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All sources NASA NTRS DOE OSTI

DOE OSTI2022

High-throughput, in situ imaging of multi-layer powder-blown directed energy deposition with angled nozzle

Laser metal additive manufacturing (AM) has become an increasingly popular technology due to its flexibility in geometry and materials. As one of the commercialized additive processes, powder-blown directed energy deposition (DED) has been used in multiple industries such as aerospace, automotive, medical device, etc. However, a lack of fundamental understanding remains for this process, and many opportunities for alloy development and implementation can be identified. A high-throughput, in situ DED system capable of multi-layer builds that can address these issues is presented here. In this work, implications of layer heights and energy density are investigated through an extensive process parameter sweep, showcasing the power of a high-throughput setup while also discussing multi-layer interactions.

47 OTHER INSTRUMENTATION↗

NASA NTRS1991

Motion detection, novelty filtering, and target tracking using an interferometric technique with GaAs phase conjugate mirror

A method and apparatus for detecting and tracking moving objects in a noise environment cluttered with fast- and slow-moving objects and other time-varying background. A pair of phase conjugate light beams carrying the same spatial information commonly cancel each other out through an image subtraction process in a phase conjugate interferometer, wherein gratings are formed in a fast photorefractive phase conjugate mirror material. In the steady state, there is no output. When the optical path of one of the two phase conjugate beams is suddenly changed, the return beam loses its phase conjugate nature and the interferometer is out of balance, resulting in an observable output. The observable output lasts until the phase conjugate nature of the beam has recovered. The observable time of the output signal is roughly equal to the formation time of the grating. If the optical path changing time is slower than the formation time, the change of optical path becomes unobservable, because the index grating can follow the change. Thus, objects traveling at speeds which result in a path changing time which is slower than the formation time are not observable and do not clutter the output image view.

Cheng, Li-Jen↗

DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗

DOE OSTI2022

Mechanisms of graphite ablation by sub-millisecond ytterbium fiber laser pulses

Graphite is a key material in a variety of cross-cutting applications in energy conversion, energy storage, and nuclear energy. Recently, temporally modulated continuous wave lasers have been shown to produce well-defined ablation features in graphite at relatively high processing speeds. In this work, we analyze in detail the laser ablation dynamics of single-pulse ablation in the sub-millisecond time regime to elucidate the origins of the resulting well-defined ablation craters using a combination of time-resolved emission imaging, diffuse reflection/scattering imaging, and optical emission spectroscopy. These multimodal in situ diagnostics revealed three main contributors to achieve well-defined ablation features: (1) rapid ejection of particles with ~100 m/s speed, (2) ablation of the graphite in the gaseous form, and (3) absence of bulk liquid motion, which is typically observed in laser processing of metals. Plasma plume formation was sustained throughout the duration of the laser pulse (500 μs). Here, this work provides insights into the complex physical and chemical mechanisms of sub-millisecond laser–matter interactions, which are critical for parameter space optimization and tailoring of laser machining and drilling processes.

42 ENGINEERING↗

NASA NTRS2004

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Srivastava, Ashok↗

NASA NTRS1974

A simulation of synthetic aperture radar imaging of ocean waves

A simulation of radar imaging of ocean waves with synthetic aperture techniques is presented. The modelling is simplistic from the oceanographic and electromagnetic viewpoint in order to minimize the computational problems, yet reveal some of the physical problems associated with the imaging of moving ocean waves. The model assumes: (1) The radar illuminates a one-dimensional, one harmonic ocean wave. (2) The scattering is assumed to be governed by geometrical optics. (3) The radar is assumed to be down-looking, with Doppler processing (range processing is suppressed due to the one-dimensional nature of the problem). (4) The beamwidth of the antenna (or integration time) is assumed to be sufficiently narrow to restrict the specular points of the peaks and troughs of the wave. The results show that conventional processing of the image gives familiar results if the ocean waves are stationary. When the ocean wave dispersion relationship is satisfied, the image is smeared due to the motion of the specular points over the integration time. In effect, the image of the ocean is transferred to the near field of the synthetic aperture.

Swift, C. T.↗

NASA NTRS1991

Instrumentation for optical ocean remote sensing

Instruments used in ocean color remote sensing algorithm development, validation, and data acquisition which have the potential for further commercial development and marketing are discussed. The Ocean Data Acquisition System (ODAS) is an aircraft-borne radiometer system suitable for light aircraft, which has applications for rapid measurement of chlorophyll pigment concentrations along the flight line. The instrument package includes a three channel radiometer system for upwelling radiance, an infrared temperature sensor, a three-channel downwelling irradiance sensor, and Loran-C navigation. Data are stored on a PC and processed to transects or interpolated 'images' on the ground. The instrument has been in operational use for two and one half years. The accuracy of pigment concentrations from the instrument is quite good, even in complex Chesapeake Bay waters. To help meet the requirement for validation of future satellite missions, a prototype air-deployable drifting buoy for measurement of near-surface upwelled radiance in multiple channnels is undergoing test deployment. The optical drifter burst samples radiance, stores and processes the data, and uses the Argos system as a data link. Studies are underway to explore the limits to useful lifetime with respect to power and fouling.

Esaias, W. E.↗

NASA NTRS2018

On-Orbit Verification of GLM Navigation on GOES-16

The GOES-R flight project has developed the Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) to perform independent INR evaluations of the optical instruments on the GOES-R series spacecraft. In this presentation, we document the development of navigation (NAV) evaluation capabilities within IPATS for the Geostationary Lightning Mapper (GLM). We also discuss the post-processing quality filtering developed for GLM NAV, and present example results for several GLM background image datasets. Initial results suggest that GOES-16 GLM is compliant with navigation requirements.

GLM↗

NASA NTRS2018

Evaluation of Geostationary Lightning Mapper (GLM) Navigation Performance with the INR Performance Assessment Toolset (IPATS)

Isaacson, Peter J.↗

NASA NTRS1982

Advanced multispectral remote sensing technology and applications; Proceedings of the Conference, Arlington, VA, May 6, 7, 1982

Sensor system concepts are discussed, taking into account a multispectral linear array instrument design, an improved earth resources sensing instrument, design tradeoffs for a multispectral linear array instrument, imaging spectrometer technologies for advanced earth remote sensing, and the design concept for an optimized earth radiation budget sensor. Topics related to supporting optics and signal processing methodologies are explored, giving attention to two mirror objective design for multispectral remote sensing, the measurement of optical modulation functions in sparsely sampled mosaic focal plane arrays, multispectral linear array focal plane signal processing, signal processing for the NASA multispectral linear array, the concept for a multiple resolution pushbroom sensor, and the evolution of the octal radiometer. Techniques and applications for multispectral data are considered along with aspects of focal plane technology.

Ando, K. J.↗

NASA NTRS1993

Algorithms For Detection Of Correlation Spots

Three algorithms provide for improved postprocessing of outputs of optical correlators based on binary phase-only filters. Detect correlation spots. Function in presence of noise and executed rapidly. First algorithm starts processing correlation-image data while data fed out of video camera and digitized for subsequent analysis. Second involves convolution of correlation image with small-window two-dimensional impulse-response function followed by threshold operation in which negative values of convolution integral set to zero. Third affects generation as well as postprocessing of correlation image.

Scholl, Marija S.↗

NASA NTRS2004

Development of an Optical-Real Time Polarimetric Imaging System for Aerospace Applications

The design of an optical polarimetric imaging system, aimed to detect cracks or structural defects on the surface of rotating aircraft engine shafts, is presented. The experimental results have been processed by a real time wavelet denouncing system implemented by a Filed Programmable Gate Arrays (FPGA), which has been designed for this purpose. The experimental results clearly indicate that high signal-to-noise ratio signals can be obtained.

Giakos, George C.↗

NASA NTRS2016

Development and Implementation of a Generic Analysis Template for Structural-Thermal-Optical-Performance Modeling

Performance-related effects of system level temperature changes can be a key consideration in the design of many types of optical instruments. This is especially true for space-based imagers, which may require complex thermal control systems to maintain alignment of the optical components. Structural-Thermal-Optical-Performance (STOP) analysis is a multi-disciplinary process that can be used to assess the performance of these optical systems when subjected to the expected design environment. This type of analysis can be very time consuming, which makes it difficult to use as a trade study tool early in the project life cycle. In many cases, only one or two iterations can be performed over the course of a project. This limits the design space to best practices since it may be too difficult, or take too long, to test new concepts analytically. In order to overcome this challenge, automation, and a standard procedure for performing these studies is essential. A methodology was developed within the framework of the Comet software tool that captures the basic inputs, outputs, and processes used in most STOP analyses. This resulted in a generic, reusable analysis template that can be used for design trades for a variety of optical systems. The template captures much of the upfront setup such as meshing, boundary conditions, data transfer, naming conventions, and post-processing, and therefore saves time for each subsequent project. A description of the methodology and the analysis template is presented, and results are described for a simple telescope optical system.

Scola, Salvatore↗

NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗

NASA NTRS1999

Application of Novel Nonlinear Optical Materials to Optical Processing

We describe wave mixing and interactions in nonlinear photorefractive polymers and disodium flourescein. Higher diffracted orders yielding forward phase conjugation can be generated in a two-wave mixing geometry in photorefractive polymers, and this higher order can be used for image edge enhancement and correlation. Four-wave mixing and phase conjugation is studied using nonlinear disodium floureschein, and the nature and properties of gratings written in this material are investigated.

Banerjee, Partha P.↗

DOE OSTI2022

All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8

ABSTRACT In this paper, we present photometric redshift (photo-z) estimates for the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently the most sensitive optical survey covering the majority of the extragalactic sky. Our photo-z methodology is based on a machine-learning approach, using sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that allow regions of parameter space to be identified and trained separately in a purely data-driven way. The same GMMs are also used to calculate cost-sensitive learning weights that mitigate biases in the spectroscopic training sample. By design, this approach aims to produce reliable and unbiased predictions for all parts of the parameter space present in wide area surveys. Compared to previous literature estimates using the same underlying photometry, our photo-zs are significantly less biased and more accurate at z > 1, with negligible loss in precision or reliability for resolved galaxies at z < 1. Our photo-z estimates offer accurate predictions for rare high-value populations within the parent sample, including optically selected quasars at the highest redshifts (z > 6), as well as X-ray or radio continuum selected populations across a broad range of flux (densities) and redshift. Deriving photo-z estimates for the full Legacy Imaging Surveys Data Release 8, the catalogues provided in this work offer photo-z estimates predicted to be of high quality for ≳9 × 108 galaxies over ${\sim}19\, 400\, \text{deg}^{2}$ and spanning 0 < z ≲ 7, offering one of the most extensive samples of redshift estimates ever produced.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

On the Cloud Observations in JAXA's Next Coming Satellite Missions

The use of JAXA's next generation satellites, the EarthCARE and the GCOM-C, for observing overall cloud systems on the Earth is discussed. The satellites will be launched in the middle of 2010-era and contribute for observing aerosols and clouds in terms of climate change, environment, weather forecasting, and cloud revolution process study. This paper describes the role of such satellites and how to use the observing data showing concepts and some sample viewgraphs. Synergistic use of sensors is a key of the study. Visible to infrared bands are used for cloudy and clear discriminating from passively obtained satellite images. Cloud properties such as the cloud optical thickness, the effective particle radii, and the cloud top temperature will be retrieved from visible to infrared wavelengths of imagers. Additionally, we are going to combine cloud properties obtained from passive imagers and radar reflectivities obtained from an active radar in order to improve our understanding of cloud evolution process. This is one of the new techniques of satellite data analysis in terms of cloud sciences in the next decade. Since the climate change and cloud process study have mutual beneficial relationship, a multispectral wide-swath imagers like the GCOM-C SGLI and a comprehensive observation package of cloud and aerosol like the EarthCARE are both necessary.

satellite remote sensing↗

DOE OSTI2022

Data Mining – Image Analysis of Radiography for Zr Redistribution

The research effort described in this report represents a first attempt to investigate the radial redistribution of Zr in ternary fuel alloys U-xPu-10Zr (x = 0, 8, 19) irradiated in the in-reactor fuel experiments during the operation of the U.S. Department of Energy’s (DOE’s) Experimental Breeder Reactor II (EBR-II) at Idaho National Laboratory (INL) using methods of image processing on post-irradiation neutron radiographs. Approximately 130,000 metal fuel pins were irradiated in EBR-II during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed historically now allowing application of modern image analysis techniques to characterize fuel behavior, such as fuel swelling, fluff formation, and now, fuel alloy constituent redistribution. The redistribution of fuel components depends on the temperature field, radially, within the fuel. Specifically, Zr is expected to redistribute radially towards the center of the pin, as well as towards the outer zones. Currently, direct imaging of a pin cross-section through optical methods or scanning electron microscopy (SEM) is used to study the constituents’ redistribution, which is very time-consuming and can only be applied to a limited number of pins. An automated image processing technique allowing for the investigation of fuel radial redistribution zones would significantly accelerate data analysis. In general, if the fuel temperature is hot enough, three redistribution zones are expected, corresponding to the main fuel components (e.g., U, Pu, Zr). While more assessment will be performed in fiscal year (FY)-2023, it seems possible to differentiate the pins according to their fuel composition using image analysis techniques on neutron radiographs of metallic fuel pins based on the analysis to date.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗