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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimization Studies of Radiation Shielding for the PIP-II Project at Fermilab

The PIP-II project at Fermilab, which includes an 800-MeV superconducting LINAC, demands rigorous radiation shielding optimization to meet safety requirements. We updated the MARS geometry model to reflect new magnet and collimator designs and introduced high-resolution detector planes to better capture radiation field distributions. To overcome the significant computational demands, we implemented a well-known branching technique that drastically reduced simulation runtimes while maintaining statistical integrity. This was achieved through particle splitting and the application of Russian Roulette techniques. Additionally, new graphical tools were created to streamline data visualization and MARS code usability.

Makovec, Alajos [Fermilab] (ORCID:0000000286157492↗
NASA NTRS1980

The effects of motion and g-seat cues on pilot simulator performance of three piloting tasks

Data are presented that show the effects of motion system cues, g-seat cues, and pilot experience on pilot performance during takeoffs with engine failures, during in-flight precision turns, and during landings with wind shear. Eight groups of USAF pilots flew a simulated KC-135 using four different cueing systems. The basic cueing system was a fixed-base type (no-motion cueing) with visual cueing. The other three systems were produced by the presence of either a motion system or a g-seat, or both. Extensive statistical analysis of the data was performed and representative performance means were examined. These data show that the addition of motion system cueing results in significant improvement in pilot performance for all three tasks; however, the use of g-seat cueing, either alone or in conjunction with the motion system, provides little if any performance improvement for these tasks and for this aircraft type.

Showalter, T. W.↗
NASA NTRS2008

Parametric-Studies and Data-Plotting Modules for the SOAP

"Parametric Studies" and "Data Table Plot View" are the names of software modules in the Satellite Orbit Analysis Program (SOAP). Parametric Studies enables parameterization of as many as three satellite or ground-station attributes across a range of values and computes the average, minimum, and maximum of a specified metric, the revisit time, or 21 other functions at each point in the parameter space. This computation produces a one-, two-, or three-dimensional table of data representing statistical results across the parameter space. Inasmuch as the output of a parametric study in three dimensions can be a very large data set, visualization is a paramount means of discovering trends in the data (see figure). Data Table Plot View enables visualization of the data table created by Parametric Studies or by another data source: this module quickly generates a display of the data in the form of a rotatable three-dimensional-appearing plot, making it unnecessary to load the SOAP output data into a separate plotting program. The rotatable three-dimensionalappearing plot makes it easy to determine which points in the parameter space are most desirable. Both modules provide intuitive user interfaces for ease of use.

Source record↗
NASA NTRS2022

Peru Health and Air Quality II: Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention

Peru's Madre de Dios region is a hotspot for dengue fever and leishmaniasis due to its tropical Amazonian climate. Though treatable, these zoonotic diseases are debilitating for under-resourced communities whose already close proximity to mosquito and sandfly vectors continues to increase via rapid urbanization and deforestation. Peru’s Ministries of Health (MINSA) and Environment (MINAM) are working to better understand the environmental factors amplifying the risk of dengue fever and leishmaniasis transmission. The first term of this project classified the land use and land cover of Madre de Dios’ 11 districts for 2010, 2015, and 2020 and identified a correlation between both diseases and urbanization. Our team expanded this analysis by creating urban-forest edge maps and incorporating climatic and topographic variables with data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), and the Shuttle Radar Topography Mission (SRTM). We determined these variables’ impacts on disease incidence by assessing existing literature and running regression models. Dengue fever correlated with urban-forest edge, urban area, slope, temperature, and precipitation. Leishmaniasis primarily correlated with forest-edge area and elevation, but lacking additional statistical significance prevented further work, a decision supported by the literature. Thus, the risk matrix and risk map which we scripted in R to visualize the risk of disease posed to districts alongside health post locations addresses only dengue fever. The results and products will inform MINSA and MINAM in public health interventions, resource distribution, and policy initiatives.

Jennifer Rogers↗
NASA NTRSDate not supplied

Effects of Motion Fidelity on Pilot Performance Using Mixed-Reality Visuals

This paper investigates the effects of simulator motion fidelity on pilot performance using mixed-reality visuals. In NASA’s Vertical Motion Simulator, eleven pilots flew a lift-plus-cruise electric vertical takeoff and landing vehicle in a vertiport approach and landing task with four different motion conditions: no motion, small hexapod motion, large hexapod motion, and full motion. The simulator motion condition statistically significantly affected: 1) the severity of cybersickness, 2) motion ratings, 3) final approach and take-off boundary crossing groundspeed, 4) longitudinal touchdown point, and 5) battery time remaining after landing. The findings suggest that motion systems are essential in simulators with virtual- or mixed-reality visuals when it is required to have pilot flying performance similar to that in real flight.

mixed reality↗
DOE OSTI2023

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
NASA NTRSDate not supplied

Effects of Motion Fidelity on Pilot Performance Using Mixed-Reality Visuals

This paper investigates the effects of simulator motion fidelity on pilot performance using mixed-reality visuals. In NASA’s Vertical Motion Simulator, eleven pilots flew a lift-plus-cruise electric vertical takeoff and landing vehicle in a vertiport approach and landing task under four different motion conditions: no motion, small hexapod motion, large hexapod motion, and full motion. The simulator motion condition statistically significantly affected: 1) the severity of cybersickness, 2) motion ratings, 3) final approach and take-off boundary crossing groundspeeds, 4) longitudinal touchdown point, and 5) battery time remaining after landing. These results suggest that the use of motion systems in simulators with virtual- or mixed-reality visuals is warranted.

simulation↗
DOE OSTI2018

Exploration of thermal counterflow in He II using particle tracking velocimetry

Flow visualization using particle image velocimetry (PIV) and particularly particle tracking velocimetry (PTV) has been applied to thermal counterflow in He II for nearly two decades now, but the results remain difficult to interpret because tracer particle motion can be influenced by both the normal fluid and superfluid components of He II as well as the quantized vortex tangle. For instance, in one early experiment it was observed (using PTV) that tracer particles move at the normal fluid velocity v n , while in another it was observed (using PIV) that particles move at v n /2. Besides the different visualization methods, the range of applied heat flux investigated by these experiments differed by an order of magnitude. To resolve this apparent discrepancy and explore the statistics of particle motion in thermal counterflow, we apply the PTV method to a wide range of heat flux at a number of different fluid temperatures. In our analysis, we introduce a scheme for analyzing the velocity of particles presumably moving with the normal fluid separately from those presumably influenced by the quantized vortex tangle. Our results show that for lower heat flux there are two distinct peaks in the streamwise particle velocity probability density function (PDF), with one centered at the normal fluid velocity v n (named G2 for convenience) while the other is centered near v n /2 (G1). For higher heat flux there is a single peak centered near v n /2 (G3). Using our separation scheme, we show quantitatively that there is no size difference between the particles contributing to G1 and G2. We also show that nonclassical features of the transverse particle velocity PDF arise entirely from G1, while the corresponding PDF for G2 exhibits the classical Gaussian form. The G2 transverse velocity fluctuation, backed up by second sound attenuation in decaying counterflow, suggests that large-scale turbulence in the normal fluid is absent from the two-peak region. We offer a brief discussion of the physical mechanisms that may be responsible for our observations, revealing that G1 velocity fluctuations may be linked to fluctuations of quantized vortex line velocity, and suggest a number of numerical simulations that may reveal the underlying physics in detail.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

anomaly detection↗
DOE OSTI2014

SU-E-I-21: Deformation Mapping and Shape Prediction with 3D Tumor Volume Morphing

Purpose: To compare projection-based versus global correction that compensate for deadtime count loss in SPECT/CT images. Methods: SPECT/CT images of an IEC phantom (2.3GBq 99mTc) with ∼10% deadtime loss containing the 37mm (uptake 3), 28 and 22mm (uptake 6) spheres were acquired using a 2 detector SPECT/CT system with 64 projections/detector and 15 s/projection. The deadtime, Ti and the true count rate, Ni at each projection, i was calculated using the monitor-source method. Deadtime corrected SPECT were reconstructed twice: (1) with projections that were individually-corrected for deadtime-losses; and (2) with original projections with losses and then correcting the reconstructed SPECT images using a scaling factor equal to the inverse of the average fractional loss for 5 projections/detector. For both cases, the SPECT images were reconstructed using OSEM with attenuation and scatter corrections. The two SPECT datasets were assessed by comparing line profiles in xyplane and z-axis, evaluating the count recoveries, and comparing ROI statistics. Higher deadtime losses (up to 50%) were also simulated to the individually corrected projections by multiplying each projection i by exp(-a*Ni*Ti), where a is a scalar. Additionally, deadtime corrections in phantoms with different geometries and deadtime losses were also explored. The same two correction methods were carried for all these data sets. Results: Averaging the deadtime losses in 5 projections/detector suffices to recover >99% of the loss counts in most clinical cases. The line profiles (xyplane and z-axis) and the statistics in the ROIs drawn in the SPECT images corrected using both methods showed agreement within the statistical noise. The count-loss recoveries in the two methods also agree within >99%. Conclusion: The projection-based and the global correction yield visually indistinguishable SPECT images. The global correction based on sparse sampling of projections losses allows for accurate SPECT deadtime loss correction while keeping the study duration reasonable.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy

Purpose: To automatically identify and outline suspicious regions of recurrent or residual disease in the prostate bed using Dynamic Contrast Enhanced-MRI (DCE-MRI) in patients after prostatectomy. Methods: Twenty-two patients presenting for salvage radiotherapy and with identified Gross Tumor Volume (GTV) in the prostate bed were retrospectively analyzed. The MRI data consisted of Axial T2weighted-MRI (T2w) of the pelvis: resolution 1.25×1.25×2.5 mm; Field of View (FOV): 320×320 mm; slice thickness=2.5mm; 72 slices; and Dynamic Contrast Enhanced MRI (DCE-MRI)–12 series of T1w with identical spatial resolution to T2w and at 30–34s temporal resolution. Unsupervised pattern recognition was used to decompose the 4D DCE data as the product W.H of weights W of k patterns H. A well-perfused pattern Hwp was identified and the weight map Wwp associated to Hwp was used to delineate suspicious volumes. Threshold of Wwp set at mean(Wwp)+S*std(Wwp), S=1,1.5,2 and 2.5 defined four volumes labeled as DCE1.0 to DCE2.5. These volumes were displayed on T2w and, along with GTV, were correlated with the highest pre-treatment PSA values, and with pharmacokinetic analysis constants. Results: GTV was significantly correlated with DCE2.0(ρ= 0.60, p<0.003), and DCE 2.5 (ρ=0.58, p=0.004)). Significant correlation was found between highest pre-treatment PSA and GTV(ρ=0.42, p<0.049), DCE2.0(ρ= 0.52, p<0.012), and DCE 2.5 (ρ=0.67, p<<0.01)). Kruskal-Wallis analysis showed that Ktrans median value was statistically different between non-specific prostate bed tissue NSPBT and both GTV (p<<0.001) and DCE2.5 (p<<0.001), but while median Ve was statistically different between DCE2.5 and NSPBT (p=0.002), it was not statistically different between GTV and NSPBT (p=0.054), suggesting that automatic volumes capture more accurately the area of malignancy. Conclusion: Software developed for identification and visualization of suspicions regions in DCE-MRI from post-prostatectomy patients has been validated by PSA and pharmacokinetic constants analysis showing that it generates clinically relevant volumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Using Stage Level Microseismic Analysis to Gain Insight into Fracture Efficiency and Completion Effectiveness

Microseismic event density heat maps at sequential time intervals during the treatment of individual completion stages have been developed to better understand fracture behavior on a stage level. Animating these heat maps through time on a single stage provides fascinating visual insights into the dynamic sequencing of populations of events, event timing and their likely genesis, providing a new way of visualizing and interpreting microseismic data sets. This technique leverages heat-map benefits from URTEC 2674376, excluding microseismic event clusters that may obscure or skew statistical analysis of the event densities, thus allowing for more accurate assessment of geologic features, stimulated rock volume, near-field concentration of events due to varying cluster spacing, fracture efficiency and stress shadowing. Far-field microseismic events prior to ramp up of treatment pressure are clearly indicated from this technique that cannot be observed with standard heat maps with certain completion designs. Unambiguous changes in microseismic event character from these far-field pre-ramp up events to those near-field events occurring at the onset of pressure are also observed. Applying a stage by stage approach on packages of wells and different locations provides robust statistical conclusions pertaining to reservoir behavior during well completion.

Wang, Iris↗
NASA NTRS1974

The Princeton equipment on board

The Princeton telescope-spectrometer on the Copernicus satellite scans stellar spectra between 950 and 1450 A in second order with a resolution of about 0.05 A. The resolution in first order, between 1650 and 3000 A, is twice that in second order. The equipment may be used down to a nominal limit of 5th magnitude with the photometric precision in second order limited only by the statistics of photon counts. At 1100 A, a rate of 1,000 per 14 sec is obtained on an unreddened B 1 star with a visual magnitude of 5.0. In the first order, phototube noise from cosmic rays limits observations to stars brighter than visual magnitude 3.0 in general.

Drake, J. F.↗
NASA NTRS1986

Pilot use of simulator cues for autorotation landings

A ground-based simulator experiment was conducted to investigate the influence of simulator cue variations on helicopter autorotation landing-task performance. Using the NASA Ames Research Center Vertical Motion Simulator (VMS), variations were made in motion-system performance and selection of either longitudinal or lateral linear acceleration cues. Analysis of landing performance statistics and pilot commentary revealed that task performance changed with degraded motion cues. However, fixed base (no motion) could provide acceptable landing performance if adequate visual cues were present. Lontitudinal linear-acceleration cues assisted one pilot's control of the landing forward speed, while lateral linear-acceleration cues assisted in control of lateral drift or sideslip. Variations were also made in the content and details of visual landing scenes. Pilot commentary collected on the use of three landing scenes stressed the importance of receiving consistent, easily recognized, position, attitude, and speed cues from the visual scene. The importance of subtle aural cues to pilot acceptance of the simulation was also noted.

Decker, W. A.↗
NASA NTRS1999

Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

NASA Discipline Cardiopulmonary↗
DOE OSTI2016

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗