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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Holodeck Testbed Project

The main objective of the Holodeck Testbed is to create a cost effective, realistic, and highly immersive environment that can be used to train astronauts, carry out engineering analysis, develop procedures, and support various operations tasks. Currently, the Holodeck testbed allows to step into a simulated ISS (International Space Station) and interact with objects; as well as, perform Extra Vehicular Activities (EVA) on the surface of the Moon or Mars. The Holodeck Testbed is using the products being developed in the Hybrid Reality Lab (HRL). The HRL is combining technologies related to merging physical models with photo-realistic visuals to create a realistic and highly immersive environment. The lab also investigates technologies and concepts that are needed to allow it to be integrated with other testbeds; such as, the gravity offload capability provided by the Active Response Gravity Offload System (ARGOS). My main two duties were to develop and animate models for use in the HRL environments and work on a new way to interface with computers using Brain Computer Interface (BCI) technology. On my first task, I was able to create precise computer virtual tool models (accurate down to the thousandths or hundredths of an inch). To make these tools even more realistic, I produced animations for these tools so they would have the same mechanical features as the tools in real life. The computer models were also used to create 3D printed replicas that will be outfitted with tracking sensors. The sensor will allow the 3D printed models to align precisely with the computer models in the physical world and provide people with haptic/tactile feedback while wearing a VR (Virtual Reality) headset and interacting with the tools. Getting close to the end of my internship the lab bought a professional grade 3D Scanner. With this, I was able to replicate more intricate tools at a much more time-effective rate. The second task was to investigate the use of BCI to control objects inside the hybrid reality ISS environment. This task looked at using an Electroencephalogram (EEG) headset to collect brain state data that could be mapped to commands that a computer could execute. On this Task, I had a setback with the hardware, which stopped working and was returned to the vendor for repair. However, I was still able to collect some data, was able to process it, and started to create correlation algorithms between the electrical patterns in the brain and the commands we wanted the computer to carry out. I also carried out a test to investigate the comfort of the headset if it is worn for a long time. The knowledge gained will benefit me in my future career. I learned how to use various modeling and programming tools that included Blender, Maya, Substance Painter, Artec Studio, Github, and Unreal Engine 4. I learned how to use a professional grade 3D scanner and 3D printer. On the BCI Project I learned about data mining and how to create correlation algorithms. I also supported various demos including a live demo of the hybrid reality lab capabilities at ComicPalooza. This internship has given me a good look into engineering at NASA. I developed a more thorough understanding of engineering and my overall confidence has grown. I have also realized that any problem can be fixed, if you try hard enough, and as an engineer it is your job to not only fix problems but to embrace coming up with solutions to those problems.

Arias, Adriel↗
NASA NTRS2004

Surface Modeling and Grid Generation for Iced Airfoils (SmaggIce)

Many of the troubles associated with problem solving are alleviated when there is a model that can be used to represent the problem. Through the Advanced Graphics and Visualization (G-VIS) Laboratory and other facilities located within the Research Analysis Center, the Computer Services Division (CSD) is able to develop and maintain programs and software that allow for the modeling of various situations. For example, the Icing Research Branch is devoted to investigating the effect of ice that forms on the wings and other airfoils of airplanes while in flight. While running tests that physically generate ice and wind on airfoils within the laboratories and wind tunnels on site are done, it would be beneficial if most of the preliminary work could be done outside of the lab. Therefore, individuals from within CSD have collaborated with Icing Research in order to create SmaggIce. This software allows users to create ice patterns on clean airfoils or open files containing a variety of icing situations, manipulate and measure these forms, generate, divide, and merge grids around these elements for more explicit analysis, and specify and rediscretize subcurves. With the projected completion date of Summer 2005, the majority of the focus of the Smagglce team is user-functionality and error handling. My primary responsibility is to test the Graphical User Interface (GUI) in SmaggIce in order to ensure the usability and verify the expected results of the events (buttons, menus, etc.) within the program. However, there is no standardized, systematic way in which to test all the possible combinations or permutations of events, not to mention unsolicited events such as errors. Moreover, scripting tests, if not done properly and with a view towards inevitable revision, can result in more apparent errors within the software and in effect become useless whenever the developers of the program make a slight change in the way a specific process is executed. My task therefore requires a brief yet intense study into GUI coverage criteria and creating algorithms for GUI implementation. Nevertheless, there are still heavily graphical features of SmaggIceSmaggIce that must be either corrected or redesigned before its release. A particular feature of SmaggIce is the ability to smooth out curves created by control points that form an arbitrary shape into something more acquiescent to gridding (while maintaining the integrity of the data). This is done by a mathematical model known as Non-Uniform Rational B-Spline (NURBS) curves. Existing NURBS code is written in FORTRAN-77 with static arrays for holding information. My new assignment is to allow for dynamic memory allocation within the code and to make it possible for the developers to call out functions from the NURBS code using C.

Hammond, Brandy M.↗
NASA NTRS2010

NASA SPoRT GOES-R Proving Ground Activities

The NASA Short-term Prediction Research and Transition (SPoRT) program is a partner with the GOES-R Proving Ground (PG) helping prepare forecasters understand the unique products to come from the GOES-R instrument suite. SPoRT is working collaboratively with other members of the GOES-R PG team and Algorithm Working Group (AWG) scientists to develop and disseminate a suite of proxy products that address specific forecast problems for the WFOs, Regional and National Support Centers, and other NOAA users. These products draw on SPoRT s expertise with the transition and evaluation of products into operations from the MODIS instrument and the North Alabama Lightning Mapping Array (NALMA). The MODIS instrument serves as an excellent proxy for the Advanced Baseline Imager (ABI) that will be aboard GOES-R. SPoRT has transitioned and evaluated several multi-channel MODIS products. The true and false color products are being used in natural hazard detection by several SPoRT partners to provide better observation of land features, such as fires, smoke plumes, and snow cover. Additionally, many of SPoRT s partners are coastal offices and already benefit from the MODIS sea surface temperature composite. This, along with other surface feature observations will be developed into ABI proxy products for diagnostic use in the forecast process as well as assimilation into forecast models. In addition to the MODIS instrument, the NALMA has proven very valuable to WFOs with access to these total lightning data. These data provide situational awareness and enhanced warning decision making to improve lead times for severe thunderstorm and tornado warnings. One effort by SPoRT scientists includes a lightning threat product to create short-term model forecasts of lightning activity. Additionally, SPoRT is working with the AWG to create GLM proxy data from several of the ground based total lightning networks, such as the NALMA. The evaluation will focus on the vastly improved spatial coverage of the GLM, but with the trade-off of lower resolution compared to the NALMA. In addition to the above tasks, SPoRT will make these data available in the NWS next generation display software, AWIPS II. This has already been successfully completed for the two basic GLM proxies. SPoRT will use these products to train forecasters on the capabilities of GOES-R and foster feedback to develop additional products, visualizations, and requirements beneficial to end users needs. These developments and feedback will be made available to the GOES-R Proving Ground for the upcoming 2010 Spring Program in Norman, Oklahoma.

Stano, Geoffrey T.↗
NASA NTRS1997

STRIPE: Remote Driving Using Limited Image Data

Driving a vehicle, either directly or remotely, is an inherently visual task. When heavy fog limits visibility, we reduce our car's speed to a slow crawl, even along very familiar roads. In teleoperation systems, an operator's view is limited to images provided by one or more cameras mounted on the remote vehicle. Traditional methods of vehicle teleoperation require that a real time stream of images is transmitted from the vehicle camera to the operator control station, and the operator steers the vehicle accordingly. For this type of teleoperation, the transmission link between the vehicle and operator workstation must be very high bandwidth (because of the high volume of images required) and very low latency (because delayed images can cause operators to steer incorrectly). In many situations, such a high-bandwidth, low-latency communication link is unavailable or even technically impossible to provide. Supervised TeleRobotics using Incremental Polyhedral Earth geometry, or STRIPE, is a teleoperation system for a robot vehicle that allows a human operator to accurately control the remote vehicle across very low bandwidth communication links, and communication links with large delays. In STRIPE, a single image from a camera mounted on the vehicle is transmitted to the operator workstation. The operator uses a mouse to pick a series of 'waypoints' in the image that define a path that the vehicle should follow. These 2D waypoints are then transmitted back to the vehicle, where they are used to compute the appropriate steering commands while the next image is being transmitted. STRIPE requires no advance knowledge of the terrain to be traversed, and can be used by novice operators with only minimal training. STRIPE is a unique combination of computer and human control. The computer must determine the 3D world path designated by the 2D waypoints and then accurately control the vehicle over rugged terrain. The human issues involve accurate path selection, and the prevention of disorientation, a common problem across all types of teleoperation systems. STRIPE is the only semi-autonomous teleoperation system that can accurately follow paths designated in monocular images on varying terrain. The thesis describes the STRIPE algorithm for tracking points using the incremental geometry model, insight into the design and redesign of the interface, an analysis of the effects of potential errors, details of the user studies, and hints on how to improve both the algorithm and interface for future designs.

REMOTE CONTROL↗
NASA NTRS1997

Object detection in natural backgrounds predicted by discrimination performance and models

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

NASA Center ARC↗
NASA NTRS2015

Use of Generalized Fluid System Simulation Program (GFSSP) for Teaching and Performing Senior Design Projects at the Educational Institutions

This paper describes the experience of the authors in using the Generalized Fluid System Simulation Program (GFSSP) in teaching Design of Thermal Systems class at University of Alabama in Huntsville. GFSSP is a finite volume based thermo-fluid system network analysis code, developed at NASA/Marshall Space Flight Center, and is extensively used in NASA, Department of Defense, and aerospace industries for propulsion system design, analysis, and performance evaluation. The educational version of GFSSP is freely available to all US higher education institutions. The main purpose of the paper is to illustrate the utilization of this user-friendly code for the thermal systems design and fluid engineering courses and to encourage the instructors to utilize the code for the class assignments as well as senior design projects. The need for a generalized computer program for thermofluid analysis in a flow network has been felt for a long time in aerospace industries. Designers of thermofluid systems often need to know pressures, temperatures, flow rates, concentrations, and heat transfer rates at different parts of a flow circuit for steady state or transient conditions. Such applications occur in propulsion systems for tank pressurization, internal flow analysis of rocket engine turbopumps, chilldown of cryogenic tanks and transfer lines, and many other applications of gas-liquid systems involving fluid transients and conjugate heat and mass transfer. Computer resource requirements to perform time-dependent, three-dimensional Navier-Stokes computational fluid dynamic (CFD) analysis of such systems are prohibitive and therefore are not practical. Available commercial codes are generally suitable for steady state, single-phase incompressible flow. Because of the proprietary nature of such codes, it is not possible to extend their capability to satisfy the above-mentioned needs. Therefore, the Generalized Fluid System Simulation Program (GFSSP1) has been developed at NASA Marshall Space Flight Center (MSFC) as a general fluid flow system solver capable of handling phase changes, compressibility, mixture thermodynamics and transient operations. It also includes the capability to model external body forces such as gravity and centrifugal effects in a complex flow network. The objectives of GFSSP development are: a) to develop a robust and efficient numerical algorithm to solve a system of equations describing a flow network containing phase changes, mixing, and rotation; and b) to implement the algorithm in a structured, easy-to-use computer program. The analysis of thermofluid dynamics in a complex network requires resolution of the system into fluid nodes and branches, and solid nodes and conductors as shown in Figure 1. Figure 1 shows a schematic and GFSSP flow circuit of a counter-flow heat exchanger. Hot nitrogen gas is flowing through a pipe, colder nitrogen is flowing counter to the hot stream in the annulus pipe and heat transfer occurs through metal tubes. The problem considered is to calculate flowrates and temperature distributions in both streams. GFSSP has a unique data structure, as shown in Figure 2, that allows constructing all possible arrangements of a flow network with no limit on the number of elements. The elements of a flow network are boundary nodes where pressure and temperature are specified, internal nodes where pressure and temperature are calculated, and branches where flowrates are calculated. For conjugate heat transfer problems, there are three additional elements: solid node, ambient node, and conductor. The solid and fluid nodes are connected with solid-fluid conductors. GFSSP solves the conservation equations of mass and energy, and equation of state in internal nodes to calculate pressure, temperature and resident mass. The momentum conservation equation is solved in branches to calculate flowrate. It also solves for energy conservation equations to calculate temperatures of solid nodes. The equations are coupled and nonlinear; therefore, they are solved by an iterative numerical scheme. GFSSP employs a unique numerical scheme known as simultaneous adjustment with successive substitution (SASS), which is a combination of Newton-Raphson and successive substitution methods. The mass and momentum conservation equations and the equation of state are solved by the Newton-Raphson method while the conservation of energy and species are solved by the successive substitution method. GFSSP is linked with two thermodynamic property programs, GASP2 and WASP3 and GASPAK4, that provide thermodynamic and thermophysical properties of selected fluids. Both programs cover a range of pressure and temperature that allows fluid properties to be evaluated for liquid, liquid-vapor (saturation), and vapor region. GASP and WASP provide properties of 12 fluids. GASPAK includes a library of 36 fluids. GFSSP has three major parts. The first part is the graphical user interface (GUI), visual thermofluid analyzer of systems and components (VTASC). VTASC allows users to create a flow circuit by a 'point and click' paradigm. It creates the GFSSP input file after the completion of the model building process. GFSSP's GUI provides the users a platform to build and run their models. It also allows post-processing of results. The network flow circuit is first built using three basic elements: boundary node, internal node, and branch.

Majumdar, A. K.↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS2003

Three-dimensional ocular kinematics during eccentric rotations: evidence for functional rather than mechanical constraints

Previous studies have reported that the translational vestibuloocular reflex (TVOR) follows a three-dimensional (3D) kinematic behavior that is more similar to visually guided eye movements, like pursuit, rather than the rotational VOR (RVOR). Accordingly, TVOR rotation axes tilted with eye position toward an eye-fixed reference frame rather than staying relatively fixed in the head like in the RVOR. This difference arises because, contrary to the RVOR where peripheral image stability is functionally important, the TVOR like pursuit and saccades cares to stabilize images on the fovea. During most natural head and body movements, both VORs are simultaneously activated. In the present study, we have investigated in rhesus monkeys the 3D kinematics of the combined VOR during yaw rotation about eccentric axes. The experiments were motivated by and quantitatively compared with the predictions of two distinct hypotheses. According to the first (fixed-rule) hypothesis, an eye-position-dependent torsion is computed downstream of a site for RVOR/TVOR convergence, and the combined VOR axis would tilt through an angle that is proportional to gaze angle and independent of the relative RVOR/TVOR contributions to the total eye movement. This hypothesis would be consistent with the recently postulated mechanical constraints imposed by extraocular muscle pulleys. According to the second (image-stabilization) hypothesis, an eye-position-dependent torsion is computed separately for the RVOR and the TVOR components, implying a processing that takes place upstream of a site for RVOR/TVOR convergence. The latter hypothesis is based on the functional requirement that the 3D kinematics of the combined VOR should be governed by the need to keep images stable on the fovea with slip on the peripheral retina being dependent on the different functional goals of the two VORs. In contrast to the fixed-rule hypothesis, the data demonstrated a variable eye-position-dependent torsion for the combined VOR that was different for synergistic versus antagonistic RVOR/TVOR interactions. Furthermore, not only were the eye-velocity tilt slopes of the combined VOR as much as 10 times larger than what would be expected based on extraocular muscle pulley location, but also eye velocity during antagonistic RVOR/TVOR combinations often tilted opposite to gaze. These results are qualitatively and quantitatively consistent with the image-stabilization hypothesis, suggesting that the eye-position-dependent torsion is computed separately for the RVOR and the TVOR and that the 3D kinematics of the combined VOR are dependent on functional rather than mechanical constraints.

Non-NASA Center↗