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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.

UV radiation↗
NASA NTRSDate not supplied

Nearfield Anisotropic Mesh Adaptation for the Third AIAA Sonic Boom Workshop

Sonic boom prediction is critical for the development of low-boom supersonic aircraft and encouraging a replacement of the overland commercial supersonic ban with a certification standard. The Third AIAA Sonic Boom Workshop nearfield test cases provide a unique opportunity to review anisotropic adaptive-mesh strategies for complex geometries with important propulsion interaction effects. Anisotropic metrics formulated to control estimated Mach interpolation and integrated nearfield pressure signature (goal-oriented) errors are compared with the results obtained on workshop-provided meshes. Detailed flow solutions and mesh renderings are combined with computational schlieren images to reveal the complex interactions present in a shock-plume interaction wind tunnel model and a prototype of the Low-Boom Flight Demonstrator. A favorable comparison of independent implementations of the entire simulation process provides evidence that each process is implemented correctly and sets a solid foundation for confidence in the nearfield prediction of each method.

sonic boom↗
NASA NTRS2007

Visual end-effector position error compensation for planetary robotics

This paper describes a vision-guided manipulation algorithm that improves arm end-effector positioning to subpixel accuracy and meets the highly restrictive imaging and computational constraints of a planetary robotic flight system. Analytical, simulation-based, and experimental analyses of the algorithm's effectiveness and sensitivity to camera and arm model error is presented along with results on several prototype research systems and ground-in-the-loop technology experiments on the Mars Exploration Rover (MER) vehicles. A computationally efficient and robust subpixel end-effector fiducial detector that is instrumental to the algorithm's ability to achieve high accuracy is also described along with its validation results on MER data.

Nickels, Kevin↗
NASA NTRS2022

A Method for Measuring Optical Distortion in Curved Optical Surfaces using Moiré Interferometry

In FY2020, KSC’s Applied Physics Lab created a computer based image processing system to allow inspection of the new visors being developed for the Artemis Program. This system was based on an ASTM standard where the distortion of an image is used to determine the optical aberrations in a visor, but this approach is restricted to small fields-of-view (small areas of the visor) and is limited in its ability to reliably detect and measure distortion. From our experience with flat surface inspection, we know that other optical techniques can offer higher sensitivity and accuracy. [1] This memorandum describes a method to model and measure the distortion in curved optical surfaces using moir´e interferometry. We were able to apply this process to examine samples of the xEMU Artemis astronaut helmets. Design details are provided along with examples to illustrate performance.

Optical Distortion↗
NASA NTRSDate not supplied

ASTM International Conference on Additive Manufacturing – 2022

The In-Situ Project: Correlating In-situ monitoring data and Non-Destructive Evaluation (NDE) methods to characterize defect populations in Laser Powder Bed Fusion (L-PBF) material NDE is critical to enable the safe use of additive manufactured (AM) parts in aerospace applications. However, traditional NDE methods are not fully adaptable to complex AM geometries to appropriately screen for critical defects, and L-PBF processes inherently generate defects. In-situ monitoring techniques can give information about the quality of the build process and indicate process variations that could potentially create defects. Proven, causal correlations between in-situ monitoring indications and resulting defects are needed to inform about the final quality of the part. Thus, the development of a new approach is necessary. NASA started a two-phase study that would investigate techniques for characterizing defect populations in L-PBF material, first in material with induced defects, then in a nominal L-PBF process, by correlating in-situ, NDE, and serial sectioning data. Objectives also include the evaluation of seeded defect methodologies for realistic defect creation, creating a baseline of defect populations in a Qualified Material Process (QMP), characterizing the effect of heat treatment, and correlating defect populations with tensile and fatigue properties. By comparing computed tomography images, in-situ monitoring images, and metallography images, the team has been able to draw preliminary conclusions. They include a better understanding of the limitation of the NDE tools, learning about the “healing” of the samples observed during layer building and a comparison study on different laser power variation samples. Those preliminary conclusions will help dictate the follow-up work that will be presented at the conference.

additive manufacturing↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission's Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

thermal protection systems↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗
DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Image Processing

The Computer Graphics Center of North Carolina State University uses LAS, a COSMIC program, to analyze and manipulate data from Landsat and SPOT providing information for government and commercial land resource application projects. LAS is used to interpret aircraft/satellite data and enables researchers to improve image-based classification accuracies. The system is easy to use and has proven to be a valuable remote sensing training tool.

Source record↗
NASA NTRS1973

Preliminary geologic investigations in the Colorado Plateau using enhanced ERTS images

Bulk and computer enhanced frames of the Verde Valley region of Central Arizona, have been analyzed for structural information and rock unit identification. Most major rock units in areas of sparse ground cover are identifiable on enhanced false-color composites. Regional structural patterns are strikingly visible on the ERTS images. New features have been identified which will aid in the search for ground water near Flagstaff, Sedona and Stewart Ranch.

Goetz, A. F. H.↗
NASA NTRS1975

Impact of remote sensing upon the planning, management and development of water resources. Summary of computers and computer growth trends for hydrologic modeling and the input of ERTS image data processing load

An analysis of current computer usage by major water resources users was made to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era. The analysis showns significant impact due to the utilization and processing of ERTS CCT's data.

Castruccio, P. A.↗
NASA NTRS1991

Maximum entropy method applied to deblurring images on a MasPar MP-1 computer

A statistical inference method based on the principle of maximum entropy is developed for the purpose of enhancing and restoring satellite images. The proposed maximum entropy image restoration method is shown to overcome the difficulties associated with image restoration and provide the smoothest and most appropriate solution consistent with the measured data. An implementation of the method on the MP-1 computer is described, and results of tests on simulated data are presented.

Bonavito, N. L.↗
NASA NTRS1983

Diagnosing convective instability using VAS data

The utility of combining visible and various infrared images from the VAS to produce a forecasting tool, that can be available on a near real time basis, to predict severe weather development is shown. Areas where dry air in the midtroposphere overlays substantial moisture at low levels are used to diagnose mesoscale regions that have the potential for being convectively unstable before the onset of severe convection. Specifically, 6.7 micron water vapor imagery, used for isolating regions of substantial midlevel dryness, are combined with images of low level clouds or with split-window low level moisture images to delineate regions that have the potential for convective instability. In areas where scattered low level clouds are present, computer generated, color image combinations are used to isolate those warm, low level clouds that are in potential convectively unstable environments from clouds that exist under a deeply moist atmosphere. In clear regions, the split window technique is used for delineating areas of substantial boundary layer moisture. These images are again computer overlayed by the midlevel dryness to produce a color coded image of potential convective instability.

Petersen, R. A.↗
NASA NTRS1971

Investigation of Atmospheric Effects in Image Transfer

Several computations on radiation emerging from the atmosphere for different aerosol models have been performed and the reflection matrix of a given surface was defined by incident light signature analyses. A two channel polarimeter is being constructed for measuring the Stokes parameters of reflected light. Surface albedo measurements were made in six wavelength ranges in the ultraviolet, visible, and near infrared spectral regions for six different vegetated surfaces, four soil surfaces, and two roadway surfaces. The effects of polarization changes on the intensity of projected images were studied by using a rotatable polarizer in front of an aerial camera lens.

Coulson, K. L.↗
NASA NTRS2011

Image Registration for Stability Testing of MEMS

Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.

Memarsadeghi, Nargess↗
NASA NTRS1984

LANDSAT data and interactive computer mapping

The integration of image processing capabilities with interactive computer mapping systems is discussed. It is noted that the accomplishment of this integration will result in powerful geographic information systems which will enhance the applicatons of LANDSAT and other types of remotely sensed data in solving problems in the resource planning and management domain.

Grady, R. K.↗