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NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗
NASA NTRS2014

3D Printing In Zero-G ISS Technology Demonstration

The National Aeronautics and Space Administration (NASA) has a long term strategy to fabricate components and equipment on‐demand for manned missions to the Moon, Mars, and beyond. To support this strategy, NASA and Made in Space, Inc. are developing the 3D Printing In Zero‐G payload as a Technology Demonstration for the International Space Station (ISS). The 3D Printing In Zero‐G experiment ('3D Print') will be the first machine to perform 3D printing in space. The greater the distance from Earth and the longer the mission duration, the more difficult resupply becomes; this requires a change from the current spares, maintenance, repair, and hardware design model that has been used on the International Space Station (ISS) up until now. Given the extension of the ISS Program, which will inevitably result in replacement parts being required, the ISS is an ideal platform to begin changing the current model for resupply and repair to one that is more suitable for all exploration missions. 3D Printing, more formally known as Additive Manufacturing, is the method of building parts/objects/tools layer‐by‐layer. The 3D Print experiment will use extrusion‐based additive manufacturing, which involves building an object out of plastic deposited by a wire‐feed via an extruder head. Parts can be printed from data files loaded on the device at launch, as well as additional files uplinked to the device while on‐orbit. The plastic extrusion additive manufacturing process is a low‐energy, low‐mass solution to many common needs on board the ISS. The 3D Print payload will serve as the ideal first step to proving that process in space. It is unreasonable to expect NASA to launch large blocks of material from which parts or tools can be traditionally machined, and even more unreasonable to fly up multiple drill bits that would be required to machine parts from aerospace‐grade materials such as titanium 6‐4 alloy and Inconel. The technology to produce parts on demand, in space, offers unique design options that are not possible through traditional manufacturing methods while offering cost-effective, high‐precision, low‐unit on‐demand manufacturing. Thus, Additive Manufacturing capabilities are the foundation of an advanced manufacturing in space roadmap. The 3D Printing In Zero‐G experiment will demonstrate the capability of utilizing Additive Manufacturing technology in space. This will serve as the enabling first step to realizing an additive manufacturing, print‐on‐demand "machine shop" for long‐duration missions and sustaining human exploration of other planets, where there is extremely limited ability and availability of Earth‐based logistics support. Simply put, Additive Manufacturing in space is a critical enabling technology for NASA. It will provide the capability to produce hardware on‐demand, directly lowering cost and decreasing risk by having the exact part or tool needed in the time it takes to print. This capability will also provide the much‐needed solution to the cost, volume, and up‐mass constraints that prohibit launching everything needed for long‐duration or long‐distance missions from Earth, including spare parts and replacement systems. A successful mission for the 3D Printing In Zero‐G payload is the first step to demonstrate the capability of printing on orbit. The data gathered and lessons learned from this demonstration will be applied to the next generation of additive manufacturing technology on orbit. It is expected that Additive Manufacturing technology will quickly become a critical part of any mission's infrastructure.

Werkheiser, Niki↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS2005

Making a Metal-Lined Composite-Overwrapped Pressure Vessel

process has been devised for the fabrication of a pressure vessel that comprises a composite-material (matrix/fiber) shell with a metal liner on its inner surface. The use of the composite material makes it possible for the tank to be strong enough to withstand the anticipated operating pressure and yet weigh less than does an equivalent all-metal tank. The metal liner is used as a barrier against permeation: In the absence of such a barrier, the pressurized gas in the tank could leak by diffusing through the composite-material shell. The figure depicts workpieces at four key stages in the process, which consists of the following steps: 1. A mandrel that defines the size and shape of the pressure vessel is made by either molding or machining a piece of tooling wax. 2. Silver paint is applied to the surface of the mandrel to make it electrically conductive. 3. The ends of the mandrel are fitted with metal bosses. 4. The mandrel is put into a plating bath, wherein the metal liner is electrodeposited. Depending on the applications, the liner metal could be copper, nickel, gold, or an alloy. Typical liner thicknesses range from 1 to 10 mils (0.025 to 0.25 mm). 5. The wax is melted from within, leaving the thin metal liner. 6. A hollow shaft that includes holes and fittings through which the liner can be pressurized is sealed to both ends of the liner. The liner is pressurized to stiffen (and hence stabilize) it for the next step. 7. The pressurized liner is placed in a filament-winding machine, which is then operated to cover the liner with multiple layers of an uncured graphite-fiber/epoxy-matrix or other suitable composite material. 8. The composite-overwrapped liner is cured in an oven. 9. The pressure is relieved and the shaft is removed. The tank is then ready for use. The process as described above accommodates variations: a) The mandrel could be made of a wax that melts at a higher temperature and not removed until the tank is cured in the oven. b) The tank need not be cylindrical or axisymmetric, as long as the filament-winding machine can accommodate the chosen shape. c) Shallow grooves could be formed on the surface of the mandrel to give the liner a bellows-like character for reinforcement and/or to accommodate expansion and contraction.

DeLay, Tom↗
NASA NTRS2014

3D Printing in Zero-G ISS Technology Demonstration

The National Aeronautics and Space Administration (NASA) has a long term strategy to fabricate components and equipment on-demand for manned missions to the Moon, Mars, and beyond. To support this strategy, NASA and Made in Space, Inc. are developing the 3D Printing In Zero-G payload as a Technology Demonstration for the International Space Station. The 3D Printing In Zero-G experiment will be the first machine to perform 3D printing in space. The greater the distance from Earth and the longer the mission duration, the more difficult resupply becomes; this requires a change from the current spares, maintenance, repair, and hardware design model that has been used on the International Space Station up until now. Given the extension of the ISS Program, which will inevitably result in replacement parts being required, the ISS is an ideal platform to begin changing the current model for resupply and repair to one that is more suitable for all exploration missions. 3D Printing, more formally known as Additive Manufacturing, is the method of building parts/ objects/tools layer-by-layer. The 3D Print experiment will use extrusion-based additive manufacturing, which involves building an object out of plastic deposited by a wire-feed via an extruder head. Parts can be printed from data files loaded on the device at launch, as well as additional files uplinked to the device while on-orbit. The plastic extrusion additive manufacturing process is a low-energy, low-mass solution to many common needs on board the ISS. The 3D Print payload will serve as the ideal first step to proving that process in space. It is unreasonable to expect NASA to launch large blocks of material from which parts or tools can be traditionally machined, and even more unreasonable to fly up specialized manufacturing hardware to perform the entire range of function traditionally machining requires. The technology to produce parts on demand, in space, offers unique design options that are not possible through traditional manufacturing methods while offering cost-effective, high-precision, low-unit on-demand manufacturing. Thus, Additive Manufacturing capabilities are the foundation of an advanced manufacturing in space roadmap.

Johnston, Mallory M.↗
NASA NTRSDate not supplied

The Repair Maintenance and Fabrication Facility in the Common Habitat Architecture

The Common Habitat Architecture seeks to increase the habitability of long-duration human spaceflight systems. A key aspect of this is vehicle survivability. Missions beyond low Earth orbit need onboard capabilities for Repair, Maintenance, And Fabrication (RMAF) to overcome potential contingency scenarios. Strategies employed in historic human spaceflight such as redundancy management, reliability, sparing, orbital replacement units, and aborts may be insufficient by themselves. Based on subject matter input, a list of 53 critical failures defining a set of incidents that can render a key spacecraft subsystem inoperable were generated. A subsequent analysis found that a robust in-space RMAF system capable of performing 14 key functions can potentially repair a subsystem plagued by any of these failures. An ancillary benefit is this capability may provide psychological benefits to the crew, by enabling greater self-sufficiency in earth-independent problem solving. A basic RMAF facility has been defined for the Common Habitat, consisting of five workstations. A work bench and computer workstation provide a multipurpose horizontal work surface, computing interface, and tools storage. A CNC machining center provides a subtractive manufacturing capability for metals and plastics. A multi-material 3D printing facility provides additive manufacturing capabilities for plastic, metals, and printed electronics. A welding facility is used for joining metal components where a higher strength is needed than can be achieved with fasteners or adhesives. A glovebox facility is used to perform work that is too hazardous for any of the other workstations. This may include hardware brought in from outside the spacecraft that could potentially contaminate the cabin environment. Forward work includes considering the accommodation of additional manufacturing processes not modeled in the current system, assessing the ability of systems to operate in partial gravity and microgravity environments, incorporation of the system into the Common Habitat Computer Aided Design (CAD) model, bottoms-up mass estimating, and a crew time analysis.

Habitat↗
NASA NTRS1989

Error-Compensated Telescope

Proposed reflecting telescope includes large, low-precision primary mirror stage and small, precise correcting mirror. Correcting mirror machined under computer control to compensate for error in primary mirror. Correcting mirror machined by diamond cutting tool. Computer analyzes interferometric measurements of primary mirror to determine shape of surface of correcting mirror needed to compensate for errors in wave front reflected from primary mirror and commands position and movement of cutting tool accordingly.

Meinel, Aden B.↗
NASA NTRS2012

Revamping Spacecraft Operational Intelligence with Splunk

So what is Splunk? Instead of giving the technical details, which you can find online, I'll tell you what it did for me. Splunk slapped everything into one place, with one uniform format, and gave me the ability to forget about all these annoying details of where it is, how to parse it, and all that. Instead, I only need to interact with Splunk to find the data I need. This sounds simple and obvious, but it's surprising what you can do once you all of your data is indexed in one place. By having your data organized, querying becomes much easier. Let's say that I want to search telemetry for a sensor_name gtemp_1 h and to return all data that is at most five minutes old. And because Splunk can hook into a real ]time stream, this data will always be up-to-date. Extending the previous example, I can now aggregate all types of data into one view based in time. In this picture, I've got transaction logs, telemetry, and downlinked files all in one page, organized by time. Even though the raw data looks completely than this, I've defined interfaces that transform it into this uniform format. This gives me a more complete picture for the question what was the spacecraft doing at this particular time? And because querying data is simple, I can start with a big block of data and whiddle it down to what I need, rather than hunting around for the individual pieces of data that I need. When we have all the data we need, we can begin widdling down the data with Splunk's Unix-like search syntax. These three examples highlights my trial-and-error attempts to find large temperature changes. I begin by showing the first 5 temperatures, only to find that they're sorted chronologically, rather than from highest temperatures to lowest temperatures. The next line shows sorting temperatures by their values, but I find that that fs not really what I want either. I want to know the delta temperatures between readings. Looking through Splunk's user manual, I find the delta function, which lets me dynamically generate new information to use in my query. With that extra piece of information, I can now return only the telemetry readings where the temperature changed by at least 10. One other useful feature I'll mention is that all of these queries can be run through Splunk's API. So any scripting language you can think of can plug right in and make these queries. This gives us the ability to build a lot of new tools.

operational intelligence↗
NASA NTRSDate not supplied

V&V for Autonomy

Explore the source record for details and available documents.

V&V↗
NASA NTRSDate not supplied

NASA Glenn Electric Propulsion Work

NASA Glenn Electrified Aircraft Propulsion (EAP) Presentation Outline: High-Voltage, High-Power EAP - Standards and Regulations - Safety and Certification - High Power Density, High-Efficiency Electric Machines and Drives - Thermal Management - Fault Management - Superconducting Electric Machines - Materials Research - Modeling Tools - Advanced Controls

Electric Propulsion↗
NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗
NASA NTRS1966

Heat treatment stabilizes welded aluminum jigs and tool structures

Heat treatment processes, applied after welding but before machining, imparts above normal stability to welded aluminum jigs and tool structures. Weight saving will not be realized in these tools if rigidity equal to that of a comparable steel tool is required.

Mehnert, R. S.↗
NASA NTRS2003

Applying Model-Based Reasoning to the FDIR of the Command and Data Handling Subsystem of the International Space Station

All of the International Space Station (ISS) systems which require computer control depend upon the hardware and software of the Command and Data Handling System (C&DH) system, currently a network of over 30 386-class computers called Multiplexor/Dimultiplexors (MDMs)[18]. The Caution and Warning System (C&W)[7], a set of software tasks that runs on the MDMs, is responsible for detecting, classifying, and reporting errors in all ISS subsystems including the C&DH. Fault Detection, Isolation and Recovery (FDIR) of these errors is typically handled with a combination of automatic and human effort. We are developing an Advanced Diagnostic System (ADS) to augment the C&W system with decision support tools to aid in root cause analysis as well as resolve differing human and machine C&DH state estimates. These tools which draw from sources in model-based reasoning[ 16,291, will improve the speed and accuracy of flight controllers by reducing the uncertainty in C&DH state estimation, allowing for a more complete assessment of risk. We have run tests with ISS telemetry and focus on those C&W events which relate to the C&DH system itself. This paper describes our initial results and subsequent plans.

Robinson, Peter↗
NASA NTRS2003

Prompt and Precise Prototyping

For Sanders Design International, Inc., of Wilton, New Hampshire, every passing second between the concept and realization of a product is essential to succeed in the rapid prototyping industry where amongst heavy competition, faster time-to-market means more business. To separate itself from its rivals, Sanders Design aligned with NASA's Marshall Space Flight Center to develop what it considers to be the most accurate rapid prototyping machine for fabrication of extremely precise tooling prototypes. The company's Rapid ToolMaker System has revolutionized production of high quality, small-to-medium sized prototype patterns and tooling molds with an exactness that surpasses that of computer numerically-controlled (CNC) machining devices. Created with funding and support from Marshall under a Small Business Innovation Research (SBIR) contract, the Rapid ToolMaker is a dual-use technology with applications in both commercial and military aerospace fields. The advanced technology provides cost savings in the design and manufacturing of automotive, electronic, and medical parts, as well as in other areas of consumer interest, such as jewelry and toys. For aerospace applications, the Rapid ToolMaker enables fabrication of high-quality turbine and compressor blades for jet engines on unmanned air vehicles, aircraft, and missiles.

Source record↗
NASA NTRS2004

UIVerify: A Web-Based Tool for Verification and Automatic Generation of User Interfaces

In this poster, we describe a web-based tool for verification and automatic generation of user interfaces. The verification component of the tool accepts as input a model of a machine and a model of its interface, and checks that the interface is adequate (correct). The generation component of the tool accepts a model of a given machine and the user's task, and then generates a correct and succinct interface. This write-up will demonstrate the usefulness of the tool by verifying the correctness of a user interface to a flight-control system. The poster will include two more examples of using the tool: verification of the interface to an espresso machine, and automatic generation of a succinct interface to a large hypothetical machine.

Shiffman, Smadar↗
NASA NTRS2011

Man-Machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface transportation systems as exemplified by the Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by the assistive technologies. One formalized approach to study the impact of assistive technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human-system designs are proposed, developed, and tested. One HPM tool termed the Man-machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human-system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM and simulation environment that facilitates the design, visualization, and computational evaluation of complex man-machine system concepts in simulated operational environments. The paper will discuss a range of aviation specific applications including an approach used to model human error for NASA s Aviation Safety Program, and what-if analyses to evaluate flight deck technologies for NextGen operations. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human-system designs that evaluate assistive technologies: that of (1) model transparency and (2) model validation.

Gore, Brian F.↗