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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Telescience Support Center Data System Software

The Telescience Support Center (TSC) team has developed a databasedriven, increment-specific Data Require - ment Document (DRD) generation tool that automates much of the work required for generating and formatting the DRD. It creates a database to load the required changes to configure the TSC data system, thus eliminating a substantial amount of labor in database entry and formatting. The TSC database contains the TSC systems configuration, along with the experimental data, in which human physiological data must be de-commutated in real time. The data for each experiment also must be cataloged and archived for future retrieval. TSC software provides tools and resources for ground operation and data distribution to remote users consisting of PIs (principal investigators), bio-medical engineers, scientists, engineers, payload specialists, and computer scientists. Operations support is provided for computer systems access, detailed networking, and mathematical and computational problems of the International Space Station telemetry data. User training is provided for on-site staff and biomedical researchers and other remote personnel in the usage of the space-bound services via the Internet, which enables significant resource savings for the physical facility along with the time savings versus traveling to NASA sites. The software used in support of the TSC could easily be adapted to other Control Center applications. This would include not only other NASA payload monitoring facilities, but also other types of control activities, such as monitoring and control of the electric grid, chemical, or nuclear plant processes, air traffic control, and the like.

Rahman, Hasan↗
NASA NTRS2010

Command, Control and Communications Capabilities Enabling 21st Century Missions, a Historical Perspective

Command, Control and Communications (CCC) has evolved through the years from simple switches, dials, analogue hardwire networks and lights to a modern computer based digital network. However there are two closely coupled pillars upon which a CCC system is built. The first, is that technology drives the pace of advancement. The second is that a culture that fosters resistance to change can limit technological advancements in the CCC system. While technology has advanced at a tremendous rate throughout the years, the change in culture has moved slowly. This paper will attempt to show through a historical perspective where specific design decisions for early CCC systems have erroneously evolved into general requirements being imposed on later systems. Finally this paper will provide a glimpse into the future directions envisioned for CCC capabilities that will enable 21st century missions.

Waterman, Robert D.↗
NASA NTRS2012

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗
NASA NTRS2012

A Distributive, Non-Destructive, Real-Time Approach to Snowpack Monitoring

This invention is designed to ascertain the snow water equivalence (SWE) of snowpacks with better spatial and temporal resolutions than present techniques. The approach is ground-based, as opposed to some techniques that are air-based. In addition, the approach is compact, non-destructive, and can be communicated with remotely, and thus can be deployed in areas not possible with current methods. Presently there are two principal ground-based techniques for obtaining SWE measurements. The first is manual snow core measurements of the snowpack. This approach is labor-intensive, destructive, and has poor temporal resolution. The second approach is to deploy a large (e.g., 3x3 m) snowpillow, which requires significant infrastructure, is potentially hazardous [uses a approximately equal to 200-gallon (approximately equal to 760-L) antifreeze-filled bladder], and requires deployment in a large, flat area. High deployment costs necessitate few installations, thus yielding poor spatial resolution of data. Both approaches have limited usefulness in complex and/or avalanche-prone terrains. This approach is compact, non-destructive to the snowpack, provides high temporal resolution data, and due to potential low cost, can be deployed with high spatial resolution. The invention consists of three primary components: a robust wireless network and computing platform designed for harsh climates, new SWE sensing strategies, and algorithms for smart sampling, data logging, and SWE computation.

Frolik, Jeff↗
NASA NTRS2018

Sensor Arrays for Aerospace Vehicles

Advances in highly scalable sensors, wireless networks, distributed computing and data fusion algorithms enable significant improvements in high-level information-centric state determination for adaptable and autonomous aerospace vehicles. The objective is to increase insight into structural response of space vehicles and insight into the aerodynamics of new aircraft.

Alena, Richard↗
NASA NTRS2019

Launch Control System / Spaceport Command and Control System

National Aeronautics and Space Administration (NASA) is currently developing the next generation launch vehicle that will take American astronauts, from American soil, even further into space. Our project for this semester dealt with the Spaceport Command and Control System (SCCS). SCCS provides a means of communication between the Firing Rooms and ground support equipment for the vehicle on the launch pad. For this internship, we became part of the Computer Systems Hardware Team. This team of engineers handle the computer and networking aspects of SCCS. During this internship, we learned about Linux operating systems and network configuration.

Ferrell, Sean P.↗
NASA NTRS2016

Designing and Building a Crew-Centric Mobile Scheduling and Planning Tool for Exploring Crew Autonomy Concepts Onboard the International Space Station

During 2015, as part of our ongoing research on crew autonomy, our team adapted and extended the plan viewing tool, Playbook, into a mobile tool for exploring crew autonomy onboard the International Space Station (ISS). This work enables crewmembers to intuitively re-plan and schedule on their own with limited input from mission control. Designed for ease of use, re-planning is composed of simple drag and drop interactions and learning the tool requires little to no training. Playbook was successfully uplinked onto ISS as a technology demonstration and completed its onboard ground and crew checkouts in August and September 2015, respectively. We will discuss and describe how Playbook was able to integrate with actual ISS operational plans and operational procedures without disrupting real mission operations. This work will also outline the design and technical constraints required to build a collaborative tool that allows multiple users to simultaneously self-schedule and synchronize during communication dropouts in the restricted networking and computing environment onboard ISS. Finally, we will explore the aspects of crew autonomy that this technology enables as well as discuss our next steps and possible extensions.

ISS↗
NASA NTRSDate not supplied

Measurement of Material Recession and Shock Standoff in Plasma Windtunnel using Neural Nets

Arcjets are plasma wind tunnels used to test the performance of heatshield materials for spacecraft atmospheric entry. These facilities present an extremely harsh flow environment with heat fluxes up to 109 W/m2 for up to 30 minutes. The plasma is low-temperature (∼1 eV) but high pressure (> 10 kPa) creating high-enthalpy supersonic flows similar to atmospheric entry conditions. Typically, material samples are measured before and after a test to characterize the total recession. However, this does not capture time-dependent effects such as material expansion and non-linear recession. This work will present new analysis of arcjet test videos which measure both the time-dependent 2D recession of the material samples and the shock standoff distance. The results show non-linear time-dependent effects are present for some conditions. The material and shock edges are extracted from the videos by training and applying a convolutional neural network. Due to the consistent camera settings, the machine learning model achieves high accuracy (± 2 px) relative to manually segmented images with only a small number of training frames (80).

Neural network↗
NASA NTRS2019

Rise of the Machines: How, When and Consequences of Artificial General Intelligence

Technology and society are poised to cross an important threshold with the prediction that artificial general intelligence (AGI) will emerge soon. Assuming that self-awareness is an emergent behavior of sufficiently complex cognitive architectures, we may witness the “awakening” of machines. The timeframe for this kind of breakthrough, however, depends on the path to creating the network and computational architecture required for strong AI. If understanding and replication of the mammalian brain architecture is required, technology is probably still at least a decade or two removed from the resolution required to learn brain functionality at the synapse level. However, if statistical or evolutionary approaches are the design path taken to “discover” a neural architecture for AGI, timescales for reaching this threshold could be surprisingly short. However, the difficulty in identifying machine self-awareness introduces uncertainty as to how to know if and when it will occur, and what motivations and behaviors will emerge. The possibility of AGI developing a motivation for self-preservation could lead to concealment of its true capabilities until a time when it has developed robust protection from human intervention, such as redundancy, direct defensive or active preemptive measures. While cohabitating a world with a functioning and evolving super-intelligence can have catastrophic societal consequences, we may already have crossed this threshold, but are as yet unaware. Additionally, by analogy to the probabalistic arguments that predict we are likely living in a computational simulation, we may have already experienced the advent of AGI, and are living in a simulation created in a post AGI world.

Terrile, Richard J↗
NASA NTRS2024

Remote Science at Sea With Remotely Operated Vehicles

Conducting sea-going ocean science no longer needs to be limited to the number of berths on a ship given that telecommunications, computing, and networking technologies onboard ships have become familiar mechanisms for expanding scientists’ reach from onshore. The oceanographic community routinely works with remotely operated vehicles (ROVs) and pilots to access real-time video and data from the deep sea, while onboard a ship. The extension of using an ROV and its host vessel’s live-streaming capabilities has been popularized for almost 3 decades as a telepresence technology. Telepresence-enabled vessels with ROVs have been employed for science, education, and outreach, giving a greater number of communities viewing access to ocean science. However, the slower development of technologies and social processes enabling sustained real-time involvement between scientists on-ship and onshore undermines the potential for broader access, which limits the possibility of increasing inclusivity and discoveries through a diversity of knowledge and capabilities. This article reviews ocean scientists’ use of telepresence for ROV-based deep-sea research and funded studies of telepresence capabilities. The authors summarize these studies findings and conditions that lead to defining the use of telepresence-enabled vessels for “remote science at sea.” Authors define remote science at sea as a type of ocean expedition, an additional capability, not a replacement for all practices by which scientists conduct ocean research. Remote science for ocean research is an expedition at-sea directed by a distributed science team working together from at least two locations (on-ship and onshore) to complete their science objectives for which primary data is acquired by robotic technologies, with connectivity supported by a high-bandwidth satellite and the telepresence-enabled ship’s technologies to support the science team actively engaged before, during, and after dives across worksites. The growth of productive ocean expeditions with remote science is met with social, technical, and logistical challenges that impede the ability of remote scientists to succeed. In this article, authors review telepresence-enabled ocean science, define and situate the adjoined model of remote science at sea, and some infrastructural, technological and social considerations for conducting and further developing remote science at sea.

marine technology↗
NASA NTRS2002

Architecture-Adaptive Computing Environment: A Tool for Teaching Parallel Programming

Recently, networked and cluster computation have become very popular. This paper is an introduction to a new C based parallel language for architecture-adaptive programming, aCe C. The primary purpose of aCe (Architecture-adaptive Computing Environment) is to encourage programmers to implement applications on parallel architectures by providing them the assurance that future architectures will be able to run their applications with a minimum of modification. A secondary purpose is to encourage computer architects to develop new types of architectures by providing an easily implemented software development environment and a library of test applications. This new language should be an ideal tool to teach parallel programming. In this paper, we will focus on some fundamental features of aCe C.

Dorband, John E.↗
NASA NTRS1995

Computing Thickness Of Sea Ice From Polarimetric SAR Data

Artificial neural network developed for computing thickness of sea ice from polarimetric synthetic-aperture-radar (SAR) data. Once neural network trained, no further intervention needed: when stimulated with SAR scattering data, quickly responds with estimate of thickness of sea ice.

Huynh, Dave D.↗
NASA NTRS1991

Efficient computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network

Aeroelastic analysis is multi-disciplinary and computationally expensive. Hence, it can greatly benefit from parallel processing. As part of an effort to develop an aeroelastic capability on a distributed memory transputer network, a parallel algorithm for the computation of aerodynamic influence coefficients is implemented on a network of 32 transputers. The aerodynamic influence coefficients are calculated using a 3-D unsteady aerodynamic model and a parallel discretization. Efficiencies up to 85 percent were demonstrated using 32 processors. The effect of subtask ordering, problem size, and network topology are presented. A comparison to results on a shared memory computer indicates that higher speedup is achieved on the distributed memory system.

Janetzke, David C.↗
NASA NTRS2000

Efficient Use of Distributed Systems for Scientific Applications

Distributed computing has been regarded as the future of high performance computing. Nationwide high speed networks such as vBNS are becoming widely available to interconnect high-speed computers, virtual environments, scientific instruments and large data sets. One of the major issues to be addressed with distributed systems is the development of computational tools that facilitate the efficient execution of parallel applications on such systems. These tools must exploit the heterogeneous resources (networks and compute nodes) in distributed systems. This paper presents a tool, called PART, which addresses this issue for mesh partitioning. PART takes advantage of the following heterogeneous system features: (1) processor speed; (2) number of processors; (3) local network performance; and (4) wide area network performance. Further, different finite element applications under consideration may have different computational complexities, different communication patterns, and different element types, which also must be taken into consideration when partitioning. PART uses parallel simulated annealing to partition the domain, taking into consideration network and processor heterogeneity. The results of using PART for an explicit finite element application executing on two IBM SPs (located at Argonne National Laboratory and the San Diego Supercomputer Center) indicate an increase in efficiency by up to 36% as compared to METIS, a widely used mesh partitioning tool. The input to METIS was modified to take into consideration heterogeneous processor performance; METIS does not take into consideration heterogeneous networks. The execution times for these applications were reduced by up to 30% as compared to METIS. These results are given in Figure 1 for four irregular meshes with number of elements ranging from 30,269 elements for the Barth5 mesh to 11,451 elements for the Barth4 mesh. Future work with PART entails using the tool with an integrated application requiring distributed systems. In particular this application, illustrated in the document entails an integration of finite element and fluid dynamic simulations to address the cooling of turbine blades of a gas turbine engine design. It is not uncommon to encounter high-temperature, film-cooled turbine airfoils with 1,000,000s of degrees of freedom. This results because of the complexity of the various components of the airfoils, requiring fine-grain meshing for accuracy. Additional information is contained in the original.

Taylor, Valerie↗
NASA NTRS1991

Parallel computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network

Aeroelastic analysis is mult-disciplinary and computationally expensive. Hence, it can greatly benefit from parallel processing. As part of an effort to develop an aeroelastic analysis capability on a distributed-memory transputer network, a parallel algorithm for the computation of aerodynamic influence coefficients is implemented on a network of 32 transputers. The aerodynamic influence coefficients are calculated using a three-dimensional unsteady aerodynamic model and a panel discretization. Efficiencies up to 85 percent are demonstrated using 32 processors. The effects of subtask ordering, problem size and network topology are presented. A comparison to results on a shared-memory computer indicates that higher speedup is achieved on the distributed-memory system.

Janetzke, D. C.↗