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Requirements for and definitions of a cost effective orbital facility concept, capable of supporting extended manned operations in earth orbit beyond those visualized for the 7 to 30 day shuttle/spacelab system, were studied. Data are given on requirements derivation, concepts identification, systems analysis and definition, and programmatics.
Parametric studies to identify a vortex generator were completed. Data acquisition in the first chosen configuration, in which a longitudinal vortex pair generated by an isolated delta wing starts to merge with a turbulent boundary layer on a flat plate fairly close to the leading edge is nearly completed. Work on a delta-wing/flat-plate combination, consisting of a flow visualization and hot wire measurements taken with a computer controlled traverse gear and data logging system were completed. Data taking and analysis have continued, and sample results for another cross stream plane are presented. Available data include all mean velocity components, second order mean products of turbulent fluctuations, and third order mean products. Implementation of a faster data logging system was accomplished.
Viewgraphs on grid analysis and display system (GrADS): a practical tool for earth science visualization are presented. Topics covered include: GrADS design goals; data sets; and temperature profiles.
An initial assessment of a proposed High-Speed Civil Transport (HSCT) was conducted in the fall of 1995 at the NASA Langley Research Center. This configuration, known as the Industry Reference-H (Ref.-H), was designed by the Boeing Aircraft Company as part of their work in the High Speed Research program. It included a conventional tail, a cranked-arrow wing, four mixed-flow turbofan engines, and capacity for transporting approximately 300 passengers. The purpose of this assessment was to evaluate and quantify operational aspects of the Reference-H configuration from a pilot's perspective with the additional goal of identifying design strengths as well as any potential configuration deficiencies. This study was aimed at evaluating the Ref.-H configuration at many points of the aircraft's envelope to determine the suitability of the vehicle to accomplish typical mission profiles as well as emergency or envelope-limit conditions. Pilot-provided Cooper-Harper ratings and comments constituted the primary vehicle evaluation metric. The analysis included simulated real-time piloted evaluations, performed in a 6 degree of freedom motion base NASA Langley Visual-Motion Simulator, combined with extensive bath analysis. The assessment was performed using the third major release of the simulation data base (known as Ref.-H cycle 2B).
A previous study reported that inadequate weigh outs of suited subjects contribute to fatigue and the risk of injury during training in the Neutral Buoyancy Laboratory (NBL). Another study suggested that shoulder injuries observed in suited subjects who train in the NBL may be attributed to excessive righting moments caused by a non-optimal weigh out. The purpose of this study was to develop a mathematical model to predict and maintain the neutral buoyancy of suited subjects during training operations at the NBL. Due to time constraints, one certified NBL support diver served as a subject (height: 66.54 in; weight: 182 lbs) for this study and only one complete test was conducted. The study was divided into two runs for which the first run required the NBL divers to perform a weigh out similar to a suited astronaut on a scuba diver wearing a mock Portable Life Support System and a Displays and Control Module. For the second run, the same subject and equipment were weighed out according to the mathematical model. The objective of each run was to achieve a neutrally buoyant subject floating 450 to the pool floor. Motion data was collected using two underwater cameras and analyzed using Dartfish video analysis software while force and moment data were recorded using an AMTI force plate. The results from the NBL divers visual run indicate that the subject was floating at an angle of 29.50 while the resultant force and moment data were 1.139 lb and 1.125 ft-lb respectively. The mathematical model s weigh out resulted in the subject floating at an angle of 37.40 and a resultant force of 0.765 lb and resultant moment of 1.248 ft-lb. The mathematical model was better able to orient the subject and reduce resultant moment and force as compared to the NBL divers.
With the tragic passing this year of Gregory Leptoukh, the Earth and Space Sciences community lost a tireless participant in--and advocate for--science informatics. Throughout his career at NASA, Dr. Leptoukh established a theme of bridging the gulf between the informatics and science communities. Nowhere is this more evident than his leadership in the development of Giovanni (GES DISC Interactive Online Visualization ANd aNalysis Infrastructure). Giovanni is an online tool that serves to hide the often-complex technical details of data format and structure, making science data easier to explore and use by Earth scientists. To date Giovanni has been acknowledged as a contributor in 500-odd scientific articles. In recent years, Leptoukh concentrated his efforts on multi-sensor data inter-comparison, merging and fusion. This work exposed several challenges at the intersection of data and science. One of these was the ease with which a naive user might generate spurious comparisons, a potential hazard that was the genesis of the Multi-sensor Data Synergy Advisor (MDSA). The MDSA uses semantic ontologies and inference rules to organize knowledge about dataset quality and other salient characteristics in order to advise users on potential caveats for comparing or merging two datasets. Recently, Leptoukh also led the development of AeroStat, an online Giovanni instance to investigate aerosols via statistics from station and satellite comparisons and merged maps of data from more than one instrument. Aerostat offers a neural net based bias adjustment to harmonize the data by removing systematic offsets between datasets before merging. These examples exhibit Leptoukh's talent for adopting advanced computer technologies in the service of making science data more accessible to researchers. In this, he set an example that is at once both vital and challenging for the ESSI community to emulate.
Human behavior often consists of a series of distinct activities, each characterized by a unique signature of visual behavior. This is true even in a restricted domain, such as piloting an aircraft, where patterns of visual signatures might represent activities like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these activities based on their behavioral signatures. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as τ. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration τ, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to counts by multiplying by τ divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We have investigated the method using a set of 10 synthetic "activities," that sample 4 AOIs. Four of these activities visit 3 of the 4 AOIs, with equal probability; as there are four different ways to leave-one- out, there are four such activities. Similarly, there are six different activities that leave-two-out. Sequences of simulated behavior were generated by running each activity for 40 seconds, in sequence, for a total of 6.7 minutes. The figure to the right shows the matrix of chi-square statistics, using a value of 2.8 seconds for τ, corresponding to 10 fixations. Low values (dark) indicate poor evidence for activity differences, while high values (bright) indicate strong evidence. The dark squares along the main diagonal each correspond to the forty second intervals in which the activity was held constant; the 4x4 block at the lower left corresponds to the four leave-one-out activities, while the 6x6 block in the upper right corresponds to the leave-two-out activities. (The anti-diagonal pattern of white squares indicates those activity pairs that share no AOIs.) The chi-square values can be binarized by choosing a particular significance level; we are interested in grouping bins that represent the same activity, effectively accepting the null hypothesis. Therefore, we may adopt a relatively lax criterion; for example, choosing a p-value of 0.2 means that two behaviors that have only a 1-in-5 chance of being produced by a single activity might nevertheless be clustered together. We have explored several methods to perform clustering on the data and solving for the activity probabilities. Greedy methods begin by selecting the time bin that is similar to the most (or least) other bins, and then forming a cluster from it and all other non-discriminable bins. These methods show mediocre performance, as they do not take into account temporal contiguity. Preliminary results indicate that methods that "grow" clusters in time from seed points perform better.
Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a piloting an aircraft, where activities with distinct visual signatures might be things like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as delta. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration delta, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to integer counts by multiplying by delta divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. The method has been applied to approximately 100 hours of eye movement data collected from pilots in a high-fidelity B747 flight simulator, and the results have been compared to synthetic data in which the each activity is represented as first-order Markov process with random probabilities assigned to the AOIs. Randomly-generated synthetic activities can require thousands of fixations to be discriminated with statistical significance, while the human data can be clustered using averaging windows of some 10's of seconds, suggesting that the actual activities are much more narrowly focused than random Markov models.
Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.
Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.
The NONA neutrino experiment based in Fermilab is designed to measure ν µ →ν e neutrino oscillations. This experiment will give us insight into the properties of massive neutrinos. Neutral current (NC) ν µ,e neutral pion production events can mimic the ν µ →ν e oscillation signal and therefore are an important background for NONA to understand. Neutral pions decay into two photons which can fake a single electron shower (νe appearance signal) in two ways: either the 2 photons can merge together or one of them may escape detection. In order to constrain this background, NONA utilizes the Near Detector to measure neutral current π 0 neutrino interactions. In this analysis, neutrino-Nucleus (ν µ →N) NC π 0 interactions with total pion energy greater then 0.3 GeV are studied by selecting two prong events with two final state photons as determined by prong based Convolutional Visual Networks (CVN). The analysis is performed on 3.54x10 21 Protons On Target (POT) of NONA Near Detector simulated data and compared to 8.09x10 20 POT of data. Optimization of the selection based on fractional cross-section uncertainty and an initial energy resolution study of the final sample are presented. The final 2 prong selection using prong based CVN gave a purity of 74%, selection efficiency of ~ 1.8%, and an expected (NC) ν µ,e neutral pion cross section of ~ 15.2%.
Echo™ is a MATLAB-based software package designed for robust and scalable analysis of complex data workflows. An alternative to tedious, error-prone conventional processes, Echo is based on three transformative principles for data analysis: self-describing data, name-based indexing, and dynamic resource allocation. The software takes an object-oriented approach to data analysis, intimately connecting measurement data with associated metadata. Echo operations in an analysis workflow automatically track and merge metadata and computation parameters to provide a complete history of the process used to generate final results, while automated figure and report generation tools eliminate the potential to mislabel those results. History reporting and visualization methods provide straightforward auditability of analysis processes. Furthermore, name-based indexing on metadata greatly improves code readability for analyst collaboration and reduces opportunities for errors to occur. Echo efficiently manages large data sets using a framework that seamlessly allocates resources such that only the necessary computations to produce a given result are executed. Echo provides a versatile and extensible framework, allowing advanced users to add their own tools and data classes tailored to their own specific needs. Applying these transformative principles and powerful features, Echo greatly improves analyst efficiency and quality of results in many application areas.
Purpose: Dynamic CT ventilation imaging (CT-VI) visualizes air volume changes in the lung by evaluating breathing-induced lung motion using deformable image registration (DIR). Dynamic CT-VI could enable functionally adaptive lung cancer radiation therapy, but its sensitivity to DIR parameters poses challenges for validation. We hypothesize that a direct metric using CT parameters derived from Hounsfield units (HU) alone can provide similar ventilation images without DIR. We compare the accuracy of Direct and Dynamic CT-VIs versus positron emission tomography (PET) images of inhaled {sup 68}Ga-labelled nanoparticles (‘Galligas’). Methods: 25 patients with lung cancer underwent Galligas 4D-PET/CT scans prior to radiation therapy. For each patient we produced three CT- VIs. (i) Our novel method, Direct CT-VI, models blood-gas exchange as the product of air and tissue density at each lung voxel based on time-averaged 4D-CT HU values. Dynamic CT-VIs were produced by evaluating: (ii) regional HU changes, and (iii) regional volume changes between the exhale and inhale 4D-CT phase images using a validated B-spline DIR method. We assessed the accuracy of each CT-VI by computing the voxel-wise Spearman correlation with free-breathing Galligas PET, and also performed a visual analysis. Results: Surprisingly, Direct CT-VIs exhibited better global correlation with Galligas PET than either of the dynamic CT-VIs. The (mean ± SD) correlations were (0.55 ± 0.16), (0.41 ± 0.22) and (0.29 ± 0.27) for Direct, Dynamic HU-based and Dynamic volume-based CT-VIs respectively. Visual comparison of Direct CT-VI to PET demonstrated similarity for emphysema defects and ventral-to-dorsal gradients, but inability to identify decreased ventilation distal to tumor-obstruction. Conclusion: Our data supports the hypothesis that Direct CT-VIs are as accurate as Dynamic CT-VIs in terms of global correlation with Galligas PET. Visual analysis, however, demonstrated that different CT-VI algorithms might have varying accuracy depending on the underlying cause of ventilation abnormality. This research was supported by a National Health and Medical Research Council (NHMRC) Australia Fellowship, an Cancer Institute New South Wales Early Career Fellowship 13-ECF-1/15 and NHMRC scholarship APP1038399. No commercial funding was received for this work.
Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.
The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.
Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.
Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.
Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.