with Shear and Redshift Interference

One of the most fundamental mysteries we face in physics is understanding what the nature of the dark energy component of our Universe is that causes the accelerated expansion of space. A powerful way to study dark energy is via weak gravitational lensing due to large-scale structure in the Universe, which we observe as tiny distortions in the shapes of very distant galaxies due to the bending of light by foreground mass. The Dark Energy Survey (DES) and Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) were designed to enable unprecedented measurements of these distortions in the shapes of galaxies to study dark energy. The intrinsic shapes of galaxies before they are distorted by weak gravitational lensing can cancel out some of the effects of lensing to obscure or bias what we infer about the nature of dark energy. This research program has led to improved image-level simulations of the Rubin Observatory LSST data. These simulations have made it possible to characterize for the first time the cosmic impact of these intrinsic shapes on biasing weak gravitational lensing calibration. This work will inform crucial improvements in methodology for studying dark energy with weak gravitational lensing at the precision promised by the the Rubin Observatory LSST over the next decade to ensure we are able to infer unbiased information about the nature of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy

Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation of additional modules to include any aspect of the treatment process, and therefore has great potential for both assessment and optimization within radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Monte Carlo simulation of a biological object with optical coherent tomography structural images using a voxel-based geometry of a medium

We describe a Monte Carlo algorithm for simulating an interference signal and constructing a structural image of a biological object using optical coherent tomography (OCT). The algorithm is specific in that the object geometry is described by using a three-dimensional array of voxels combined into parallelograms, and the border cross checking is carried out by using an improved Smith algorithm. The geometry of the simulated object is reconstructed based on the structure of real biological tissues obtained by OCT. The algorithm efficiency is estimated by comparing experimental and corresponding simulated images of a subcutaneous blood vessel and upper layer of human skin in vivo . (biophotonics)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

deep learning↗
NASA NTRS1991

Emerging standards for still image compression: A software implementation and simulation study

The software implementation is described of an emerging standard for the lossy compression of continuous tone still images. This software program can be used to compress planetary images and other 2-D instrument data. It provides a high compression image coding capability that preserves image fidelity at compression rates competitive or superior to most known techniques. This software implementation confirms the usefulness of such data compression and allows its performance to be compared with other schemes used in deep space missions and for data based storage.

Pollara, F.↗