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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1994

Analysis of space network loading

The NASA Space Network (SN) consists of several geosynchronous communications satellites, in addition to ground support facilities. Space Network management must predict years in advance what network resources are necessary to adequately satisfy all SN users. Similarly, users of the Space Network must know throughout all stages of mission planning and operations to what extent their communication support requirements can be met. NASA, at the Goddard Space Flight Center, performs Space Network and Mission Modeling using The Network Planning and Analysis System (NPAS), to determine the answers to these questions.

Simons, Mark↗

NASA NTRS1999

Traffic Management in ATM Networks Over Satellite Links

This report presents a survey of the traffic management Issues in the design and implementation of satellite Asynchronous Transfer Mode (ATM) networks. The report focuses on the efficient transport of Transmission Control Protocol (TCP) traffic over satellite ATM. First, a reference satellite ATM network architecture is presented along with an overview of the service categories available in ATM networks. A delay model for satellite networks and the major components of delay and delay variation are described. A survey of design options for TCP over Unspecified Bit Rate (UBR), Guaranteed Frame Rate (GFR) and Available Bit Rate (ABR) services in ATM is presented. The main focus is on traffic management issues. Several recommendations on the design options for efficiently carrying data services over satellite ATM networks are presented. Most of the results are based on experiments performed on Geosynchronous (GEO) latencies. Some results for Low Earth Orbits (LEO) and Medium Earth Orbit (MEO) latencies are also provided.

Goyal, Rohit↗

NASA NTRS2009

Controlling Infrastructure Costs: Right-Sizing the Mission Control Facility

Johnson Space Center's Mission Control Center is a space vehicle, space program agnostic facility. The current operational design is essentially identical to the original facility architecture that was developed and deployed in the mid-90's. In an effort to streamline the support costs of the mission critical facility, the Mission Operations Division (MOD) of Johnson Space Center (JSC) has sponsored an exploratory project to evaluate and inject current state-of-the-practice Information Technology (IT) tools, processes and technology into legacy operations. The general push in the IT industry has been trending towards a data-centric computer infrastructure for the past several years. Organizations facing challenges with facility operations costs are turning to creative solutions combining hardware consolidation, virtualization and remote access to meet and exceed performance, security, and availability requirements. The Operations Technology Facility (OTF) organization at the Johnson Space Center has been chartered to build and evaluate a parallel Mission Control infrastructure, replacing the existing, thick-client distributed computing model and network architecture with a data center model utilizing virtualization to provide the MCC Infrastructure as a Service. The OTF will design a replacement architecture for the Mission Control Facility, leveraging hardware consolidation through the use of blade servers, increasing utilization rates for compute platforms through virtualization while expanding connectivity options through the deployment of secure remote access. The architecture demonstrates the maturity of the technologies generally available in industry today and the ability to successfully abstract the tightly coupled relationship between thick-client software and legacy hardware into a hardware agnostic "Infrastructure as a Service" capability that can scale to meet future requirements of new space programs and spacecraft. This paper discusses the benefits and difficulties that a migration to cloud-based computing philosophies has uncovered when compared to the legacy Mission Control Center architecture. The team consists of system and software engineers with extensive experience with the MCC infrastructure and software currently used to support the International Space Station (ISS) and Space Shuttle program (SSP).

Martin, Keith↗

NASA NTRS2007

Space-Shuttle Emulator Software

A package of software has been developed to execute a raw binary image of the space shuttle flight software for simulation of the computational effects of operation of space shuttle avionics. This software can be run on inexpensive computer workstations. Heretofore, it was necessary to use real flight computers to perform such tests and simulations. The package includes a program that emulates the space shuttle orbiter general- purpose computer [consisting of a central processing unit (CPU), input/output processor (IOP), master sequence controller, and buscontrol elements]; an emulator of the orbiter display electronics unit and models of the associated cathode-ray tubes, keyboards, and switch controls; computational models of the data-bus network; computational models of the multiplexer-demultiplexer components; an emulation of the pulse-code modulation master unit; an emulation of the payload data interleaver; a model of the master timing unit; a model of the mass memory unit; and a software component that ensures compatibility of telemetry and command services between the simulated space shuttle avionics and a mission control center. The software package is portable to several host platforms.

Arnold, Scott↗

NASA NTRS1998

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Decker, A. J.↗

NASA NTRSDate not supplied

Concept-based Analysis of Neural Networks via Vision-Language Models

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. We describe a logical specification language Con spec designed to facilitate writing specifications in terms of these concepts. To define and formally check Con spec specifications, we build a map between the internal representations of a given vision model and a VLM, leading to an efficient verification procedure of natural-language properties for vision models. We demonstrate our techniques on a ResNet-based classifier trained on the RIVAL-10 dataset using CLIP as the multimodal model.

Large Vision Language Models↗

NASA NTRS1992

Identification of aerodynamic coefficients using computational neural networks

Precise, smooth aerodynamic models are required for implementing adaptive, nonlinear control strategies. Accurate representations of aerodynamic coefficients can be generated for the complete flight envelope by combining computational neural network models with an Estimation-Before-Modeling paradigm for on-line training information. A novel method of incorporating first-partial-derivative information is employed to estimate the weights in individual feedforward neural networks for each aerodynamic coefficient. The method is demonstrated by generating a model of the normal force coefficient of a twin-jet transport aircraft from simulated flight data, and promising results are obtained.

Linse, Dennis J.↗

NASA NTRS1981

A mechanism for solar ultraviolet flux variability

Following an examination of solar UV emission observed by a filter photometer on Nimbus IV from 1969 to 1973 in an attempt to understand the 27-day and secular variability, two models are proposed to account for the variations: (1) a calcium plage model and (2) a chromospheric network faculae and spicule structure model. An association between UV brightenings and the large scale magnetic field has been found to be consistent with the network model. An increase in the UV emittance can be achieved by raising the effective chromospheric temperature closer to a photospheric level. If the sun's luminosity is constant on these time intervals, the enhanced UV radiation could be partially offset by an overall decrease in photospheric temperature such as that measured by Livingston (1978) in visible photospheric profiles.

Schatten, K. H.↗

NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗

NASA NTRS1990

Toward modeling a dynamic biological neural network

The organization of a mammalian macular endorgan is interpreted on physical and engineering principles. Having accomplished this task, it is possible to mathematically and symbolically model information processing by the macular neural network. A complex symbolic model is produced through use of mathematical notations that describe the functioning system. The model, consisting of six tiers, is constructed to mimic the neural system. On the basis of initial simulations, it is concluded that the network functions best when some of the detecting elements (i.e., type I hair cells) are weakly inhibitory. The simulations also illustrate the importance of disinhibition of receptors located in the third tier in shaping nerve discharge patterns at the sixth tier in the model system.

Ross, M. D.↗

NASA NTRS1990

Networks for image acquisition, processing and display

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Ahumada, Albert J., Jr.↗

NASA NTRS2010

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Shameldin, Ramez Ahmed↗

NASA NTRS2024

Nodal Modeling of Liquid Propellant Feed and Pressurization System

Nodal or network flow modeling plays an important role in the design and operation of the feed and pressurization system of a liquid rocket engine. Model development and execution time is relatively short for nodal codes in comparison to the Navier-Stokes based CFD codes. Nodal models also allow the inclusion of several components into one model to predict the behavior of a larger system. Unlike CFD models, the nodal models do not need very fine discretization of the flow field because they use empirical correlations to model fluid friction and heat transfer. This paper presents several applications of nodal modeling of liquid propellant feed and pressurization systems using the Generalized Fluid System Simulation Program (GFSSP), a nodal code developed at NASA/ Marshall Space Flight Center. GFSSP discretizes the flow field into nodes which are connected by branches. The mass and energy conservation equations and the equation of state are solved to calculate pressure, temperature, and resident mass at the nodes. The momentum equations are solved at the branches to calculate flow rates. Applications include a) tank pressurization by inert gas as well as autogenous pressurization by gaseous propellant, b) submerged pressurization by helium, c) self-pressurization due to boil-off of cryogenic propellant, d) chilldown of a transfer line of a cryogenic propellant feed system, and e) chilldown and filling of a cryogenic tank. Each of the above-mentioned models was verified and validated by comparing with test data.

Nodal Model↗

NASA NTRS2024

Nodal Modeling of Liquid Propellant Feed and Pressurization System

Nodal Model↗

NASA NTRS2011

Contact Graph Routing

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology of scheduled communication contacts in a network based on the DTN (Delay-Tolerant Networking) architecture. It is designed to enable dynamic selection of data transmission routes in a space network based on DTN. This dynamic responsiveness in route computation should be significantly more effective and less expensive than static routing, increasing total data return while at the same time reducing mission operations cost and risk. The basic strategy of CGR is to take advantage of the fact that, since flight mission communication operations are planned in detail, the communication routes between any pair of bundle agents in a population of nodes that have all been informed of one another's plans can be inferred from those plans rather than discovered via dialogue (which is impractical over long one-way-light-time space links). Messages that convey this planning information are used to construct contact graphs (time-varying models of network connectivity) from which CGR automatically computes efficient routes for bundles. Automatic route selection increases the flexibility and resilience of the space network, simplifying cross-support and reducing mission management costs. Note that there are no routing tables in Contact Graph Routing. The best route for a bundle destined for a given node may routinely be different from the best route for a different bundle destined for the same node, depending on bundle priority, bundle expiration time, and changes in the current lengths of transmission queues for neighboring nodes; routes must be computed individually for each bundle, from the Bundle Protocol agent's current network connectivity model for the bundle s destination node (the contact graph). Clearly this places a premium on optimizing the implementation of the route computation algorithm. The scalability of CGR to very large networks remains a research topic. The information carried by CGR contact plan messages is useful not only for dynamic route computation, but also for the implementation of rate control, congestion forecasting, transmission episode initiation and termination, timeout interval computation, and retransmission timer suspension and resumption.

Burleigh, Scott C.↗

NASA NTRS2000

Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks

NASA Langley Research Center, Hampton, VA 23681 Under a joint research and development effort conducted by the National Aeronautics and Space Administration and The Boeing Company (formerly McDonnell Douglas) three neural-network based control systems were developed and tested. The control systems were experimentally evaluated using a transonic wind-tunnel model in the Langley Transonic Dynamics Tunnel. One system used a neural network to schedule flutter suppression control laws, another employed a neural network in a predictive control scheme, and the third employed a neural network in an inverse model control scheme. All three of these control schemes successfully suppressed flutter to or near the limits of the testing apparatus, and represent the first experimental applications of neural networks to flutter suppression. This paper will summarize the findings of this project.

Scott, Robert C.↗

NASA NTRS1966

Dynamics of a pinch discharge driven by a high current pulse-forming network

Dynamics of linear pinch discharge driven by high current pulse-forming network - snowplow model

ELECTRIC NETWORK↗

NASA NTRS2023

Prediction of Stiffness and Fatigue Lives of Polymer Matrix Composite Laminates Using Artificial Neural Networks

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, both Python and MATLAB-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been developed for both platforms. Results show that the both neural net types can provide an excellent estimate of initial stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminate. RNNs are better able to capture the shape of the fatigue curve of a laminate. This tool can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. The associated surrogate models could also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make multiscale analyses a viable industrial tool for large scale structural problems.

Composite↗