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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 NTRS1991

Extreme Ultraviolet Explorer. Long look at the next window

The Extreme Ultraviolet Explorer (EUVE) will map the entire sky to determine the existence, direction, brightness, and temperature of thousands of objects that are sources of so-called extreme ultraviolet (EUV) radiation. The EUV spectral region is located between the x-ray and ultraviolet regions of the electromagnetic spectrum. From the sky survey by EUVE, astronomers will determine the nature of sources of EUV light in our galaxy, and infer the distribution of interstellar gas for hundreds of light years around the solar system. It is from this gas and the accompanying dust in space that new stars and solar systems are born and to which evolving and dying stars return much of their material in an endless cosmic cycle of birth, death, and rebirth. Besides surveying the sky, astronomers will make detailed studies of selected objects with EUVE to determine their physical properties and chemical compositions. Also, they will learn about the conditions that prevail and the processes at work in stars, planets, and other sources of EUV radiation, maybe even quasars. The EUVE mission and instruments are described. The objects that EUVE will likely find are described.

Maran, Stephen P.↗
NASA NTRS1999

Mineralogy of the Solar System

The coming decade will witnesses the first sample return missions from solar system bodies in 30 years. These samples will all be very small, some missions return only a few milligrams of total mass. Fortunately, the capability of modem methods to characterize ultra-small samples is well established from analysis of interplanetary dust particles (IDPs), interstellar grains recovered from meteorites, and other materials requiring ultra-sensitive analytical capabilities. Powerful analytical techniques are available that require, under favorable circumstances, single particles of only a few nanograms for entire suites of fairly comprehensive characterizations. A returned sample of greater than 1,000 particles with total mass of just one microgram permits comprehensive quantitative geochemical measurements that are impractical to can-y out in situ by flight instruments. With the Galileo flybys of Gaspra and Ida, it is now recognized that even very small airless bodies have indeed developed a particulate regolith. Acquiring a sample of the bulk regolith, a simple sampling strategy, provides two critical pieces of information about the body. Regolith samples are excellent bulk samples since they normally contain all the key components of the local environment, albeit in particulate form. Furthermore, since this fine fraction dominates remote measurements, regolith samples also provide information about surface alteration processes and are a key link to remote sensing of other bodies. Studies indicate that a statistically significant number of nanogram-sized particles should be able to characterize the regolith of a primitive asteroid, although the presence of larger components within even primitive meteorites (e.g.. Murchison), e.g. chondrules, CAI, large crystal fragments, etc., points out the limitations of using data obtained from nanogram-sized samples to characterize entire primitive asteroids. However, most important asteroidal geological processes have left their mark on the matrix, since this is the finest-grained portion and therefore most sensitive to chemical and physical changes. Thus, the following information can be learned from this fine grain size fraction alone: (1) mineral paragenesis; (2) regolith processes, (3) bulk composition; (4) conditions of thermal and aqueous alteration (if any); (5) relationships to planets, comets, meteorites (via isotopic analyses, including oxygen; (6) abundance of water and hydrated material; (7) abundance of organics; (8) history of volatile mobility, (9) presence and origin of presolar and/or interstellar material.

Zolensky, Michael E.↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRSDate not supplied

AmesDT: Digital Twin and Autonomy Validation Environment

A simulation of NASA Ames Research Center was developed to provide a common testbed for multiple areas of research within the Intelligent Systems Division, primarily related to verification and validation of autonomous technologies, machine learning, and digital twin systems. AmesSim corresponds a physical rover that is capable of navigation in the real-world environment; in this way, the same experiments can be run in both settings, with the same software and hardware stacks in the loop. The simulation is built in Unreal Engine 4 and uses the AirSim plugin for API convenience. Several custom modifications allow deterministic, faster-than-realtime execution, which enables consistent testing of on-line algorithms and large-scale data collection. This paper describes the architecture and capabilities of the simulation and discusses development challenge.

simulation↗
NASA NTRS2009

Bio-Inspired Neural Model for Learning Dynamic Models

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Duong, Tuan↗
NASA NTRS1995

Data management, archiving, visualization and analysis of space physics data

A series of programs for the visualization and analysis of space physics data has been developed at UCLA. In the course of those developments, a number of lessons have been learned regarding data management and data archiving, as well as data analysis. The issues now facing those wishing to develop such software, as well as the lessons learned, are reviewed. Modern media have eased many of the earlier problems of the physical volume required to store data, the speed of access, and the permanence of the records. However, the ultimate longevity of these media is still a question of debate. Finally, while software development has become easier, cost is still a limiting factor in developing visualization and analysis software.

Russell, C. T.↗
NASA NTRSDate not supplied

Development of Solar Flare and Energetic Particle Prediction Portal (SEP 3 )

Solar activity is a primary factor determining the state of the Earth’s space environment, geomagnetic and ionospheric disturbances, and radiation hazards. In the current state of knowledge, machine learning (ML) methods provide essential tools for processing data, investigating relationships among various physical properties and characteristics, uncovering hidden connections, and predicting hazardous solar events. The primary difficulty in developing and applying modern machine-learning tools in heliophysics is that the essential data are scattered among over a hundred data repositories developed by instrument teams of space missions and ground-based observatories. In addition, statistical and ML methods require long time series of homogeneous measurements. To facilitate ML-ready data preparation and access, we have developed an interactive database of solar flares integrating the most essential datasets (https://solarflare.njit.edu/). The database performs an initial data processing and is automatically updated. In addition, we are developing the Solar Energetic Particle Prediction Portal (SEP3, https://sun.njit.edu/SEP3), which hosts web applications that allow users to retrieve the database records. The Portal has a search page for browsing the events from the most widely used catalogs and a dedicated space to share the most recent achievements of the team. The interactive widget can display soft X-ray and proton flux time series from GOES satellites and the flare records. The data portal has been used to evaluate the forecasts of solar proton events and investigate machine-learning approaches to SEP prediction.

SMD↗
NASA NTRS2009

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

EXPLICIT PHYSICS ALGORITHMS↗
NASA NTRS1990

What we learn from eclipsing binaries in the ultraviolet

Recent results on stars and stellar physics from IUE (International Ultraviolet Explorer) observations of eclipsing binaries are discussed. Several case studies are presented, including V 444 Cyg, Aur stars, V 471 Tau and AR Lac. Topics include stellar winds and mass loss, stellar atmospheres, stellar dynamos, and surface activity. Studies of binary star dynamics and evolution are discussed. The progress made with IUE in understanding the complex dynamical and evolutionary processes taking place in W UMa-type binaries and Algol systems is highlighted. The initial results of intensive studies of the W UMa star VW Cep and three representative Algol-type binaries (in different stages of evolution) focused on gas flows and accretion, are included. The future prospects of eclipsing binary research are explored. Remaining problems are surveyed and the next challenges are presented. The roles that eclipsing binaries could play in studies of stellar evolution, cluster dynamics, galactic structure, mass luminosity relations for extra galactic systems, cosmology, and even possible detection of extra solar system planets using eclipsing binaries are discussed.

Guinan, Edward F.↗
NASA NTRS2016

Simulation Development and Analysis of Crew Vehicle Ascent Abort

NASA's Commercial Crew Program is an integral step in its journey to Mars as it would expedite development of space technologies and open up partnership with U.S. commercial companies. NASA reviews and independent assessment of Commercial Crew Program is fundamental to its success, and being able to model a commercial crew vehicle in a simulation rather than conduct a live test would be a safer, faster, and less expensive way to assess and certify the capabilities of the vehicle. To this end, my project was to determine the feasibility of using a simulation tool named SOMBAT version 2.0 to model a multiple parachute system for Commercial Crew Program simulation. The main tasks assigned to me were to debug and test the main parachute system model, (capable of simulating one to four main parachute bodies), and to utilize a graphical program to animate the simulation results. To begin tackling the first task, I learned how to use SOMBAT by familiarizing myself with its mechanics and by understanding the methods used to tweak its various parameters and outputs. I then used this new knowledge to set up, run, and analyze many different situations within SOMBAT in order to explore the limitations of the parachute model. Some examples of parameters that I varied include the initial velocity and orientation of the falling capsule, the number of main parachutes, and the location where the parachutes were attached to the capsule. Each parameter changed would give a different output, and in some cases, would expose a bug or limitation in the model. A major bug that I discovered was the inability of the model to handle any number of parachutes other than three. I spent quite some time trying to debug the code logically, but was unable to figure it out until my mentor taught me that digital simulation limitations can occur when some approximations are mistakenly assumed for certain in a physical system. This led me to the realization that unlike in all of the programming classes I have taken thus far that focus on pure logic, simulation code focuses on mimicking the physical world with some approximation and can have inaccuracies or numerical instabilities. Learning from my mistake, I adopted new methods to analyze these different simulations. One method the student used was to numerically plot various physical parameters using MATLAB to confirm the mechanical behavior of the system in addition to comparing the data to the output from a separate simulation tool called FAST. By having full control over what was being outputted from the simulation, I could choose which parameters to change and to plot as well as how to plot them, allowing for an in depth analysis of the data. Another method of analysis was to convert the output data into a graphical animation. Unlike the numerical plots, where all of the physical components were displayed separately, this graphical display allows for a combined look at the simulation output that makes it much easier for one to see the physical behavior of the model. The process for converting SOMBAT output for EDGE graphical display had to be developed. With some guidance from other EDGE users, I developed a process and created a script that would easily allow one to display simulations graphically. Another limitation with the SOMBAT model was the inability for the capsule to have the main parachutes instantly deployed with a large angle between the air speed vector and the chutes drag vector. To explore this problem, I had to learn about different coordinate frames used in Guidance, Navigation & Control (J2000, ECEF, ENU, etc.) to describe the motion of a vehicle and about Euler angles (e.g. Roll, Pitch, Yaw) to describe the orientation of the vehicle. With a thorough explanation from my mentor about the description of each coordinate frame, as well as how to use a directional cosine matrix to transform one frame to another, I investigated the problem by simulating different capsule orientations. In the end, I was able to show that this limitation could be avoided if the capsule is initially oriented antiparallel to its velocity vector.

Wong, Chi S.↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRSDate not supplied

Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky

The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.

Shan Zeng↗
NASA NTRS1990

Fundamental research in artificial intelligence at NASA

This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

Friedland, Peter↗
NASA NTRS2002

Geography From Another Dimension

The GEODESY software program is intended to promote geographical awareness among students with its remote sensing capabilities to observe the Earth's surface from distant vantage points. Students and teachers using GEODESY learn to interpret and analyze geographical data pertaining to the physical attributes of their community. For example, the program provides a digital environment of physical features, such as mountains and bodies of water, as well as man-made features, such as roads and parks, using aerial photography, satellite imagery, and geographic information systems data in accordance with National Geography Standards. The main goal is to have the students and teachers gain a better understanding of the unique forces that drive their coexistence. GEODESY was developed with technical assistance and financial support from Stennis Space Center's Commercial Remote Sensing Program Office, now known as the Earth Science Applications Directorate.

Source record↗
NASA NTRS2015

VEGGIE and the VEG-01 Hardware Validation Test

This is a presentation to NASA HQ for a lunch-and-learn detailing the Veggie testing and results. Space Life and Physical Sciences plans to record this presentation and make it available for public display.

Massa, Gioia↗
NASA NTRS2022

Review of Solar Energetic Particle Models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

Kathryn Whitman↗
NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗