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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Marching Cubes in Cylindrical and Spherical Coordinates

Isosurface extraction is a common analysis and visualization technique for three-dimensional scalar data. Marching Cubes is the most commonly-used algorithm for finding polygonal representations of isosurfaces in such data. We extend Marching Cubes to produce geometry for data sets that lie in spherical and cylindrical coordinate systems as well as show the steps for derivation of transformations for other coordinate systems.

techniques marching cubes isosurfaces coordinate s↗
DOE OSTI2023

LLNL Wind Cube V2 Lidar / Raw Data

This dataset contains vertical profiling lidar datafrom AWAKEN site A1. This is the WindCube v2 lidar. The Windcube v2 replaced the ZX300 at this location at the end of March 2023.

17 WIND ENERGY↗
NASA NTRS2018

Restructuring Big Data to Improve Data Access and Performance in Analytic Services Making Research More Efficient for the Study of Extreme Weather Events and Application User Communities

By developing and enhancing various services and tools, the GES DISC provides users with the capability to access and visualize data, and to make comparisons of data from multiple sensor and models via a number of cross-discipline projects. Discovering Data via Faceted Web Interface Web interface to data products and services Search and Download mechanisms Dataset Landing Pages Accessing Data through Interoperable Services: GDS – GrADS Data Server OPeNDAP - Open-source Project for a Network Data Access Protocol WMS – OGC service GIS connector – allowing IS tools to access data easier (coming soon) HTTPS -- direct online access Downloading Data Basics: Subset and egridding Service – Parameter, Spatial, Time, Vertical, Mean averaging, format conversion, and regridding for L3/L4 gridded data Swath Data Subsetter – Parameter, spatial subset of L2 /L1 data. Visualizing Data Online: Giovanni –Visualization and Analysis L3/L4 gridded data AIRS NRT Viewer – AIRS near-real-time DQVis – L2 data quality visualization

data cube↗
NASA NTRS2013

Optical Alignment of the Global Precipitation Measurement (GPM) Star Trackers

The optical alignment of the star trackers on the Global Precipitation Measurement (GPM) core spacecraft at NASA Goddard Space Flight Center (GSFC) was challenging due to the layout and structural design of the GPM Lower Bus Structure (LBS) in which the star trackers are mounted as well as the presence of the star tracker shades that blocked line-of-sight to the primary star tracker optical references. The initial solution was to negotiate minor changes in the original LBS design to allow for the installation of a removable item of ground support equipment (GSE) that could be installed whenever measurements of the star tracker optical references were needed. However, this GSE could only be used to measure secondary optical reference cube faces not used by the star tracker vendor to obtain the relationship information and matrix transformations necessary to determine star tracker alignment. Unfortunately, due to unexpectedly large orthogonality errors between the measured secondary adjacent cube faces and the lack of cube calibration data, we required a method that could be used to measure the same reference cube faces as originally measured by the vendor. We describe an alternative technique to theodolite auto-collimation for measurement of an optical reference mirror pointing direction when normal incidence measurements are not possible. This technique was used to successfully align the GPM star trackers and has been used on a number of other NASA flight projects. We also discuss alignment theory as well as a GSFC-developed theodolite data analysis package used to analyze angular metrology data.

Hetherington, Samuel↗
DOE OSTI2024

Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods

Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to implement, it has limitations when the underlying data is complex and high-order, which is the case for most real-world data. Linear interpolation can output vertices at the wrong location. Its inability to deal with sharp features and features smaller than grid cells can lead to an incorrect isosurface with holes and broken pieces. Despite these limitations, isosurface visualizations typically do not include insight into the spatial location and the magnitude of these errors. We utilize high-order interpolation methods with MC algorithms and interactive visualization to highlight these uncertainties. Our visualization tool helps identify the regions of high interpolation errors. It also allows users to query local areas for details and compare the differences between isosurfaces from different interpolation methods. In addition, we employ high-order methods to identify and reconstruct possible features that linear methods cannot detect. We showcase how our visualization tool helps explore and understand the extracted isosurface errors through synthetic and real-world data.

Ouermi, Timbwaoga↗
DOE OSTI2024

Data-Driven Computation of Probabilistic Marching Cubes for Efficient Visualization of Level-Set Uncertainty

Uncertainty visualization is an important emerging research area. Being able to visualize data uncertainty can help scientists improve trust in analysis and decision-making. However, visualizing uncertainty can add computational overhead, which can hinder the efficiency of analysis. In this paper, we propose novel data-driven techniques to reduce the computational requirements of the probabilistic marching cubes (PMC) algorithm. PMC is an uncertainty visualization technique that studies how uncertainty in data affects level-set positions. However, the algorithm relies on expensive Monte Carlo (MC) sampling for the multivariate Gaussian uncertainty model because no closed-form solution exists for the integration of multivariate Gaussian. In this work, we propose the eigenvalue decomposition and adaptive probability model techniques that reduce the amount of MC sampling in the original PMC algorithm and hence speed up the computations. Our proposed methods produce results that show negligible differences compared with the original PMC algorithm demonstrated through metrics, including root mean squared error, maximum error, and difference images. We demonstrate the performance and accuracy evaluations of our data-driven methods through experiments on synthetic and real datasets.

Athawale, Tushar↗
DOE OSTI2017

On the regularity of the covariance matrix of a discretized scalar field on the sphere

We present a comprehensive study of the regularity of the covariance matrix of a discretized field on the sphere. In a particular situation, the rank of the matrix depends on the number of pixels, the number of spherical harmonics, the symmetries of the pixelization scheme and the presence of a mask. Taking into account the above mentioned components, we provide analytical expressions that constrain the rank of the matrix. They are obtained by expanding the determinant of the covariance matrix as a sum of determinants of matrices made up of spherical harmonics. We investigate these constraints for five different pixelizations that have been used in the context of Cosmic Microwave Background (CMB) data analysis: Cube, Icosahedron, Igloo, GLESP and HEALPix, finding that, at least in the considered cases, the HEALPix pixelization tends to provide a covariance matrix with a rank closer to the maximum expected theoretical value than the other pixelizations. The effect of the propagation of numerical errors in the regularity of the covariance matrix is also studied for different computational precisions, as well as the effect of adding a certain level of noise in order to regularize the matrix. In addition, we investigate the application of the previous results to a particular example that requires the inversion of the covariance matrix: the estimation of the CMB temperature power spectrum through the Quadratic Maximum Likelihood algorithm. Finally, some general considerations in order to achieve a regular covariance matrix are also presented.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

MAGIC: M arching Cubes Isosurface Uncertainty Visualization for G auss i an Uncertain Data With Spatial C orrelation

Here, in this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations,existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley's derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to 585× and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.

Gaussian↗
NASA NTRS1973

A solution of the geodetic boundary value problem to order e3

A solution is obtained for the geodetic boundary value problem which defines height anomalies to + or - 5 cm, if the earth were rigid. The solution takes into account the existence of the earth's topography, together with its ellipsoidal shape and atmosphere. A relation is also established between the commonly used solution of Stokes and a development correct to order e cubed. The data requirements call for a complete definition of gravity anomalies at the surface of the earth and a knowledge of elevation characteristics at all points exterior to the geoid. In addition, spherical harmonic representations must be based on geocentric rather than geodetic latitudes.

Mather, R. S.↗
NASA NTRS1985

Importance of Gravity for Plant Growth and Behavior

Flight experiments on the importance of gravity to plant growth and behavior are reported. The following studies were undertaken: (1) hyperastic responses to incremental changes of an axially imposed centripetal force; (2) Spacelab-1 experiments, methods for preparing soil in flight hardware containers were impound, to ensure desired moisture content and minimal contamination probability; (3) mesocotyl growth patterns were established by Avena lore exposure to red light during early seedling outogency; (4) the development of flight hardware; (5) choice of member of seedlings in each cube; (6) data processing and reduction; (7) clinostat validation; circummutation in space was more vigorous than on Earth based clinostat.

Brown, A. H.↗
NASA NTRS2011

Final Report: MaRSPlus Sensor System Electrical Cable Management and Distributed Motor Control Computer Interface

The success of JPL's Next Generation Imaging Spectrometer (NGIS) in Earth remote sensing has inspired a follow-on instrument project, the MaRSPlus Sensor System (MSS). One of JPL's responsibilities in the MSS project involves updating the documentation from the previous JPL airborne imagers to provide all the information necessary for an outside customer to operate the instrument independently. As part of this documentation update, I created detailed electrical cabling diagrams to provide JPL technicians with clear and concise build instructions and a database to track the status of cables from order to build to delivery. Simultaneously, a distributed motor control system is being developed for potential use on the proposed 2018 Mars rover mission. This system would significantly reduce the mass necessary for rover motor control, making more mass space available to other important spacecraft systems. The current stage of the project consists of a desktop computer talking to a single "cold box" unit containing the electronics to drive a motor. In order to test the electronics, I developed a graphical user interface (GUI) using MATLAB to allow a user to send simple commands to the cold box and display the responses received in a user-friendly format.

data cubes↗
NASA NTRS1994

Simulation of arthroscopic surgery using MRI data

With the availability of Magnetic Resonance Imaging (MRI) technology in the medical field and the development of powerful graphics engines in the computer world the possibility now exists for the simulation of surgery using data obtained from an actual patient. This paper describes a surgical simulation system which will allow a physician or a medical student to practice surgery on a patient without ever entering an operating room. This could substantially lower the cost of medial training by providing an alternative to the use of cadavers. This project involves the use of volume data acquired by MRI which are converted to polygonal form using a corrected marching cubes algorithm. The data are then colored and a simulation of surface response based on springy structures is performed in real time. Control for the system is obtained through the use of an attached analog-to-digital unit. A remote electronic device is described which simulates an imaginary tool having features in common with both arthroscope and laparoscope.

Heller, Geoffrey↗
NASA NTRS2024

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This first release of the database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series calculations were run on the NASA High-End Compute Capability (HECC) supercomputer and the corresponding airfoil performance coefficients are embedded in the Appendix of this document for public distribution. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop OVERFLOW-quality airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. Downstream surrogate models enable OVERFLOW- quality airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle-of-attack within the bounds of the database.

Database↗
NASA NTRSDate not supplied

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The PALMO database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series OVERFLOW calculations were run on the NASA High-End Compute Capability (HECC) supercomputer. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop accurate airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. This work presents an example PALMO surrogate model that enables accurate airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle of attack within the bounds of the database. Airfoil performance tables predicted for an airfoil not used in training the model are used in three-dimensional OVERFLOW simulations to quantify the downstream accuracy on aggregate rotor performance metrics. For the NACA 3415 airfoil, which had no common thickness or camber with the training data, the surrogate predicted and CFD generated tables were within 2.1% of each other in the forward flight lift to drag metric. This suggests that performance tables generated for airfoils within the bounds of the PALMO database will yield aggregate rotor performance predictions on par with tables generated from directly running OVERFLOW airfoil calculations. The PALMO airfoil performance coefficients are available publicly.

Database↗
DOE OSTI2015

SU-E-T-570: New Quality Assurance Method Using Motion Tracking for 6D Robotic Couches

Purpose: To accommodate geometrically accurate patient positioning, a robotic couch that is capable of 6-degrees of freedom has been introduced. However, conventional couch QA methods are not sufficient to enable the necessary accuracy of tests. Therefore, we have developed a camera based motion detection and geometry calibration system for couch QA. Methods: Employing a Visual-Tracking System (VTS, BonitaB10, Vicon, UK) which tracks infrared reflective(IR) markers, camera calibration was conducted using a 5.7 × 5.7 × 5.7 cm{sup 3} cube attached with IR markers at each corner. After positioning a robotic-couch at the origin with the cube on the table top, 3D coordinates of the cube’s eight corners were acquired by VTS in the VTS coordinate system. Next, positions in reference coordinates (roomcoordinates) were assigned using the known relation between each point. Finally, camera calibration was completed by finding a transformation matrix between VTS and reference coordinate systems and by applying a pseudo inverse matrix method. After the calibration, the accuracy of linear and rotational motions as well as couch sagging could be measured by analyzing the continuously acquired data of the cube while the couch moves to a designated position. Accuracy of the developed software was verified through comparison with measurement data when using a Laser tracker (FARO, Lake Mary, USA) for a robotic-couch installed for proton therapy. Results: VTS system could track couch motion accurately and measured position in room-coordinates. The VTS measurements and Laser tracker data agreed within 1% of difference for linear and rotational motions. Also because the program analyzes motion in 3-Dimension, it can compute couch sagging. Conclusion: Developed QA system provides submillimeter/ degree accuracy which fulfills the high-end couch QA. This work was supported by the National Research Foundation of Korea funded by Ministry of Science, ICT & Future Planning. (2013M2A2A7043507 and 2012M3A9B6055201)

62 RADIOLOGY AND NUCLEAR MEDICINE↗