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

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NASA NTRS2005

Launch Commit Criteria Monitoring Agent

The Spaceport Processing Systems Branch at NASA Kennedy Space Center has developed and deployed a software agent to monitor the Space Shuttle's ground processing telemetry stream. The application, the Launch Commit Criteria Monitoring Agent, increases situational awareness for system and hardware engineers during Shuttle launch countdown. The agent provides autonomous monitoring of the telemetry stream, automatically alerts system engineers when predefined criteria have been met, identifies limit warnings and violations of launch commit criteria, aids Shuttle engineers through troubleshooting procedures, and provides additional insight to verify appropriate troubleshooting of problems by contractors. The agent has successfully detected launch commit criteria warnings and violations on a simulated playback data stream. Efficiency and safety are improved through increased automation.

Semmel, Glenn S.↗

NASA NTRS2012

In-Trail Procedure Air Traffic Control Procedures Validation Simulation Study

In August 2007, Airservices Australia (Airservices) and the United States National Aeronautics and Space Administration (NASA) conducted a validation experiment of the air traffic control (ATC) procedures associated with the Automatic Dependant Surveillance-Broadcast (ADS-B) In-Trail Procedure (ITP). ITP is an Airborne Traffic Situation Awareness (ATSA) application designed for near-term use in procedural airspace in which ADS-B data are used to facilitate climb and descent maneuvers. NASA and Airservices conducted the experiment in Airservices simulator in Melbourne, Australia. Twelve current operational air traffic controllers participated in the experiment, which identified aspects of the ITP that could be improved (mainly in the communication and controller approval process). Results showed that controllers viewed the ITP as valid and acceptable. This paper describes the experiment design and results.

Chartrand, Ryan C.↗

NASA NTRS2009

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.

Ferrell, Bob↗

NASA NTRSDate not supplied

Development and Demonstration of a Digital NDE Pipeline for Streamlined Analysis of Ultrasonic Data

Currently when nondestructive evaluation (NDE) is performed on composite structures, the results, although recorded digitally are often manually interpreted and indicated (drawn) on the part being inspected by hand. Following this, a determination must be made on how to disposition that part. This decision could be based on engineering guidelines and best practices, rule-of-thumb, expert opinion or finite-element analysis of the part with some approximate representation of the damage. In order to streamline this process for ultrasonic inspection an effort was undertaken as part of NASA Advanced Composites Project to develop the Digital NDE Pipeline. The Digital NDE Pipeline is an integrated tool suite and associated framework that streamlines the inspection and defect disposition process through model-assisted inspection optimization, automated defect analysis of NDE data, and mapping of NDE data into finite element analysis software. This paper will provide an overview of the Digital NDE Pipeline and provide details of the individual tools developed along with the results of applying these tools to a demonstration test case.

Composites↗

DOE OSTI2014

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency and the robustness. The automatic segmentation results with the original average Dice similarity coefficient of 0.78 were improved to 0.865–0.872 after conducting 55–59 interactions by using the proposed method, where each editing procedure took less than 3 s. In addition, the proposed method obtained the most consistent editing results with respect to different user interactions, compared to other methods. Conclusions: The proposed method obtains robust editing results with few interactions for various wrong segmentation cases, by selecting the location-adaptive features and further imposing the manifold regularization. The authors expect the proposed method to largely reduce the laborious burdens of manual editing, as well as both the intra- and interobserver variability across clinicians.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗

NASA NTRS1979

An electric propulsion long term test facility

An existing test facility was modified to provide for extended testing of multiple electric propulsion thruster subsystems. A program to document thruster subsystem characteristics as a function of time is currently in progress. The facility is capable of simultaneously operating three 2.7-kW, 30-cm mercury ion thrusters and their power processing units. Each thruster is installed via a separate air lock so that it can be extended into the 7m x 10m main chamber without violating vacuum integrity. The thrusters exhaust into a 3m x 5m frozen mercury target. An array of cryopanels collect sputtered target material. Power processor units are tested in an adjacent 1.5m x 2m vacuum chamber or accompanying forced convection enclosure. The thruster subsystems and the test facility are designed for automatic unattended operation with thruster operation computer controlled. Test data are recorded by a central data collection system scanning 200 channels of data a second every two minutes. Results of the Systems Demonstration Test, a short shakedown test of 500 hours, and facility performance during the first year of testing are presented.

Trump, G.↗

NASA NTRS1989

A distributed program composition system

A graphical technique for creating distributed computer programs is investigated and a prototype implementation is described which serves as a testbed for the concepts. The type of programs under examination is restricted to those comprising relatively heavyweight parts that intercommunicate by passing messages of typed objects. Such programs are often presented visually as a directed graph with computer program parts as the nodes and communication channels as the edges. This class of programs, called parts-based programs, is not well supported by existing computer systems; much manual work is required to describe the program to the system, establish the communication paths, accommodate the heterogeneity of data types, and to locate the parts of the program on the various systems involved. The work described solves most of these problems by providing an interface for describing parts-based programs in this class in a way that closely models the way programmers think about them: using sketches of diagraphs. Program parts, the computational modes of the larger program system are categorized in libraries and are accessed with browsers. The process of programming has the programmer draw the program graph interactively. Heterogeneity is automatically accommodated by the insertion of type translators where necessary between the parts. Many decisions are necessary in the creation of a comprehensive tool for interactive creation of programs in this class. Possibilities are explored and the issues behind such decisions are presented. An approach to program composition is described, not a carefully implemented programming environment. However, a prototype implementation is described that can demonstrate the ideas presented.

Brown, Robert L.↗

NASA NTRS1992

Ionospheric gravity wave measurements with the USU dynasonde

A method for the measurement of ionospheric Gravity Wave (GW) using the USU Dynasonde is outlined. This method consists of a series of individual procedures, which includes functions for data acquisition, adaptive scaling, polarization discrimination, interpolation and extrapolation, digital filtering, windowing, spectrum analysis, GW detection, and graphics display. Concepts of system theory are applied to treat the ionosphere as a system. An adaptive ionogram scaling method was developed for automatically extracting ionogram echo traces from noisy raw sounding data. The method uses the well known Least Mean Square (LMS) algorithm to form a stochastic optimal estimate of the echo trace which is then used to control a moving window. The window tracks the echo trace, simultaneously eliminating the noise and interference. Experimental results show that the proposed method functions as designed. Case studies which extract GW from ionosonde measurements were carried out using the techniques described. Geophysically significant events were detected and the resultant processed results are illustrated graphically. This method was also developed for real time implementation in mind.

Berkey, Frank T.↗

NASA NTRS2008

ATHLETE's Feet: Mu1ti-Resolution Planning for a Hexapod Robot

ATHLETE is a large six-legged tele-operated robot. Each foot is a wheel; travel can be achieved by walking, rolling, or some combination of the two. Operators control ATHLETE by selecting parameterized commands from a command dictionary. While rolling can be done efficiently with a single command, any motion involving steps is cumbersome - walking a few meters through difficult terrain can take hours. Our goal is to improve operator efficiency by automatically generating sequences of motion commands. There is increasing uncertainty regarding ATHLETE s actual configuration over time and decreasing quality of terrain data farther away from the current position. This, combined with the complexity that results from 36 degrees of kinematic freedom, led to an architecture that interleaves planning and execution at multiple levels, ranging from traditional configuration space motion planning algorithms for immediate moves to higher level task and path planning algorithms for overall travel. The modularity of the architecture also simplifies the development process and allows the operator to interact with and control the system at varying levels of autonomy depending on terrain and need.

Smith, Tristan B.↗

NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗

DOE OSTI2015

A method for volumetric imaging in radiotherapy using single x-ray projection

Purpose: It is an intriguing problem to generate an instantaneous volumetric image based on the corresponding x-ray projection. The purpose of this study is to develop a new method to achieve this goal via a sparse learning approach. Methods: To extract motion information hidden in projection images, the authors partitioned a projection image into small rectangular patches. The authors utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients was built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are then further used to generate a motion vector field and hence a volumetric image. The authors have also proposed an intensity baseline correction method based on the partitioned projection, in which the first and the second moments of pixel intensities at a patch in a simulated projection image are matched with those in a measured one via a linear transformation. The proposed method has been validated in both simulated data and real phantom data. Results: The algorithm is able to identify patches that contain relevant motion information such as the diaphragm region. It is found that an intensity baseline correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced the prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning was not used, and the 95th percentile error for the predicted motion vector was reduced from 2.40 to 0.92 mm. In the phantom case with a regular tumor motion, the predicted tumor trajectory was successfully reconstructed with a 0.82 mm error for tumor center localization compared to a 1.66 mm error without using the sparse learning method. When the tumor motion was driven by a real patient breathing signal with irregular periods and amplitudes, the average tumor center error was 0.6 mm. The algorithm robustness with respect to sparsity level, patch size, and presence or absence of diaphragm, as well as computation time, has also been studied. Conclusions: The authors have developed a new method that automatically identifies motion information from an x-ray projection, based on which a volumetric image is generated.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Data Link Summary for Peer Review.

New generations of high-temperature (HT) sensors and electronics are enabling increased measurement speed and accuracy allowing collection of more accurate and relevant data by downhole tools. Unfortunately, this increased capability is often not realized due to the bottleneck in the uplink data transmission rates due to poor signal characteristics of HT wireline. The objective of this project is to enable the high transmission rate of raw data from downhole tools such as acoustic logging tools and seismic measurement devices to minimize the need for downhole signal processing. To achieve this objective, Sandia has undertaken the effort to develop an asymmetric high-temperature (HT), highspeed data link system for downhole tools capable of operating at temperatures of 210°C while taking advantage of existing wireline transmission channels. Current data rates over HT single-conductor wireline are limited to approximately 200 kbps. The goal system will be capable of transmitting data from the tool to the surface (uplink) at rates of > 1Mbps over 5,000 feet of single-conductor wireline as well as automatically adapt the data rate to the longer wirelines by adapting modern telecommunications techniques to operate on high temperature electronics. The data rate from the surface to the tool (downlink) will be significantly smaller but sufficient for command and control functions. While 5,000 feet of cable is the benchmark for this effort, improvements apply to all lengths of cable.

47 OTHER INSTRUMENTATION↗

NASA NTRS1976

Polarization compensator for optical communications

An optical data communication system is provided whereby two orthogonal polarization states of a light beam carrier correspond to digital states. In such a system, automatic polarization compensation is provided by applying a dither modulating voltage to a cell exhibiting the electro-optic effect. The cell controls the relative phase of electric field components of an input light beam enabling the dither frequency component of the difference of the instantaneous powers in the two polarization states to be coherently detected. A signal derived from the coherent detection process is fed back to the cell via an integrator to form polarization bias compensating servo loop ot Type 1.

Fitzmaurice, M. W.↗

NASA NTRS1990

Automated extraction of knowledge for model-based diagnostics

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

Gonzalez, Avelino J.↗

DOE OSTI2020

Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗

NASA NTRS2013

Autonomous Control of Space Nuclear Reactors

Nuclear reactors to support future robotic and manned missions impose new and innovative technological requirements for their control and protection instrumentation. Long-duration surface missions necessitate reliable autonomous operation, and manned missions impose added requirements for failsafe reactor protection. There is a need for an advanced instrumentation and control system for space-nuclear reactors that addresses both aspects of autonomous operation and safety. The Reactor Instrumentation and Control System (RICS) consists of two functionally independent systems: the Reactor Protection System (RPS) and the Supervision and Control System (SCS). Through these two systems, the RICS both supervises and controls a nuclear reactor during normal operational states, as well as monitors the operation of the reactor and, upon sensing a system anomaly, automatically takes the appropriate actions to prevent an unsafe or potentially unsafe condition from occurring. The RPS encompasses all electrical and mechanical devices and circuitry, from sensors to actuation device output terminals. The SCS contains a comprehensive data acquisition system to measure continuously different groups of variables consisting of primary measurement elements, transmitters, or conditioning modules. These reactor control variables can be categorized into two groups: those directly related to the behavior of the core (known as nuclear variables) and those related to secondary systems (known as process variables). Reliable closed-loop reactor control is achieved by processing the acquired variables and actuating the appropriate device drivers to maintain the reactor in a safe operating state. The SCS must prevent a deviation from the reactor nominal conditions by managing limitation functions in order to avoid RPS actions. The RICS has four identical redundancies that comply with physical separation, electrical isolation, and functional independence. This architecture complies with the safety requirements of a nuclear reactor and provides high availability to the host system. The RICS is intended to interface with a host computer (the computer of the spacecraft where the reactor is mounted). The RICS leverages the safety features inherent in Earth-based reactors and also integrates the wide range neutron detector (WRND). A neutron detector provides the input that allows the RICS to do its job. The RICS is based on proven technology currently in use at a nuclear research facility. In its most basic form, the RICS is a ruggedized, compact data-acquisition and control system that could be adapted to support a wide variety of harsh environments. As such, the RICS could be a useful instrument outside the scope of a nuclear reactor, including military applications where failsafe data acquisition and control is required with stringent size, weight, and power constraints.

Merk, John↗