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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Measurement of wind near the surface

A survey is presented of instrumentation suitable for measurement of wind near the surface of the earth by using in-situ techniques and further restricted to sensors that are operational. In this case, a sensor is deemed to be operational if it is commercially available. There is no discussion here of the systems that might be used to acquire, process, display, and store the sensor data. It is assumed that some sort of automatic data logging equipment would be used. Without special requirements such as need for high frequency response, low power consumption, etc., this treatment must be quite general and provides little detail. Also, without special restrictions, emphasis must be placed on conventional sensors that provide the bulk of wind data today.

Brock, Fred V.↗
NASA NTRS2014

Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond

With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.

solar chromospheric activity↗
NASA NTRSDate not supplied

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS2018

JSC OCFO Cloud Integrated Budget Analytical Toolbox

The JSC OCFO analyst's job function relies heavily on analytics to provide every directorate customer with quality service in managing their respective budgets. This technology, configured on the Salesforce cloud platform, enhances the JSC OCFO customer user interface, provides state of the art data analytics and statistics, reduces IT costs, increases productivity, eliminates duplicate entry and errors, and utilizes machine learning and artificial intelligence to process day to day transactional tasks automatically without analyst manipulation. The unique configuration of the COTS technology allows for highly advanced analysis and reporting of government budget data. This provides the ability for complex budgets to be fully understood in detail by non-budget organizations and eliminates time consuming analysis on the OCFO analyst's behalf.

Robertson, Arden↗
NASA NTRSDate not supplied

Three-Dimensional Reconstruction of Defects and Structures in Additively Manufactured Parts with Automated Serial Sectioning

Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with complex components for space flight applications. Laser powderbed fusion (L-PBF) is a commonly used AM technology due to the ability to produce complex parts with fine feature resolution in a wide variety of alloys and applications. L-PBF, like many other manufacturing processes, can produce minor flaws in parts when in nominal operation as well as process-escape defects when process abnormalities occur. The effects of the flaws and methods of detecting the flaws are a subject of interest to understand the difficulties in detecting these flaws with current technology and how much risk the flaws or defects pose to potential flight parts. Using a RoboMet.3D automated serial sectioning system, seeded defects as well as minor process flaws can be imaged and reconstructed in three dimensions to compare to non-destructive evaluation (NDE) techniques, such as x-ray computed tomography (CT), neutron CT, and in-situ monitoring. The RoboMet automates the metallography process by automatically grinding, polishing, and imaging samples in a single system and providing the control data for NDE comparisons to know the real size of defects built into coupons. These comparisons provide an understanding behind the technological limitations of the NDE techniques for different alloys. The same serial sectioning methods have also been utilized to characterize the surfaces of parts to reconstruct the surfaces and take measurements of internal features not easily examined with non-destructive methods. Using the RoboMet, fine lattice structures built with L-PBF have been characterized to determine the actual thicknesses of struts and density of the lattice structures. These structures have been used as finer build supports for the L-PBF process, designs for fine catalysts, and other design considerations for small components. The RoboMet data helps to inform the modeling and design efforts around these fine components.

additive manufacturing↗
NASA NTRS1992

Flight deck benefits of integrated data link communication

A fixed-base, piloted simulation study was conducted to determine the operational benefits that result when air traffic control (ATC) instructions are transmitted to the deck of a transport aircraft over a digital data link. The ATC instructions include altitude, airspeed, heading, radio frequency, and route assignment data. The interface between the flight deck and the data link was integrated with other subsystems of the airplane to facilitate data management. Data from the ATC instructions were distributed to the flight guidance and control system, the navigation system, and an automatically tuned communication radio. The co-pilot initiated the automation-assisted data distribution process. Digital communications and automated data distribution were compared with conventional voice radio communication and manual input of data into other subsystems of the simulated aircraft. Less time was required in the combined communication and data management process when data link ATC communication was integrated with the other subsystems. The test subjects, commercial airline pilots, provided favorable evaluations of both the digital communication and data management processes.

Waller, Marvin C.↗
NASA NTRS1994

Planning applications in image analysis

We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

Boddy, Mark↗
NASA NTRS1995

Automatic Radiated Susceptibility Test System for Payload Equipment

An automatic radiated susceptibility test system (ARSTS) was developed for NASA Lewis Research Center's Electro-magnetic Interference laboratory. According to MSFC-SPEC 521B, any electrical or electronic equipment that will be transported by the spacelab and space shuttle must be tested for susceptibility to electromagnetic interference. This state-of-the-art automatic test system performs necessary calculations; analyzes, processes, and records a great quantity of measured data; and monitors the equipment being tested in real-time and with minimal user intervention. ARSTS reduces costly test time, increases test accuracy, and provides reliable test results.

Ngo, Hoai T.↗
NASA NTRS2005

Control Method for Video Guidance Sensor System

A method is provided for controlling operations in a video guidance sensor system wherein images of laser output signals transmitted by the system and returned from a target are captured and processed by the system to produce data used in tracking of the target. Six modes of operation are provided as follows: (i) a reset mode; (ii) a diagnostic mode; (iii) a standby mode; (iv) an acquisition mode; (v) a tracking mode; and (vi) a spot mode wherein captured images of returned laser signals are processed to produce data for all spots found in the image. The method provides for automatic transition to the standby mode from the reset mode after integrity checks are performed and from the diagnostic mode to the reset mode after diagnostic operations are commands is permitted only when the system is in the carried out. Further, acceptance of reset and diagnostic standby mode. The method also provides for automatic transition from the acquisition mode to the tracking mode when an acceptable target is found.

Richard T Howard↗
NASA NTRS2005

Control method for video guidance sensor system

A method is provided for controlling operations in a video guidance sensor system wherein images of laser output signals transmitted by the system and returned from a target are captured and processed by the system to produce data used in tracking of the target. Six modes of operation are provided as follows: (i) a reset mode; (ii) a diagnostic mode; (iii) a standby mode; (iv) an acquisition mode; (v) a tracking mode; and (vi) a spot mode wherein captured images of returned laser signals are processed to produce data for all spots found in the image. The method provides for automatic transition to the standby mode from the reset mode after integrity checks are performed and from the diagnostic mode to the reset mode after diagnostic operations are carried out. Further, acceptance of reset and diagnostic commands is permitted only when the system is in the standby mode. The method also provides for automatic transition from the acquisition mode to the tracking mode when an acceptable target is found.

Howard, Richard T.↗
NASA NTRS2011

CropEx Web-Based Agricultural Monitoring and Decision Support

CropEx is a Web-based agricultural Decision Support System (DSS) that monitors changes in crop health over time. It is designed to be used by a wide range of both public and private organizations, including individual producers and regional government offices with a vested interest in tracking vegetation health. The database and data management system automatically retrieve and ingest data for the area of interest. Another stores results of the processing and supports the DSS. The processing engine will allow server-side analysis of imagery with support for image sub-setting and a set of core raster operations for image classification, creation of vegetation indices, and change detection. The system includes the Web-based (CropEx) interface, data ingestion system, server-side processing engine, and a database processing engine. It contains a Web-based interface that has multi-tiered security profiles for multiple users. The interface provides the ability to identify areas of interest to specific users, user profiles, and methods of processing and data types for selected or created areas of interest. A compilation of programs is used to ingest available data into the system, classify that data, profile that data for quality, and make data available for the processing engine immediately upon the data s availability to the system (near real time). The processing engine consists of methods and algorithms used to process the data in a real-time fashion without copying, storing, or moving the raw data. The engine makes results available to the database processing engine for storage and further manipulation. The database processing engine ingests data from the image processing engine, distills those results into numerical indices, and stores each index for an area of interest. This process happens each time new data is ingested and processed for the area of interest, and upon subsequent database entries, the database processing engine qualifies each value for each area of interest and conducts a logical processing of results indicating when and where thresholds are exceeded. Reports are provided at regular, operator-determined intervals that include variances from thresholds and links to view raw data for verification, if necessary. The technology and method of development allow the code base to easily be modified for varied use in the real-time and near-real-time processing environments. In addition, the final product will be demonstrated as a means for rapid draft assessment of imagery.

Harvey. Craig↗
NASA NTRS2011

CloudSat Reflectivity Data Visualization Inside Hurricanes

We have presented methods to rapidly produce visualization and outreach products from CloudSat data for science and the media These methods combine data from several sources in the product generation process In general, the process can be completely automatic, producing products and notifying potential users

remote science↗
NASA NTRS2017

Reducing V&V Cost of Flight Critical Systems: Myth or Reality?

This paper presents an overview of NASA research program on the V&V of flight critical systems. Five years ago, NASA started an effort to reduce the cost and possibly increase the effectiveness of V&V for flight critical systems. It is the right time to take a look back and realize what progress has been made. This paper describes our overall approach and the tools introduced to address different phases of the software lifecycle. For example, we have improved testing by developing a statistical learning approach tor defining test cases. The tool automatically identifies possible unsafe conditions by analyzing outliers in output data; using an iterative learning process, it can then generate more test cases that represent potentially unsafe regions of operation. At the code level, we have developed and made available as open source a static analyzer for C and C++ programs called IKOS. We have shown that IKOS is very precise in the analysis of embedded C programs (very few false positives) and a bit less for regular C and C++ code. At the design level, in collaboration with our NRA partners, we have developed a suite of analysis tools for Simulink models. The analysis is done in a compositional framework for scalability.

Brat, Guillaume P.↗
NASA NTRS1979

Real-time data acquisition system for the NASA Langley transonic dynamics tunnel

The hardware configuration of the Transonic Dynamics Wind Tunnel Data Acquisition System (DAS) which consists of an analog front end that can process up to 260 channels of data is presented. The DAS also has a multi-channel analog-to-digital subsystem that can process up to 50,000 samples of data per sec, and a digital computer with standard and nonstandard devices, with graphics capability. The software configuration of the DAS and complex hardware/software interfaces are described, which can provide automatic amplifier gain and offset adjustment for each data channel. Finally, a summary of specific DAS applications is given including the real-time processing of dynamic deflection data, unsteady pressure measurements, and flutter and buffet data.

Cole, P. H.↗
NASA NTRS1976

Computer simulation and evaluation of edge detection algorithms and their application to automatic path selection

The construction of an autonomous roving vehicle requires the development of complex data-acquisition and processing systems, which determine the path along which the vehicle travels. Thus, a vehicle must possess algorithms which can (1) reliably detect obstacles by processing sensor data, (2) maintain a constantly updated model of its surroundings, and (3) direct its immediate actions to further a long range plan. The first function consisted of obstacle recognition. Obstacles may be identified by the use of edge detection techniques. Therefore, the Kalman Filter was implemented as part of a large scale computer simulation of the Mars Rover. The second function consisted of modeling the environment. The obstacle must be reconstructed from its edges, and the vast amount of data must be organized in a readily retrievable form. Therefore, a Terrain Modeller was developed which assembled and maintained a rectangular grid map of the planet. The third function consisted of directing the vehicle's actions.

Longendorfer, B. A.↗