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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Use of a personal computer for dynamical engineering illustrations in a classroom and over an instructional TV network

A personal computer has been used to illustrate physical phenomena and problem solution techniques in engineering classes. According to student evaluations, instruction of concepts was greatly improved through the use of these illustrations. This paper describes the class of phenomena that can be effectively illustrated, the techniques used to create these illustrations, and the techniques used to display the illustrations in regular classrooms and over an instructional TV network. The features of a personal computer required to apply these techniques are listed. The capabilities of some present personal computers are discussed and a forecast of the capabilities of future personal computers is presented.

Watson, V. R.↗
NASA NTRS1990

Model-free distributed learning

Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights. The perturbation may be either via noise sources or orthogonal signals. The invariance to detailed network structure mitigates large variability between supposedly identical networks as well as implementation defects. This local, regular, and completely distributed mechanism requires no central control and involves only a few global signals. Thus it allows for integrated on-chip learning in large analog and optical networks.

Dembo, Amir↗
NASA NTRS1993

Discrete Recurrent Neural Networks for Grammatical Inference

Recurrent neural networks have recently been shown to have the ability to learn regular and context-free grammars from examples. We show that while conventional analog recurrent networks try to form clusters in activation space to represent discrete states of the grammars during learning, and can be successful in doing so, the clusters so formed tend to become unstable as longer and longer test input strings are presented to the network.

Neural↗
NASA NTRS1988

Astrometric results of 1978-1985 Deep Space Network radio interferometry - The JPL 1987-1 extragalactic source catalog

An astrometric radio reference frame has been determined from intercontinental dual-frequency radio interferometric measurements. These measurements were carried out on a regular basis during 1978-1985 between NASA's Deep Space Network stations in California, Spain, and Australia. Analysis of 6800 pairs of delay and delay-rate observations made during 51 sessions produced estimates of 1300 parameters. The most significant of these are geophysical quantities and positions of extragalactic sources. The source catalog resulting from this analysis includes 106 sources fairly uniformly distributed over the celestial sphere, north of -45 deg declination. Almost all of the resulting source positions have formal uncertainties between 0.5 and 3 milliarcseconds (mas), with rms values of 2 mas in both angular coordinates. Internal consistency checks, as well as comparisons with independently determined source catalogs of comparable quality, indicate that relative source coordinates determined by VLBI contain systematic errors at the level of 1 to 2 mas.

Sovers, O. J.↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle. However, these hypothesized relationships have not been rigorously tested due to the subjective nature of TCBs in satellite images. A machine learning technique that successfully identifies TCBs objectively in imagery from the GOES-16 Advanced Baseline Imager (ABI) has been developed to solve this problem. The technique uses a U-Net convolutional neural network (CNN) that assigns a probability to each pixel in an image based on the likelihood of the pixel being associated with a TCB. Using the U-Net CNN, a database of TCBs from 2019 to 2022 was developed for the Atlantic tropical cyclone basin by defining an appropriate probability threshold that defines the difference between TCB and non-TCB pixels. This threshold is where the Jaccard score, calculated using manually identified TCBs and model identified TCBs, is maximized. Statistics for TCB occurrence will also be presented, including the relationships between TCBs and storm relative motion, shear relative direction, cardinal direction, tropical cyclone intensity, tropical cyclone intensification rates, and time of day.

John Mark Mayhall↗
NASA NTRS1978

Rocket ozone sounding network data

During the period December 1976 through February 1977, three regular monthly ozone profiles were measured at Wallops Flight Center, two special soundings were taken at Antigua, West Indies, and at the Churchill Research Range, monthly activities were initiated to establish stratospheric ozone climatology. This report presents the data results and flight profiles for the period covered.

Wright, D. U.↗
NASA NTRS1979

Rocket ozone sounding network data

During the period March 1977 through May 1977, three regular monthly ozone profiles were measured at Wallops Flight Center and three regular monthly ozone profiles were measured at the Churchill Research Range. One additional flight was conducted at Wallops Flight Center in support of Nimbus 4 SBUV. Data results and flight profiles for the period covered are presented.

Wright, D. U.↗
NASA NTRS1998

Analysis of record-breaking low ozone values during the 1997 winter over NDSC Station Lauder, New Zealand

During early August 1997, the ozone column density measured over Lauder was unusually low, with a minimum value of 222 Dobson Units (DU) at August 10. These observations are striking since in August, during the Austral winter, the ozone column density should be heading towards its yearly maximum; The August mean ozone column density measured over Lauder between 1987 and 1996 was 348(+/-28) DU, the lowest monthly average in these ten years was 255 DU. Regular altitude profile measurements of ozone, performed at Network for the Detection of Stratospheric Change (NDSC) station Lauder, make it possible to do a detailed, altitude-resolved, study of the low ozone observations. The measurements show ozone poor air in two altitude regions of the stratosphere: A 'high region', extending from the 600 K to the 1050 K isentrope (25 to 34 km), and a 'low region', below about 550 K (22 km). High resolution reverse trajectory maps of potential vorticity (PV) and ozone mixing ratio, based on the assumption of passive advection by the large-scale three-dimensional winds, show that in the 'high region' the ozone poor air was part of the polar vortex, which was centered off the pole and extended over Lauder for several days, while in the 'low region' the ozone poor air was mixed in from low latitudes. A rapid recovery of the ozone column density, by more than 110 DU within 24 hours, was observed when in the low region an ozone rich filament of the polar vortex moved over Lauder, while in the high region the (ozone poor) high part of the vortex moved away.

Brinksma, E. J.↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds that are regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle, but these hypothesized relationships have not been rigorously tested due to the difficulty of objectively identifying TCBs in satellite images. This presentation describes a machine learning technique that successfully identifies TCBs in imagery from the GOES-16 Advanced Baseline Imager. The technique uses a convolutional neural network (CNN) that assigns a probability of each pixel in the image being associated with a TCB. Using the CNN, a database of TCBs from 2018 to 2022 was developed for the Atlantic basin. Statistics using this database will be presented, including the relationship between TCBs and deep-layer vertical wind shear, intensity change, and the TC diurnal cycle.

John Mark Mayhall↗
NASA NTRS1987

Meteor velocity distribution and an optimum monitoring mathematical model

At present, there are a great number of radio meteor, ionosphere and rocket observation data for the altitude range of 80 to 100 km which indicate the existence of large scale circulation systems in the mesopause to low thermosphere range which change regularly with season and latitude. But the existing observation network and observation programs are not optimal for revealing the main factors forming the circulation mode at these altitudes. A generalized optimum monitoring mathematical model is offered for consideration. The model input data are distribution density, response function, individual measurement root mean square uncertainty and detection effectiveness function. The model makes it possible to obtain the observation distribution density, the minimal possible dispersion and optimized system effectiveness.

Volkov, N. G.↗
NASA NTRS2008

System Engineering on the Use for Ares I,V - the Simpler, the Better

The Ares I and Ares V Vehicles will utilize the J-2X rocket engine developed for NASA by the Pratt & Whitney Rocketdyne Company. The J-2X is an improved higher power version of the original J-2 engine used during the Apollo program. With higher power and updated requirements for safety and performance, the J-2X becomes a new engine using state-of-the-art design methodology, materials and manufacturing processes. The implementation of Systems Engineering (SE) principles enables the rapid J-2X development program to remain aligned with the ARES I and V vehicle programs, Meeting the aggressive development schedule is a challenge. Coordinating the best expertise thai NASA and PWR have to offer requires effectively utilizing resources at multiple sites. This presents formidable communication challenges. SE allows honest and open discussions of issues and problems. This simple idea is often overlooked in large and complex SE programs. Regular and effective meetings linking SE objectives to component designs are used to voice differences of opinions with customer and contractor in attendance so that the best mutual decisions can be made on the shortest possible schedule. Regular technical interchange meetings on secure program wide computer networks and CM processes are effective,in the "Controlled Change" process that exemplifies good SE. Good communication is a key effective SE implementation. The System of Systems approach is the vision of the Orion program which facilitates the establishment of dynamic SE processes at all levels including the engine. SE enables requirements evolution by facilitating organizational and process agility. Flow down and distribution of requirements is controlled by Allocation Reports which breakdown numerical design objectives (weight, reliability, etc.) into quanta goals for each component area. Linked databases of design and verification requirements helps eliminate redundancy and potential mistakes inherent m separated systems. Another tool, the Architecture Design Description, is being used to control J-2X system architecture and effectively communicate configuration changes to those involved in the design process. But the proof is in successful program accomplishment. The SE is the methodology being used to meet the challenge of completing J-2X engine certification 2 years ahead of any engine program ever developed at PWR. The Ares I SE system of systems has delivered according to expectations thus far. All major design reviews (SRR. PDR, CDR) have been successfully conducted to satisfy overall program objectives using SE as the basis for accomplishment. The paper describes SE tools and techniques utilized to achieve this success.

Kelly, William↗
NASA NTRSDate not supplied

Evaluating Long-Term Changes in Atmospheric Ozone

Ozonesondes have made inexpensive, accurate measurements of ozone from the ground to 30km for more than 50 years. The data are used extensively for trend analyses and for evaluation of satellite and model data products, and are also part of climatologies that are used as a priori data for satellite retrievals. They are essential as a transfer standard when merging shorter satellite-derived time series, and are the most important source of trend-quality long-term records below about 18 km. The importance of long-term ozonesonde records as a stable reference has led to increased attention to quantifying uncertainties and changes in ozonesonde data. Based on past intercomparison data, ECC sondes show a modest (~1-5%) high bias with respect to UV-absorption measurements (including MOZAIC-IAGOS) in the troposphere, with an uncertainty of 5%, but no evidence of a change with time. Other sonde types show an increase of 5-20% in sensitivity to tropospheric ozone from 1970-1995. Agreement in the stratosphere is much better. In the past 20 years ozonesonde precision has improved by a factor of 2, primarily through the adoption of strict standard operating procedures. In addition, many of the most important ozonesonde records have been re-evaluated and corrected, and detailed estimates made of their biases and uncertainties. Corrections to historical data for known issues may reduce biases but simultaneously introduce additional uncertainties. The uncertainty budget for the ozone partial pressure reading has contributions from stoichiometry, cell background current, pump efficiency and temperature, sensing solution type and volume. The much-discussed “background current” has recently been shown to be primarily related to reaction stoichiometry. Several quality assurance issues remain, but are tractable problems that can be addressed with further research. This will be required if the present goal of better than 5% overall uncertainty throughout the global ozonesonde network is to be achieved. Finally, the importance of regular sonde intercomparisons, employing UV photometers traceable to the modern UV-absorption standard, and of regular comparison of operational station records with multiple satellite sensors, is emphasized.

Ozonesondes↗
NASA NTRS2016

The Plant Phenology Monitoring Design for the National Ecological Observatory Network

Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer-based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing, and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental-scale inference about the status, trends, causes, and ecological consequences of phenological change.

phenology↗
NASA NTRS1998

Results of the NY-Alesund ozone measurements intercomparison NAOMI

Worldwide, about ten Differential Absorption Lidars are used for long-term monitoring of stratospheric ozone. These systems are an important component of the Network for the Detection of Stratospheric Change. Although DIALs are self-calibrating in principle, regular intercomparisons with other ozone-lidars, microwave radiometers or ozone-sondes are highly desirable to ensure high data quality at a well known level. The Network for the Detection of Stratospheric Change (NDSC) validation policy suggests that such intercomparisons be "blind", meaning all participants submit their data to an impartial referee, without seeing results from the other participants. Here we report on the "blind" intercomparison taking place from January 20th to February 10th 1998 at Ny-Alesund, Spitsbergen (78.92 deg N, 11.95 deg E). Participating groups were from the Alfred Wegener Institute, Potsdam, operating the NDSC DIAL system at Ny-Alesund, from the University of Bremen operating the NDSC microwave radiometer for ozone profiling at Ny-Alesund, and the NASA Goddard Space Flight Center group with the "NDSC travelling standard" STROZ-LITE. The first author acted as the impartial referee. Also used for the intercomparison were data from ECC-6A/Vaisala RS80 ozone sondes routinely launched at Ny-Alesund by the AWI group. A 1% KI solution (3 ml) and the 1986 ECC pump correction (1.092 at 5 hPa) are used. The ECC-data were available to all participants during the campaign and thus were not "blind". Table 1 summarizes the expected performance of the instruments participating in the ozone intercomparison reported in this paper.

Steinbrecht, W.↗
NASA NTRS2009

Wireless Avionics Packet to Support Fault Tolerance for Flight Applications

In this protocol and packet format, data traffic is monitored by all network interfaces to determine the health of transmitter and subsystems. When failures are detected, the network inter face applies its recover y policies to provide continued service despite the presence of faults. The protocol, packet format, and inter face are independent of the data link technology used. The current demonstration system supports both commercial off-the-shelf wireless connections and wired Ethernet connections. Other technologies such as 1553 or serial data links can be used for the network backbone. The Wireless Avionics packet is divided into three parts: a header, a data payload, and a checksum. The header has the following components: magic number, version, quality of service, time to live, sending transceiver, function code, payload length, source Application Data Interface (ADI) address, destination ADI address, sending node address, target node address, and a sequence number. The magic number is used to identify WAV packets, and allows the packet format to be updated in the future. The quality of service field allows routing decisions to be made based on this value and can be used to route critical management data over a dedicated channel. The time to live value is used to discard misrouted packets while the source transceiver is updated at each hop. This information is used to monitor the health of each transceiver in the network. To identify the packet type, the function code is used. Besides having a regular data packet, the system supports diagnostic packets for fault detection and isolation. The payload length specifies the number of data bytes in the payload, and this supports variable-length packets in the network. The source ADI is the address of the originating interface. This can be used by the destination application to identify the originating source of the packet where the address consists of a subnet, subsystem class within the subnet, a subsystem unit, and the local ADI number. The destination ADI is used to route the packet to its ultimate destination. At each hop, the sending interface uses the destination address to determine the next node for the data. The sending node is the node address of the interface that is broadcasting the packet. This field is used to determine the health of the subsystem that is sending the packet. In the case of a packet that traverses several intermediate nodes, it may be the node address of the intermediate node. The target node is the node address of the next hop for the packet. It may be an intermediate node, or the final destination for the packet. The sequence number is used to identify duplicate packets. Because each interface has multiple transceivers, the same packet will appear at both receivers. The sequence number allows the interface to correlate the reception and forward a single, unique packet for additional processing. The subnet field allows data traffic to be partitioned into segregated local networks to support large networks while keeping each subnet at a manageable size. This also keeps the routing table small enough so routing can be done by a simple table lookup in an FPGA device. The subsystem class identifies members of a set of redundant subsystems, and, in a hot standby configuration, all members of the subsystem class will receive the data packets. Only the active subsystem will generate data traffic. Specific units in a class of redundant units can be identified and, if the hot standby configuration is not used, packets will be directed to a specific subsystem unit.

Block, Gary L.↗
NASA NTRSDate not supplied

Delay Tolerant, Radio Frequency Identification (RFID )-enabled Sensing

Radio Frequency Identification (RFID) technology offers a completely passive method to transmit fixed data sequences from an RFID tag, which typically doesn’t have its own power supply, to an interrogator. Radio frequency (RF) energy harvested from the interrogator is rectified by the tag and used to charge an integrated circuit (IC). The IC then modulates the received signal with the data stored on the tag and reflects the energy back to the interrogator. RFID has seen great proliferation in terrestrial inventory management applications, and it has recently made the jump to spaceflight applications onboard the International Space Station, augmenting an existing optical bar‐code infrastructure for tracking supplies. A number of advanced automated logistics management (ALM) concepts employing RFID are currently being developed and evaluated, including so‐called “smart” shelves, cubbies, and trash receptacles using low‐power, embeddable RFID interrogators. An infrastructure where both crew members and robotic assistants, such as autonomous free flyers, are similarly equipped with small RFID interrogators seems likely. It therefore behooves us to consider extending this infrastructure beyond ALM to applications such as low power, embedded sensing. Typically, data on an RFID tag can only be written by an RFID interrogator, using interrogator energy. In recent years, however, a few efforts have focused on using that energy to drive data acquisition from the tag IC, allowing the tag to modify its stored data sequence with sensor data before replying to an interrogator. In this way, the tag can act as a completely passive sensing device. One problem exists with this approach, however: the tag cannot gather data when an interrogator is not present. Thus, strictly passive RFID sensing tags cannot gather data at regular intervals, in the manner of a typical wireless sensor network, without careful, and impractical, planning of mobile interrogator movements. To address this shortcoming, we look to a recent advance in RFID technology which allows an external microprocessor to power the tag IC and write directly into its RFID memory using a wired serial interface. In this paradigm, data gathering is driven by a small, on‐board power supply (using batteries or harvested energy), and data transfer is provided passively through the RFID interrogation service. Since communication typically consumes the lion’s share of power in WSNs, such a technique has the potential to enable extremely long‐lived, embedded wireless sensing when used with extremely low‐current microcontrollers. Since the communication channel is only open when an interrogator is present and actively interrogating the RFID sensing tag, transport of periodically‐sampled sensor data presents itself as a delay/disruption‐tolerant networking (DTN) problem. In this paper, we present the design of a DTN‐like overlay on the common EPC Global, Class 1, Generation 2 RFID standard. This overlay allows seamless, guaranteed data transfer using the EPC Global protocol, supporting extremely low‐power, embedded sensing using an infrastructure likely to be already in place for ALM applications. We evaluate a prototype implementation of a complete end‐to‐end system using a robotic RFID interrogation agent, and we present future directions for the development of this sensing technique.

Raymond S. Wagner↗
NASA NTRSDate not supplied

Deep Space navigation for the BioSentinel spacecraft science orbit

BioSentinel is an astrobiology small spacecraft mission. The payload consists of two parts, the first has optical and microfluidics sensors, and the second is a Linear Energy Transfer spectrometer that has the objective to measure deep space radiation from events such as coronal mass ejections. The goal of the mission is to observe potential DNA damage due to the radiation in heliocentric space on the living organism Saccharomyces cerevisiae, which is a budding yeast. Two types of this living organism are included in the payload. The first is a natural type that is more radiation tolerant, while the second is a mutant strain that has a deficiency in a gene that allows DNA repair once damage occurs. The impact caused by the radiation on the DNA is compared to an identical sample aboard the International Space Station, as well as another identical sample at a laboratory on the ground. The BioSentinel mission consists of a 6U CubeSat currently ,as of January 2024, active in heliocentric orbit. The spacecraft was launched aboard the first SLS flight as part of the Artemis-I campaign in November 2022. After successful deployment from the launch vehicle, it performed a lunar flyby with an altitude of 406 km. The delta-V imparted by the flyby provided the necessary energy to achieve a heliocentric orbit, in an Earth-trailing pattern. The navigation analysis consisted of a Kalman-filter that utilized data from the Deep Space Network and the ESA Estrack network. All those antennas were needed since the Artemis-1 campaign included the deployment of several other cubesats, therefore the scheduling process required more antenna assets than usual due to simultaneous demands from various missions. The processed tracking data was later also refined with a smoother in order to obtain a more accurate solution. The type of tracking data included TCP, Sequential Range, Doppler and Range formats. The solar radiation pressure coefficient, as well as the delta-V from the deployment and the flyby were modeled to obtain suitable solutions that could decrease the position and velocity uncertainties at several steps along the mission concept of operations. The final product each time resulted in updated ephemeris files that were used by the mission and the antenna networks as the mission progressed. Once in the final science orbit, the utilized antennas are only from the DSN network and the data format is bounded to just TCP. Regular orbit determination is performed, every two weeks. The spacecraft is in a nominal well-known orbit, performing regular operations. This paper includes an analysis of the final science orbit, the techniques and procedures utilized to perform orbit determination and a description of the overall navigation campaign produced during the mission and, more specifically, during the final science operations in Deep Space.

BioSentinel↗