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NASA NTRS1973

On visual-vestibular interaction

Experimental evidence is presented regarding visual vestibular interaction, and the results of three studies on the subject are briefly noted. An attempt to put together some of these observations with elementary notions of a visual vestibular interaction program is shown in the form of a flow chart representation of a possible model. This is a nonlinear model in which visual and vestibular influences are linearly weighted when they are in relative agreement but switch to the more believable one when they are in disagreement. A solution to the human space orientation problem is depicted by a schema for optimal subjective orientation based on several sensory modalities.

Young, L. R.↗

NASA NTRS2011

Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Harshburger, Brian J.↗

NASA NTRS1996

A Rorschach Test for Visual Classification Strategies

Contemporary models of pattern, detection and discrimination often employ template matching, but there have been few direct tests of this proposition. Adopting a method developed by Ahumada, we have analyzed how human observers discriminate between two letters of the alphabet ('c' and 'x'). The stimulus consisted of a one degree tall letter plus a four degree field of static white noise, both displayed for 16 frames at a 67 Hz frame rate. Our font and display dimensions approximated those of Solomon and Pelli. The observer identified the letter presented. A QUEST staircase varied letter contrast to maintain a 75% correct rate. For each trial, we preserved the information required to reconstruct the noise field. Possible trial categories based on (signal, response) pairs are: (c,c), (c,x), (x,c), (x,x). Noise fields were averaged separately for each category, and a final classification image was obtained by averaging the four mean images after inverting the sign of categories in which x was the response. If the observer employs a template, it should be revealed in the classification image. The lowpass-filtered classification image derived from 2048 responses of one observer is shown here, along with the corresponding ideal template. An approximation to the ideal template can be seen appropriately located within the classification image. We have also simulated and will discuss the classification images expected from various discrimination models in this experimental context. The construction of classification images appears to be a powerful tool for studying classification strategies used by human observers. Like a Rorschach test, it surreptitiously discovers the inner desires of the visual system.

Watson, Andrew B.↗

NASA NTRS1981

Proposed three-phase modeling of Be stars from combined UV and visual observations

Far ultraviolet observations of the behavior of wind velocity and superionization values as a function of the phase of the (Be, B shell, B normal) pattern established by visual observations for gamma Cas and 59 Cyg are translated into a crude atmospheric model for the Be phase and several kinds of mass flux variability across the three phases.

Doazan, V.↗

DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗

DOE OSTI2021

TX$^2$: Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration (Martindale & Stewart, 2021), or TX 2 software package, is a library designed for artificial intelligence researchers to better understand the performance of transformer models (Vaswani et al., 2017) used for sequence classification. The tool is capable of integrating with a trained transformer model and a dataset split into training and testing populations to produce an ipywidget (Project Jupyter Contributors, 2021) dashboard with a number of visualizations to understand model performance with an emphasis on explainability and interpretability. The TX 2 package is primarily intended to integrate into a workflow centered around Jupyter Notebooks (Kluyver et al., 2016), and currently assumes the use of PyTorch (Paszke et al., 2019) and Hugging Face transformers library (Wolf et al., 2020). The dashboard includes visualization and data exploration features to aid researchers, including an interactive UMAP embedding graph (McInnes et al., 2018) to understand classification clusters, a word salience map that can be updated as researchers alter textual entries in near real time, a set of tools to understand word frequency and importance based on the clusters in the UMAP embedding graph, and a set of traditional confusion matrix analysis tools.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Surface Flow Visualization of the High-Lift Common Research Model

A 10% scale version of the High-Lift Common Research Model (CRM-HL) was tested in the NASA Langley 14- by 22-Foot Subsonic Tunnel (14x22) in support of the NASA Advanced Air Transport Technology (AATT) Project. The CRM-HL experiment included various configurations such as conventional and simple-hinged flaps, with and without engine nacelle/pylon, with and without nacelle chine, different Active Flow Control (AFC) methods (sweeping jets, alternating pulsed jets, and preconditioned boundary layer blowing), and their various parameters. This particular study is focused on the surface flow visualization of the conventional CRM-HL model at landing configuration. The conventional CRM-HL model with the single-slotted Fowler flap system serves as a baseline for the AFC-enabled simplified high-lift configuration as well as a high-lift technology development platform due to its publicly open geometry. Surface flow visualizations were performed using fluorescent minitufts, which were found to be nonintrusive to the aerodynamic performance. Tuft flow visualizations are supplemented with the relevant pressure and force measurements in order to understand the flow characteristics developed on the conventional CRM- HL model. In addition, three dimensional, unsteady, compressible Computational Fluid Dynamic (CFD) simulations were performed for selective cases. The surface streamlines and transverse velocity fluctuations obtained by the CFD simulations are qualitatively compared to the tuft direction and tuft unsteadiness, respectively. Force measurements of the CRM-HL model show performance degradation at higher angles of attack. Surface flow visualizations revealed the performance loss due to the nacelle/pylon wake that grows with angle of attack and eventually promotes flow separation over the inboard wing. This performance loss was successfully recovered by placing a chine on the engine nacelle.

Koklu, Mehti↗

NASA NTRS2009

The Mission Planning Lab: A Visualization and Analysis Tool

Simulation and visualization are powerful decision making tools that are time-saving and cost-effective. Space missions pose testing and e valuation challenges that can be overcome through modeling, simulatio n, and visualization of mission parameters. The National Aeronautics and Space Administration?s (NASA) Wallops Flight Facility (WFF) capi talizes on the benefits of modeling, simulation, and visualization to ols through a project initiative called The Mission Planning Lab (MPL ).

Daugherty, Sarah C.↗

NASA NTRS2010

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Putman, William M.↗

DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1986

Calculated occultation profiles of Io and the hot spots

Occultations of Io by other Galilean satellites in 1985 provide a means to locate volcanic hot spots and to model their temperatures. The expected time variations in the integral reflected and emitted radiation of the occultations are computed as a function of wavelength (visual to 8.7 microns). The best current ephemerides were used to calculate the geometry of each event as viewed from earth. Visual reflectances were modeled from global mosaics of Io. Thermal emission from the hot spots was calculated from Voyager 1 IRIS observations and, for regions unobserved by IRIS, from a model based on the distribution of low-albedo features. The occultations may help determine (1) the location and temperature distribution of Loki; (2) the source(s) of excess emission in the region from long 50 deg to 200 deg and (3) the distribution of small, high-temperature sources.

Mcewen, A. S.↗

NASA NTRS2018

Climate Lecture 20: The Educational Global Climate Model (EdGCM)

Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.

Chandler, Mark A.↗

DOE OSTI2020

Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytics goals in this domain.

Kandakatla, Akshith R.↗

NASA NTRS2008

From Informal Safety-Critical Requirements to Property-Driven Formal Validation

Most of the efforts in formal methods have historically been devoted to comparing a design against a set of requirements. The validation of the requirements themselves, however, has often been disregarded, and it can be considered a largely open problem, which poses several challenges. The first challenge is given by the fact that requirements are often written in natural language, and may thus contain a high degree of ambiguity. Despite the progresses in Natural Language Processing techniques, the task of understanding a set of requirements cannot be automatized, and must be carried out by domain experts, who are typically not familiar with formal languages. Furthermore, in order to retain a direct connection with the informal requirements, the formalization cannot follow standard model-based approaches. The second challenge lies in the formal validation of requirements. On one hand, it is not even clear which are the correctness criteria or the high-level properties that the requirements must fulfill. On the other hand, the expressivity of the language used in the formalization may go beyond the theoretical and/or practical capacity of state-of-the-art formal verification. In order to solve these issues, we propose a new methodology that comprises of a chain of steps, each supported by a specific tool. The main steps are the following. First, the informal requirements are split into basic fragments, which are classified into categories, and dependency and generalization relationships among them are identified. Second, the fragments are modeled using a visual language such as UML. The UML diagrams are both syntactically restricted (in order to guarantee a formal semantics), and enriched with a highly controlled natural language (to allow for modeling static and temporal constraints). Third, an automatic formal analysis phase iterates over the modeled requirements, by combining several, complementary techniques: checking consistency; verifying whether the requirements entail some desirable properties; verify whether the requirements are consistent with selected scenarios; diagnosing inconsistencies by identifying inconsistent cores; identifying vacuous requirements; constructing multiple explanations by enabling the fault-tree analysis related to particular fault models; verifying whether the specification is realizable.

Cimatti, Alessandro↗

NASA NTRS1991

Update of a nickel-hydrogen cycle life model

The topics are presented in viewgraph form and include the following: a cell capacity diagram, life predictions; compilation of Ni-H2 life test data; Fritts' electrode strain; Lim's electrode bending; Davolio's flexural Young's modulus; behavior of sintered nickel electrode; visualization of modified model; and nickel hydrogen technology status.

Thaller, Lawrence H.↗

DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2008

Fluorescence Imaging and Streamline Visualization of Hypersonic Flow over Rapid Prototype Wind-Tunnel Models

Reentry models for use in hypersonic wind tunnel tests were fabricated using a stereolithography apparatus. These models were produced in one day or less, which is a significant time savings compared to the manufacture of ceramic or metal models. The models were tested in the NASA Langley Research Center 31-Inch Mach 10 Air Tunnel. Only a few of the models survived repeated tests in the tunnel, and several failure modes of the models were identified. Planar laser-induced fluorescence (PLIF) of nitric oxide (NO) was used to visualize the flowfields in the wakes of these models. Pure NO was either seeded through tubes plumbed into the model or via a tube attached to the strut holding the model, which provided localized addition of NO into the model s wake through a porous metal cylinder attached to the end of the tube. Models included several 2- inch diameter Inflatable Reentry Vehicle Experiment (IRVE) models and 5-inch diameter Crew Exploration Vehicle (CEV) models. Various model configurations and NO seeding methods were used, including a new streamwise visualization method based on PLIF. Virtual Diagnostics Interface (ViDI) technology, developed at NASA Langley Research Center, was used to visualize the data sets in post processing. The use of calibration "dotcards" was investigated to correct for camera perspective and lens distortions in the PLIF images.

Danehy, Paul M.↗

DOE OSTI2020

Opportunities for Cost Savings with In-Transit Visualization

We analyze the opportunities for in-transit visualization to provide cost savings compared to in-line visualization. We begin by developing a cost model that includes factors related to both in-line and in-transit which allows comparisons to be made between the two methods. We then run a series of studies to create a corpus of data for our model. We run two different visualization algorithms, one that is computation heavy and one that is communication heavy with concurrencies up to 32, 768 cores. Our primary results are in exploring the cost model within the context of our corpus. Our findings show that in-transit consistently achieves significant cost efficiencies by running visualization algorithms at lower concurrency, and that in many cases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.

Kress, James↗