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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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At least 847 records · Page 47

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NASA NTRS1977

Interacting turbulent boundary layer over a wavy wall

This paper is concerned with the two-dimensional supersonic flow of a thick turbulent boundary layer over a train of relatively small wave-like protuberances. The flow conditions and the geometry are such that there exists a strong interaction between the viscous and inviscid flow. The problem cannot be solved without inclusion of interaction effects due to the occurrence of the separation singularity in classical boundary layer methods. Here the interacting boundary layer equations are solved numerically using a time-like relaxation method with turbulence effects represented by the inclusion of the eddy viscosity model of Cebeci and Smith. Results are presented for flow over a train of up to six waves for Mach numbers of 2.5 and 3.5, Reynolds numbers of 10,000,000/m and 32,000,000/m, and wall temperature ratios of 0.4 and 0.8. Limited comparisons with independent experimental and analytical results are also given.

Polak, A.↗

NASA NTRS1970

An Exercise Prescription Intervention Program with Periodic Ergometric Grading

A long term exercise prescription type of physical conditioning program has been available to executive personnel of the NASA Manned Spacecraft Center for the past two years. Periodic ergometric testing with a heart rate controlled, automatically programmed, bicycle ergometer is used to follow the individual's progress and appropriately alter his exercise prescription from time to time. Such a program appears feasible, and acceptance is excellent, dropout rates small and periodic testing participation good. Subjects training diligently can maintain satisfactory levels of conditioning.

Owen, C. A.↗

NASA NTRS1979

Liquid oxygen/liquid hydrogen auxiliary power system thruster investigation

The design, fabrication, and demonstration of a 111 newton (25 lb) thrust, integrated auxiliary propulsion system (IAPS) thruster for use with LH2/LO2 propellants is described. Hydrogen was supplied at a temperature range of 22 to 33 K (40 to 60 R), and oxygen from 89 to 122 K (160 to 220 R). The thruster was designed to operate in both pulse mode and steady-state modes for vehicle attitude control, space maneuvering, and as an abort backup in the event of failure of the main propulsion system. A dual-sleeve, tri-axial injection system was designed that utilizes a primary injector/combustor where 100 percent of the oxygen and 8 percent of the hydrogen is introduced; a secondary injector/combustor where 45 percent of the hydrogen is introduced to mix with the primary combustor gases; and a boundary layer injector that uses the remaining 45 percent of the hydrogen to cool the thrust throat/nozzle design. Hot-fire evaluation of this thruster with a BLC injection distance of 2.79 cm (1.10 in.) indicated that a specific impulse value of 390 sec can be attained using a coated molybdenum thrust chamber. Pulse mode tests indicated that a chamber pressure buildup to 90 percent thrust can be achieved in a time on the order of 48 msec. Some problems were encountered in achieving ignition of each pulse during pulse trains. This was interpreted to indicate that a higher delivered spark energy level ( 100 mJ) would be required to maintain ignition reliability of the plasma torch ignition system under the extra 'cold' conditions resulting during pulsing.

Eberle, E. E.↗

NASA NTRS1981

Relation of the activities of the IPDF/INPE project (reforestation subproject) during the year 1979

The state of Mato Grosso do Sul was selected as the study area to define the recognizable classes of Eucalyptus spp. and Pinus spp. by visual and automatic analyses. For visual analysis, a preliminary interpretation key and a legend of 6 groups were derived. Based on these six groups, three final classes were defined for analysis: (1) area prepared for reforestation; (2) area reforested with Eucalyptus spp.; and (3) area reforested with Pinus spp. For automatic interpretation the area along the highway from Ribas do Rio Pardo to Agua Clara was classified into the following classes: eucalytus, bare soil, plowed soil, pine and "cerrado". The results of visual analysis show that 67% of the reforested farms have relative differences in area estimate below 5%, 22%, between 5% and 10%; and 11% between 10% and 20%. The reforested eucalyptus area is 17 times greater than the area of reforested pine. Automatic classification of eucalyptus ranged from 73.03% to 92.30% in the training areas.

Dejesusparada, N.↗

NASA NTRS1986

An Ada implementation of the network manager for the advanced information processing system

From an implementation standpoint, the Ada language provided many features which facilitated the data and procedure abstraction process. The language supported a design which was dynamically flexible (despite strong typing), modular, and self-documenting. Adequate training of programmers requires access to an efficient compiler which supports full Ada. When the performance issues for real time processing are finally addressed by more stringent requirements for tasking features and the development of efficient run-time environments for embedded systems, the full power of the language will be realized.

Nagle, Gail A.↗

NASA NTRS1993

Influence of simulated microgravity on the maximal oxygen consumption of nontrained and trained rats

The effects of microgravity and endurance training (TR) on maximal O2 consumption was investigated in trained and nontrained (NT) rats subjected to head-down suspension (HDS) by comparing maximal O2 consumption, treadmill run time (RT), and mechanical efficiency (ME) of treadmill running in HDS rats, both NT and TR, and in respective cage controls. It was found that HDS for 28 days was associated with significant reduction in absolute maximal O2 consumption in both TR and NT rats. Relative maximal O2 consumption, however, was significantly reduced in TR but not NT rats. Reductions in RT and ME occurring in both TR and NT rats after 28 days of HDS were similar. The TR rats exhibited greater diuretic, natriuretic, and kaliuretic responses to HDS than the NT rats.

Woodman, Christopher R.↗

NASA NTRS1994

A distributed computing model for telemetry data processing

We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.

Barry, Matthew R.↗

NASA NTRS2002

A Scalable, Out-of-Band Diagnostics Architecture for International Space Station Systems Support

The computational infrastructure of the International Space Station (ISS) is a dynamic system that supports multiple vehicle subsystems such as Caution and Warning, Electrical Power Systems and Command and Data Handling (C&DH), as well as scientific payloads of varying size and complexity. The dynamic nature of the ISS configuration coupled with the increased demand for payload support places a significant burden on the inherently resource constrained computational infrastructure of the ISS. Onboard system diagnostics applications are hosted on computers that are elements of the avionics network while ground-based diagnostic applications receive only a subset of available telemetry, down-linked via S-band communications. In this paper we propose a scalable, out-of-band diagnostics architecture for ISS systems support that uses a read-only connection for C&DH data acquisition, which provides a lower cost of deployment and maintenance (versus a higher criticality readwrite connection). The diagnostics processing burden is off-loaded from the avionics network to elements of the on-board LAN that have a lower overall cost of operation and increased computational capacity. A superset of diagnostic data, richer in content than the configured telemetry, is made available to Advanced Diagnostic System (ADS) clients running on wireless handheld devices, affording the crew greater mobility for troubleshooting and providing improved insight into vehicle state. The superset of diagnostic data is made available to the ground in near real-time via an out-of band downlink, providing a high level of fidelity between vehicle state and test, training and operational facilities on the ground.

Fletcher, Daryl P.↗

NASA NTRS2004

Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

Gupta, Pramod↗

NASA NTRS2005

Design, Development, and Innovation of an Interactive Multimedia Training Simulator for Responding to Air Transportation Bomb Threats

This paper describes an interactive multimedia simulator for air transportation bomb threat training. The objective of this project is to improve the air transportation sector s capability to respond to bomb threats received by commercial airports and aircraft. The simulator provides realistic training on receiving and responding to a variety of bomb threats that might not otherwise be possible due to time, cost, or operational constraints. Validation analysis indicates that the use of the simulator resulted in statistically significant increases in individual ability to respond to these types of bomb threats.

Chung, Christopher A.↗

NASA NTRS2009

Image-Capture Devices Extend Medicine's Reach

Johnson Space Center, Henry Ford Hospital in Detroit, and Houston-based Wyle Laboratories collaborated on NASA's Advanced Diagnostic Ultrasound in Microgravity (ADUM) experiment, which developed revolutionary medical ultrasound diagnostic techniques for long-distance use. Mediphan, a Canadian company with U.S. operations in Springfield, New Jersey drew on NASA expertise to create frame-grabber and data archiving technology that enables ultrasound users with minimal training to send diagnostic-quality ultrasound images and video to medical professionals via the Internet in near real time allowing patients as varied as professional athletes, Olympians, and mountain climbers to receive medical attention as soon as it is needed.

Source record↗

NASA NTRS2002

INTEGRITY - Integrated Human Exploration Mission Simulation Facility

It is proposed to develop a high-fidelity ground facility to carry out long-duration human exploration mission simulations. These would not be merely computer simulations - they would in fact comprise a series of actual missions that just happen to stay on earth. These missions would include all elements of an actual mission, using actual technologies that would be used for the real mission. These missions would also include such elements as extravehicular activities, robotic systems, telepresence and teleoperation, surface drilling technology-all using a simulated planetary landscape. A sequence of missions would be defined that get progressively longer and more robust, perhaps a series of five or six missions over a span of 10 to 15 years ranging in duration from 180 days up to 1000 days. This high-fidelity ground facility would operate hand-in-hand with a host of other terrestrial analog sites such as the Antarctic, Haughton Crater, and the Arizona desert. Of course, all of these analog mission simulations will be conducted here on earth in 1-g, and NASA will still need the Shuttle and ISS to carry out all the microgravity and hypogravity science experiments and technology validations. The proposed missions would have sufficient definition such that definitive requirements could be derived from them to serve as direction for all the program elements of the mission. Additionally, specific milestones would be established for the "launch" date of each mission so that R&D programs would have both good requirements and solid milestones from which to .build their implementation plans. Mission aspects that could not be directly incorporated into the ground facility would be simulated via software. New management techniques would be developed for evaluation in this ground test facility program. These new techniques would have embedded metrics which would allow them to be continuously evaluated and adjusted so that by the time the sequence of missions is completed, the best management techniques will have been developed, implemented, and validated. A trained cadre of managers experienced with a large, complex program would then be available.

Henninger, Donald L.↗

NASA NTRS2014

A Semiautomated Multilayer Picking Algorithm for Ice-sheet Radar Echograms Applied to Ground-Based Near-Surface Data

Snow accumulation over an ice sheet is the sole mass input, making it a primary measurement for understanding the past, present, and future mass balance. Near-surface frequency-modulated continuous-wave (FMCW) radars image isochronous firn layers recording accumulation histories. The Semiautomated Multilayer Picking Algorithm (SAMPA) was designed and developed to trace annual accumulation layers in polar firn from both airborne and ground-based radars. The SAMPA algorithm is based on the Radon transform (RT) computed by blocks and angular orientations over a radar echogram. For each echogram's block, the RT maps firn segmented-layer features into peaks, which are picked using amplitude and width threshold parameters of peaks. A backward RT is then computed for each corresponding block, mapping the peaks back into picked segmented-layers. The segmented layers are then connected and smoothed to achieve a final layer pick across the echogram. Once input parameters are trained, SAMPA operates autonomously and can process hundreds of kilometers of radar data picking more than 40 layers. SAMPA final pick results and layer numbering still require a cursory manual adjustment to correct noncontinuous picks, which are likely not annual, and to correct for inconsistency in layer numbering. Despite the manual effort to train and check SAMPA results, it is an efficient tool for picking multiple accumulation layers in polar firn, reducing time over manual digitizing efforts. The trackability of good detected layers is greater than 90%.

Antarctic Ice-Sheet↗

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 NTRSDate not supplied

Case Study of Meteoroid Fluence on a Mars Sample Return Mission

The Mars Sample Return Campaign must satisfy a Back Planetary Protection requirement that limits the risk of biological contamination of the Earth by Martian microbes. The Earth Re-turn Orbiter (ERO), being developed by the European Space Agency (ESA), will carry a Capture, Containment and Return System (CCRS), which is being developed by NASA to retrieve orbiting samples (OS) from Mars orbit, surround them with two containment layers to create a Contained OS (COS), and transfer the COS to the Earth Entry System (EES), which will be re-leased by ERO to carry the samples through entry, descent and landing at the Utah Test and Training Range. Micrometeoroid impact damage to the Thermal Protection System (TPS) of the EES, which may occur at any time in the ERO mission, is recognized as a potential risk driver for loss of sample containment that needs to be rigorously assessed. A Micrometeoroid Protection System (MMPS) will be implemented to limit that risk. The MMPS consists of a shield covering the TPS called the Micrometeoroid Garage (MMG) and a system of cameras to inspect the TPS. An extensive study was performed by the CCRS Project in collaboration with the NASA Engineering and Safety Center (NESC)to assess the probability of damage to the MMG and TPS by meteoroids over the entire ERO mission. This included the computation of the meteoroid fluence on the MMG and directly on the TPS after EES separation, and assessment of damage to the MMG and TPS via hypervelocity impact tests, hydrocode simulations, and empirical damage prediction and ballistic limit equations (BLEs). The focus of this paper is the meteoroid fluence on the MMG.

Micrometeoroid↗

NASA NTRSDate not supplied

Case Study of Meteoroid Fluence on a Mars Sample Return Mission

micrometeoroid↗

NASA NTRSDate not supplied

Game Based Learning For Earth Science Applications Training

Current NASA Earth capacity development programs employ mechanisms ranging from online resource sharing, and virtual and in-person trainings to share knowledge. While these programs are highly successful at engaging individuals around the world – in 2018, over 8000 individuals and over 2000 institutions from all 50 US states and over 140 countries were engaged through over 150 projects and trainings – user feedback has highlighted the desire for expanded hands-on, practical experiences in incorporating NASA EO insights with localized data and actions. We aim to address this gap by leveraging the benefits of game-based learning to build user skills in integrating NASA and local EO data to guide decisions for climate resiliency and hazard planning. This project is being executed as a two-phase crowdsourced challenge: 1) Phase 1 will require a well-researched product concept that reflects an understanding of NASA’s Earth data and tools and user needs, and proposes an innovative and interactive game or extended reality experience to train users in identifying relevant NASA data and applying insights to their climate resiliency decisions; 2) Winners of Phase 1 will be provided seed funding to develop a working prototype of the product. We aim to award 1-3 final winners to support the development of more than one game, thereby ensuring that NASA's diverse audiences around the world can access training games that best suit their needs and capabilities. This EO training game project fits in the NASA Earth Science Applied Sciences Program’s Capacity Development Program, contributing to the program mission of “helping people around the world better understand [NASA’s Earth] data and find ways to use them” (https://appliedsciences.nasa.gov/what-we-do/capacity-building). The final training game will complement existing programmatic activities of workforce development, trainings, and collaborative projects, while providing the unique value of providing interactive experiences to users and collecting real-time data and feedback to improve NASA’s Earth applications’ products and services related to climate resilience.

Human centered design↗

NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗