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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Micro-Inspector Avionics Module (MAM): A Self-Contained Low Power, Reconfigurable Avionics Platform for Small Spacecrafts and Instruments

This paper describes development of a radiation tolerant, low power, reconfigurable avionics module aimed at meeting the avionics needs of the JPL Micro-Inspector spacecraft. This module represents a complete avionics system, consisting of two PowerPC 405 CPUs embedded within a reconfigurable FPGA fabric of over 8 Million logic gates, 64MB of EDAC protected Flash storage and 128MB of EDAC protected DDR SDRAM or SDRAM memories, along with FPGA SEU mitigation logic, and all necessary power conversion. Processor SEU mitigation is achieved by running the two processors in a lock-step and compare configuration. All of these building blocks are integrated into a double sided circuit board that takes as little as 6 square inches of board space. This module can be embedded into a user system as part of a bigger circuit assembly or as a self contained module. This module is being developed as part of a JPL led Micro-Inspector Program, funded by NASA ESMD aimed at producing a 10Kg micro spacecraft.

configurable↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS1982

Detection and analysis of radio frequency lightning emissions

The feasibility study of detection of lightning discharges from a geosynchronous satellite requires adequate ground-based information regarding emission characteristics. In this investigation, a measurement system for collection of S-band emission data is set up and calibrated, and the operations procedures for rapid data collection during a storm activity developed. The system collects emission data in two modes; a digitized, high-resolution, short duration record stored in solid-state memory, and a continuous long-duration record on magnetic tape. Representative lightning flash data are shown. Preliminary results indicate appreciable RF emissions at 2 gHz from both the leader and return strokes portions of the cloud-to-ground discharge with strong peaks associated with the return strokes.

Jalali, F.↗
NASA NTRS1999

Portable flash lamp reflectance analyzer system and method

The system and method allow spectroscopic analysis of vegetation or the like without effects from changing sun and cloud conditions, undesired portions of the area of interest or atmospheric disturbances. The system (1) includes a light source (5) such as a xenon flash lamp, a telescope (7), a spectrometer (9), an analog/digital converter (11), a memory (13), a display (15), and an on-board microprocessor (17) or a port (19) for attachment to a laptop computer. The system is taken to an area of interest in the woods (step 41), the vegetation is illuminated from below (step 43) and data are taken (step 45).

Kalshoven, James Edward↗
NASA NTRS2006

Design of a Multi-Level/Analog Ferroelectric Memory Device

Increasing the memory density and utilizing the dove1 characteristics of ferroelectric devices is important in making ferroelectric memory devices more desirable to the consumer. This paper describes a design that allows multiple levels to be stored in a ferroelectric based memory cell. It can be used to store multiple bits or analog values in a high speed nonvolatile memory. The design utilizes the hysteresis characteristic of ferroelectric transistors to store an analog value in the memory cell. The design also compensates for the decay of the polarization of the ferroelectric material over time. This is done by utilizing a pair of ferroelectric transistors to store the data. One transistor is used as a reference to determine the amount of decay that has occurred since the pair was programmed. The second transistor stores the analog value as a polarization value between zero and saturated. The design allows digital data to be stored as multiple bits in each memory cell. The number of bits per cell that can be stored will vary with the decay rate of the ferroelectric transistors and the repeatability of polarization between transistors. It is predicted that each memory cell may be able to store 8 bits or more. The design is based on data taken from actual ferroelectric transistors. Although the circuit has not been fabricated, a prototype circuit is now under construction. The design of this circuit is different than multi-level FLASH or silicon transistor circuits. The differences between these types of circuits are described in this paper. This memory design will be useful because it allows higher memory density, compensates for the environmental and ferroelectric aging processes, allows analog values to be directly stored in memory, compensates for the thermal and radiation environments associated with space operations, and relies only on existing technologies.

MacLeod, Todd C.↗
NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRS2005

Reconfigurable Processing Module

To accommodate a wide spectrum of applications and technologies, NASA s Exploration System's Missions Directorate has called for reconfigurable and modular technologies to support future missions to the moon and Mars. In response, Langley Research Center is leading a program entitled Reconfigurable Scaleable Computing (RSC) that is centered on the development of FPGA-based computing resources in a stackable form factor. This paper details the architecture and implementation of the Reconfigurable Processing Module (RPM), which is the key element of the RSC system. The RPM is an FPGA-based, space-qualified printed circuit assembly leveraging terrestrial/commercial design standards into the space applications domain. The form factor is similar to, and backwards compatible with, the PCI-104 standard utilizing only the PCI interface. The size is expanded to accommodate the required functionality while still better than 30% smaller than a 3U CompactPCI(TradeMark)card and without the overhead of the backplane. The architecture is built around two FPGA devices, one hosting PCI and memory interfaces, and another hosting mission application resources; both of which are connected with a high-speed data bus. The PCI interface FPGA provides access via the PCI bus to onboard SDRAM, flash PROM, and the application resources; both configuration management as well as runtime interaction. The reconfigurable FPGA, referred to as the Application FPGA - or simply "the application" - is a radiation-tolerant Xilinx Virtex-4 FX60 hosting custom application specific logic or soft microprocessor IP. The RPM implements various SEE mitigation techniques including TMR, EDAC, and configuration scrubbing of the reconfigurable FPGA. Prototype hardware and formal modeling techniques are used to explore the performability trade space. These models provide a novel way to calculate quality-of-service performance measures while simultaneously considering fault-related behavior due to SEE soft errors.

Somervill, Kevin↗
NASA NTRS1973

Data storage, image tube type

Method and apparatus for the storage of digital or analog electrical signals are provided by a memory storage system employing a conventional vidicon tube. At the beginning of an operating cycle, the vidicon is conditioned to accept electrical data input by exposing its photosensitive target to a short, high intensity light flash. A first electron beam scan of the photosensitive surface then sets up a charge pattern on the photosensitive target. A second electron beam scan of the photosensitive surface by an unmodulated electron beam then develops an output signal across an output resistor by using capacitive currents. The conditioning and scanning steps are operated repetitively at high speed using conventional television camera scan, sync, and power supply circuitry to provide a low cost data storage system.

Lipoma, P. C.↗
NASA NTRSDate not supplied

Predicting Lightning Initiation using Deep Learning

Lightning occurrence presents safety challenges to people and property. The main challenge with lightning safety is that the majority of guidance is reactive. In other words, lightning has to have already occurred nearby before a person will respond and take shelter. Further, most injuries or fatalities occur as the storm approaches, or as it's moving away, when rainfall may not be present at the time of the flash. Thus, this project develops a physically-based deep learning model to produce lightning probabilities out to 15 minutes. The deep learning model combines a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network to capture both the spatial and temporal evolution of storms to predict the probability that lightning initiation will occur in the next 15 minutes. The model combines radar reflectivity, correlation coefficient and differential reflectivity to inferred storm hydrometer type and precipitation phase, which aids in the identification of electrification processes. The model is trained with data from the Geostationary Lightning Mapper (GLM), which is a near infrared sensor onboard the GOES-R series of satellites that measures optical brightness from lightning. This presentation will provide an overview of the project.

Andrew T White↗
NASA NTRS1993

The Stimulus Movement Effect: Allocation of Attention or Artifact?

In previous reports, including one by the author, learning has been shown to benefit by having discriminanda move rather than remain stationary. This stimulus movement effect might be attributed to several theoretical mechanisms, including attention, topological memory, and exposure duration. The series of experiments reported in this article was designed to Contrast these potential explanatory factors. Ten rhesus monkeys (Macaca mulatta) were tested on a variety of computerized tasks in which the stimuli remained stationary, flashed, or moved at systematically varied speeds. Performance was significantly best when the sample stimulus moved quickly and was poorest when the stimulus remained stationary. Further analysis of these data and other previously published data revealed that the distribution of the stimulus movement effect across trials supported an attention allocation interpretation.

Washburn, David A.↗
NASA NTRS1988

Development of a high-speed H-alpha camera system for the observation of rapid fluctuations in solar flares

A solid-state digital camera was developed for obtaining H alpha images of solar flares with 0.1 s time resolution. Beginning in the summer of 1988, this system will be operated in conjunction with SMM's hard X-ray burst spectrometer (HXRBS). Important electron time-of-flight effects that are crucial for determining the flare energy release processes should be detectable with these combined H alpha and hard X-ray observations. Charge-injection device (CID) cameras provide 128 x 128 pixel images simultaneously in the H alpha blue wing, line center, and red wing, or other wavelength of interest. The data recording system employs a microprocessor-controlled, electronic interface between each camera and a digital processor board that encodes the data into a serial bitstream for continuous recording by a standard video cassette recorder. Only a small fraction of the data will be permanently archived through utilization of a direct memory access interface onto a VAX-750 computer. In addition to correlations with hard X-ray data, observations from the high speed H alpha camera will also be correlated and optical and microwave data and data from future MAX 1991 campaigns. Whether the recorded optical flashes are simultaneous with X-ray peaks to within 0.1 s, are delayed by tenths of seconds or are even undetectable, the results will have implications on the validity of both thermal and nonthermal models of hard X-ray production.

Kiplinger, Alan L.↗
NASA NTRS1986

On the selection and evaluation of visual display symbology Factors influencing search and identification times

Three single-target visual search tasks were used to evaluate a set of cathode-ray tube (CRT) symbols for a helicopter situation display. The search tasks were representative of the information extraction required in practice, and reaction time was used to measure the efficiency with which symbols could be located and identified. Familiar numeric symbols were responded to more quickly than graphic symbols. The addition of modifier symbols, such as a nearby flashing dot or surrounding square, had a greater disruptive effect on the graphic symbols than did the numeric characters. The results suggest that a symbol set is, in some respects, like a list that must be learned. Factors that affect the time to identify items in a memory task, such as familiarity and visual discriminability, also affect the time to identify symbols. This analogy has broad implications for the design of symbol sets. An attempt was made to model information access with this class of display.

Remington, Roger↗
NASA NTRS2011

SpaceCube Demonstration Platform

A document discusses how the HST SM4 SpaceCube flight spare was modified to create an experiment called the Space- Cube Demonstration Platform (SC DP) for use on the MISSE7 Space Station payload (in collaboration with NRL). It is designed to serve as an on-orbit platform for demonstrating advanced fault tolerance technologies. A simple C&DH (command and data handling) system was developed for the Virtex4 FPGAs (field programmable gate arrays). Both Virtex4s on each SpaceCube run the same program, and both receive incoming telemetry. The rad-hard service FPGA performs simple error checking to verify that the incoming telemetry is valid. The SpaceCube framework was modified to allow for new program files to be sent from the ground, to be stored on the SpaceCube, and to be executed through ground commands. Each SpaceCube Virtex4 FPGA has resources set aside for experiments that are functionally isolated from the C&DH system. The experiments communicate to the C&DH system through a set of dual port memories, and this area is where the fault-tolerance experiments are executed. With the use of Xilinx commercial Virtex4 FX60 FPGAs, the fault tolerant framework allows the system to recover from radiation upsets that occur in the rad-soft parts (Virtex4 FPGA logic, embedded PPCs in Virtex4 FPGAs, SDRAM and Flash), the C&DH system that runs simultaneously on both Virtex4 FPGAs that uses a robust telemetry packet structure, checksums, and the rad-hard service FPGA to validate incoming telemetry. The ability to be reconfigured from the ground while in orbit is a novel benefit, as well as is the onboard compression capabilities that allow compressed files from the ground to be uploaded to the SpaceCube.

Espinosa, Daniel↗
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗