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At least 1,441 records · Page 80

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NASA NTRS2016

An Assessment of Nondestructive Evaluation Capability for Complex Additive Manufacturing Aerospace Components

The primary focus of this work is to investigate some of the fundamental relationships between processing, mechanical testing, materials characterization, and NDE for additively manufactured (AM) components using the powder bed fusion direct melt laser sintered process. The goal is to understand the criticality of defects unique to the AM process and then how conventional nondestructive evaluation methods as well as some of the more non-traditional methods such as computed tomography, are effected by the AM material. Specific defects including cracking, porosity and partially/unfused powder will be addressed. Besides line-of-site NDE, as appropriate these inspection capabilities will be put into the context of complex AM geometries where hidden features obscure, or inhibit traditional NDE methods.

Walker, James↗

NASA NTRS2016

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

model parameterisation↗

NASA NTRS2016

Electric Potential and Electric Field Imaging with Applications

The technology and techniques for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field may be used for (illuminating) volumes to be inspected with EFI. The baseline sensor technology, electric field sensor (e-sensor), and its construction, optional electric field generation (quasistatic generator), and current e-sensor enhancements (ephemeral e-sensor) are discussed. Demonstrations for structural, electronic, human, and memory applications are shown. This new EFI capability is demonstrated to reveal characterization of electric charge distribution, creating a new field of study that embraces areas of interest including electrostatic discharge mitigation, crime scene forensics, design and materials selection for advanced sensors, dielectric morphology of structures, inspection of containers, inspection for hidden objects, tether integrity, organic molecular memory, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗

NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

NASA NTRS2016

Impact and Crashworthiness Characteristics of Venera Type Landers for Future Venus Missions

In this paper an in-depth investigation of the structural design of the Venera 9-14 landers is explored. A complete reverse engineering of the Venera lander was required. The lander was broken down into its fundamental components and analyzed. This provided in-sights into the hidden features of the design. A trade study was performed to find the sensitivity of the lander's overall mass to the variation of several key parameters. For the lander's legs, the location, length, configuration, and number are all parameterized. The size of the impact ring, the radius of the drag plate, and other design features are also parameterized, and all of these features were correlated to the change of mass of the lander. A multi-fidelity design tool used for further investigation of the parameterized lander was developed. As a design was passed down from one level to the next, the fidelity, complexity, accuracy, and run time of the model increased. The low-fidelity model was a highly nonlinear analytical model developed to rapidly predict the mass of each design. The medium and high fidelity models utilized an explicit finite element framework to investigate the performance of various landers upon impact with the surface under a range of landing conditions. This methodology allowed for a large variety of designs to be investigated by the analytical model, which identified designs with the optimum structural mass to payload ratio. As promising designs emerged, investigations in the following higher fidelity models were focused on establishing their reliability and crashworthiness. The developed design tool efficiently modelled and tested the best concepts for any scenario based on critical Venusian mission requirements and constraints. Through this program, the strengths and weaknesses inherent in the Venera-Type landers were thoroughly investigated. Key features identified for the design of robust landers will be used as foundations for the development of the next generation of landers for future exploration missions to Venus.

Schroeder, Kevin↗

NASA NTRS2017

Detection and Use of Load and Gage Output Repeats of Wind Tunnel Strain-Gage Balance Data

Criteria are discussed that may be used for the detection of load and gage output repeats of wind tunnel strain-gage balance data. First, empirical thresholds are introduced that help determine if the loads or electrical outputs of a pair of balance calibration or check load data points match. A threshold of 0.01 percent of the load capacity is suggested for the identification of matching loads. Similarly, a threshold of 0.1 microV/V is recommended for the identification of matching electrical outputs. Two examples for the use of load and output repeats are discussed to illustrate benefits of the implementation of a repeat point detection algorithm in a balance data analysis software package. The first example uses the suggested load threshold to identify repeat data points that may be used to compute pure errors of the balance loads. This type of analysis may reveal hidden data quality issues that could potentially be avoided by making calibration process improvements. The second example uses the electrical output threshold for the identification of balance fouling. Data from the calibration of a six-component force balance is used to illustrate the calculation of the pure error of the balance loads.

strain-gage balance↗

NASA NTRS2017

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗

NASA NTRS2014

Ice Surface Morphology and Flow on Malaspina Glacier, Alaska: Implications for Regional Tectonics in the Saint Elias Orogen

The Saint Elias Mountains in southern Alaska are located at a structural syntaxis where the coastal thrust and fold belt of the Fairweather plate boundary intersects thrust faults and folds generated by collision of the Yakutat Terrane. The axial trace of this syntaxis extends southeastward out of the Saint Elias Mountains and beneath Malaspina Glacier where it is hidden from view and cannot be mapped using conventional methods. Here we examine the surface morphology and flow patterns of Malaspina Glacier to infer characteristics of the bedrock topography and organization of the syntaxis. Faults and folds beneath the eastern part of the glacier trend northwest and reflect dextral transpression near the terminus of the Fairweather fault system. Those beneath the western part of the glacier trend northeast and accommodate folding and thrust faulting during collision and accretion of the Yakutat Terrane. Mapping the location and geometry of the structural syntaxis provides important constraints on spatial variations in seismicity, fault kinematics, and crustal shortening beneath Malaspina Glacier, as well as the position of the collisional deformation front within the Yakutat Terrane. We also speculate that the geometrical complexity of intersecting faults within the syntaxis formed a barrier to rupture propagation during two regional Mw 8.1earthquakes in September 1899.

ice velocities↗

NASA NTRS2014

External Dependencies-Driven Architecture Discovery and Analysis of Implemented Systems

A method for architecture discovery and analysis of implemented systems (AIS) is disclosed. The premise of the method is that architecture decisions are inspired and influenced by the external entities that the software system makes use of. Examples of such external entities are COTS components, frameworks, and ultimately even the programming language itself and its libraries. Traces of these architecture decisions can thus be found in the implemented software and is manifested in the way software systems use such external entities. While this fact is often ignored in contemporary reverse engineering methods, the AIS method actively leverages and makes use of the dependencies to external entities as a starting point for the architecture discovery. The AIS method is demonstrated using the NASA's Space Network Access System (SNAS). The results show that, with abundant evidence, the method offers reusable and repeatable guidelines for discovering the architecture and locating potential risks (e.g. low testability, decreased performance) that are hidden deep in the implementation. The analysis is conducted by using external dependencies to identify, classify and review a minimal set of key source code files. Given the benefits of analyzing external dependencies as a way to discover architectures, it is argued that external dependencies deserve to be treated as first-class citizens during reverse engineering. The current structure of a knowledge base of external entities and analysis questions with strategies for getting answers is also discussed.

Ganesan, Dharmalingam↗

NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗

NASA NTRS2007

Primary Objective Grating Astronomical Telescope

It has been 370 years since a seventeenth century French mathematician, Mersenne, presciently sketched out an astronomical telescope based on dual parabolic reflectors. Since that time the concept of the primary objective has been virtually unchanged. Now a new class of astronomical telescope with a primary objective grating (POG) has been studied as an alternative. The POG competes with mirrors, in part, because diffraction gratings provide the very chromatic dispersion that mirrors defeat. The resulting telescope deals effectively with long-standing restrictions on multiple object spectroscopy (MOS). Other potential benefits include unprecedented apertures and collection areas. The new design also favors space deployment as a gossamer membrane. The inventor, Tom Ditto, first discovered that higher-order diffraction images contain hidden depth cues, for which he was granted a seminal range finding patent in 1987. Subsequently, he invented and patented 3D localizers, profilometers and microscopes using POGs. The POG telescope was placed in the public domain to expedite research. The function of a telescopes primary objective is to collect flux and to deliver images. Both functions dictate that size matters, and bigger is better. For that reason, there has been a steady push over the past century to ramp up the size of the primary mirror. However, for every doubling of mirror diameter, the elapsed time between initial effort and first light has also doubled. Meanwhile, costs escalated beyond the mirror alone, because larger instruments required larger enclosures and better pointing mechanisms. One key catalog of observation, spectrographic data, is far more difficult to amass than two-dimensional imagery. While the number of observable objects has increased with mirror size, the capacity to take spectra has not increased proportionately. In the best of circumstances, spectrograms are available for one per cent of the all objects surveyed. Spectroscopy was a historical afterthought introduced in the nineteenth century shortly after the invention of the diffraction grating and over a century after Newtons 1670 telescope. Spectroscopy is generally accomplished using a diffraction grating as the disperser in the secondary. The light being delivered to the spectrograph is first captured by a primary mirror which provides no chromatic magnification by itself. Sizeable spectrographs could not be deployed while diffraction gratings were rare commodities scribed using mechanical ruling engines that produced one grating line at a time. Today diffraction gratings are commonplace. Their recent availability is a product of both the invention of holography and the mass replication of surface microstructures. Holography permits all lines in a grating to be made simultaneously in a single photographic exposure. Holograms can then be reproduced by embossing processes. The improvement in replication is analogous to how Gutenberg changed the availability of books. The masters may be expensive, but the copies are not. Computer science is another technology that emerged in the second half of the twentieth century without which our proposed spectrographic instrument could not function due to the complexity of image processing required in data reduction. The employment of very large diffraction gratings as primary objectives for astronomical telescopes requires a novel

spectroscopy↗

NASA NTRS2017

How to Swallow a Sun

At the heart of our milky way and of virtually every other large galaxy lurks a deep cosmic mystery- a supermassive black hole. Squeezing millions to billions of times more mass than our sun into regions smaller in size than our solar system, these objects are so bizarre as to seem almost mystical. No one yet understands exactly how nature has managed to compress so much matter into such small spaces. What is clear is that the hidden gravitational hands of supermassive black holes reach out to shape their surrounding galaxies in profound yet subtle ways. By studying the growth and behavior of these ghostly black holes, scientists hope to unlock the secrets of how galaxies themselves are born and evolve.

Cenko, S. Bradley↗

NASA NTRS2017

Satellite Contributions to Climate Change Studies

This year, well-deserved attention has been given to longtime NASA employee Katherine Johnson (Figure 1) for her crucial calculations for the flights of the Mercury and Apollo astronauts in the 1960s-attention that has come through the best-selling book Hidden Figures by Margot Lee Shetterly and the Oscar-nominated movie based on the book. Johnson is still alive, now 98 years old, and has relayed how thrilled she was with her NASA job and the privilege it afforded her of being part of the grand enterprise of sending human explorers into space. I too, for the past 38 years, have been thrilled to be a part of NASA and to have the privilege to be a participant in one of NASA's other grand enterprises, this one being to use satellites to observe Earth from space, monitor and analyze its changes, and benefit humanity through the knowledge obtained.

Climate Change↗

NASA NTRS2018

New Sensors and Techniques for Aircraft Engine Health Monitoring

The ability to monitor the health of the rotating components in aircraft engines is of major interest to aero community in improving safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, able to operate in a high temperature environment, and sensitive enough to detect minute changes and hidden flaws before undesired events occur. The National Aeronautics and Space Administration (NASA), through several Aviation Safety Programs, has taken a leadership role in the development of new sensor technologies and techniques for the in-situ health monitoring of aircraft turbine engines. Along with the development of new instrumentation for aircraft engines, the Glenn Research Center has a diverse set of ground test facilities that are used to simulate the conditions that a vehicle would see in flight allowing new concepts to be safely developed and evaluated on the ground before progressing to actual flight. In this presentation Mark will give an overview of the NASA Glenn Research Center, discuss its past aviation safety initiatives, present some select sensors that have been developed for aircraft engine safety, and then conclude with some new measurement techniques that have been developed for use at the NASA Glenn test facilities, enabling better measurement and simulation of flight conditions. He will discuss current research and development efforts that he is working on at the NASA Glenn Research Center in the areas of new sensors and techniques for aircraft engine health monitoring. This work is being conducted in support of aviation safety.

Turbine↗

NASA NTRS2018

Dynamics and Control of Quadcopter in Uncertain Environment

We consider problem of dynamics, control, and uncertainty quantification for quadcopter. We use the 6DOF model of quadcopter dynamics, linear quadratic regulator and linear quadratic Gaussian control of quadcopter in the presence of dynamical disturbances, measurement noise, hidden dynamical variables, dashing GPS signal, and wind gusts to predict quadcopter trajectory. We identify key sources of uncertainties and report on progress in development of a system that estimates the probability of safety-critical events using a set of algorithms based on the trajectory predictions.

uncertainty quantification↗

NASA NTRS2018

Accessing Data Stored in Amazon S3 Using the Hyrax OPeNDAP Server

For three years we have been investigating data storage and retrieval in the Amazon Cloud, striving to optimize both storage structures and (cache-enhanced retrieval processes. These optimizations are hidden from data users, who simply employ the Data Access Protocol (DAP), a popular, Web-based Application Programmer Interface (API) developed by OPeNDAP. We present our collected findings and their realization in the Hyrax data server. Specific techniques include caching data on spinning disk; accessing sharded data in place; optimizing the organizational structures for data within the flat key-value space of Simple Storage Service (S3); and employing an API extension that permits simultaneous operations on many datasets in a single request.

Data access↗

NASA NTRS2019

A Qualitative Investigation of Selected Infrared Flow Visualization Image Processing Techniques

The deployment and integration of high-sensitivity infrared cameras in a transonic wind tunnel testenvironment has resulted in a unique capability to image aerodynamic phenomena in real-time. Multi-camera infrared flow visualization data systems are now routinely utilized at the NASA Ames Unitary Plan Wind Tunnel. The small flow-induced temperature gradients on the surface of the wind tunnel test article coupled with the high bit-depth of the infrared camera sensor makes the processing of the image data critically important. An image processing routine must enhance features of interest with minimal artifacts. Additionally, the production wind tunnel test environment demands that these processed images are made available in a real-time, automatic fashion. Therefore, any image processing routine must be computationally economical and enhance the image data with minimal input from a human operator. The following seeks to qualitatively explore selected image processing techniques by assessing their effectiveness to resolve flow features on a wind tunnel test article. A multi-scale contrast enhancement technique is discussed as well as a new implementation of a multi-scale, non-interpolated adaptive histogram equalization. Finally, a novel method is introduced that demonstrates the ability to resolve flow features imaged on bare-steel test articles possessing low emissivity.This method makes use of dynamic mode decomposition and discrete-time filtering to separate the background reflections that dominate low emissivity surfaces from the aerodynamic driven surface temperature gradients.This process will be shown to resolve the onset of boundary layer transition on a bare metal wing as well as identify and resolve hidden features in the image data. While the implementation of this technique is very preliminary it demonstrates the potential to extend the application of infrared flow-visualization within the wind tunnel test environment.

Image Processeing↗