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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Observations of atmospheric 3D winds are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we compare state-of-the-art convolutional neural networks for optical flow in a physics-guided deep learning framework for predicting AMVs. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

Geostationary↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Introduction - Paradigm shift from one operator supervising a single vehicle, to an operator supervising multiple highly automated vehicles (One-to-Many) - One-to-Many application - Search and Rescue - Foraging - Military ops - Etc. - Calibrated trust in automation enables human operators to effectively manage highly automated vehicles - Past studies show that trust mediates relationship between reliability and dependence (Chancey et al., 2017; Chancey et al., 2015) - Future studies needed to further understand relationship

trust↗
NASA NTRSDate not supplied

Foundation AI Models for Science

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.

Manil Maskey↗
NASA NTRSDate not supplied

Human Factors Research Considerations for Terminal Area Urban Air Mobility Operations

In this presentation, we discuss the human factors research challenges from introducing greater levels of automation in a future air transportation concept called Urban Air Mobility (UAM). UAM is an air transportation concept that aims to provide air transportation services to the daily commuter, as well as emergency response and package delivery. The principal innovation over current day large air transport system is the greater distribution of important safety functions to automated and human agents; these functions include air traffic management, traditionally an air traffic controller responsibility. A central aspect of UAM is the development of an automated air traffic manager, whose primary responsibility is to approve airspace access for vehicle operators. Vehicle operator roles may include onboard and remote pilots, as well as a human manager who will supervise an entire fleet. Alternatively, both fleet manager and vehicle operators can be merged into a single role – a feasible option if UAM aircraft are autonomous. In lieu of tower controllers, vertiport managers, with the assistance of automation, will manage arrival and departure schedules between vertiports, as well as supervise surface operations. Our approach here will be to introduce use cases currently being developed by NASA, and then provide preliminary definitions for each of the roles introduced above and how coordination between them can be configured to support the operations within the use cases described. Subsequently, we review the tools and interfaces being developed to support the various roles. To conclude, we present current human factors work related to defining the roles above and suggest future work to advance the UAM concept.

trial planning↗
NASA NTRSDate not supplied

Revolutionizing Earth Science with Generalized AI Models

Foundation Models (FM) are generalized Artificial Intelligence (AI) models that are designed to replace a task or an application-specific model and can be used for many downstream applications. These FM can be built on any sequence data and are trained utilizing self-supervised approaches. The obstacle of creating a sizable labeled dataset for training is removed by using self-supervised learning. Most FM employ transformer design that takes advantage of the idea of self-attention, allowing the network to represent the impact of distant data points on one another in space and time. The FM models show emergent qualities that are induced from the data. FM can become a valuable tool for Earth science researchers. Due to the size of these models, downstream applications built fine-tuning these FM perform better and exhibit greater accuracy than models created from scratch. FM significantly lowers the entry barrier in terms of both the time and effort required to develop various downstream applications. For some scientific datasets, such as optical remote sensing data, FM can speed up processes like classification, object detection and prediction. By eliminating the training data bottleneck and maximizing the usage of science data, FM can make it simpler to integrate AI into scientific research. Initial results for three different FMs will be presented.

Rahul Ramachandran↗
NASA NTRSDate not supplied

Assessment of Remote Pilot Maneuver Taskload under Multi-Vehicle Control

Multi-vehicle control schemes where a number of remote pilots (m) supervise a number of uncrewed vehicles (m:N) are desired to enable scalability of operations, such as air cargo delivery, in the face of pilot shortage and other constraints. We use queuing models derived from historical track data to assess the increased task load on a remote pilot due to maneuvering demands as the number of supervised vehicles increase. We quantify metrics such as the probability that the inter-maneuver time and inter-communication time.

Multi-Vehicle Control↗
NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗
NASA NTRS2014

Predicting Ground Delay Program at an Airport Based on Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

traffic flow management↗
NASA NTRS1961

Radiation Heat Transfer

The pm-pose of this report is to describe work which has been carried out under the subject grant during the period from April 1, 1961, to October 1, 1961. Technical supervision and guidance of the work was provided by Mr. Seymour Lieblein, Chief, Flow Physics Branch, NASA Lewis Research Center, Cleveland, Ohio.

ABSORPTION↗
NASA NTRS1965

Studies in the fields of space flight and guidance theory progress report no. 7, 23 jul. 1964 - 1 apr. 1965

The progress reports of NASA-sponsored studies in the areas of space flight and guidance theory are presented. The studies are carried on by several universities and industrial companies. This progress report covers the period from July 23, 1964 to April 1, 1965. The contracts are technically supervised by personnel of the Astrodynamics and Guidance Theory Division, Aero-Astrodynamics Laboratory, Marshall Space Flight Center.

NASA Program↗
NASA NTRS1965

Ballistocardiography, a Bibliography <1877 - 1964<

The publication of this bibliography on Ballistocardiography, NASA SP-7021 (FAA AM 65-15) fulfills a long-standing requirement for a comprehensive and retrospective collection of references on a subject that is of particular interest to medical investigators and cardiovascular physiologists engaged in aerospace studies. Ballistocardiography is a technique for producing a graphical representation of repetitive motions of the human body arising from the sudden ejection of blood into the great vessels with each heart beat. Standard ballistocardiographic techniques are emphasized in the references included in the bibliography, but attention is also given to several related methods that are used to record these phenomena and to the equipment and instrumentation employed in such investigations. The format of the citations in NASA SP-7021 (FAA AM 65-15) was chosen to resemble, as closely as possible, the format that is now being used in the Index Medicwl published by the National Library of Medicine, except that English titles of foreign language articles appear in parentheses instead of in brackets as they do in Index Medicus. In each case, an attempt was made to identify the language of the article, or the place of publication of the foreign language journal. Since the citations were gathered over a period of years and occasionally from sources which do not appear in standard indexes, complete information was not always available. Citations are arranged alphabetically by personal author. In a large measure, the comprehensivenes of this collection of references is due to information obtained from card files compiled and maintained by Dr. William R. Scarborough of the Georgetown Clinical Research Institute, Office of Aviation Medicine, FAA, supplemented by the private collections of Drs. Isaac Starr, Abraham Noordergraaf, and John L. Nickerson. Selection and preparation of individual references and general editing of the material was done by Dr. Scarborough, who received valuable assistance from Mrs. Claire Tedesco of the Medical Library Branch of the FAA Library. Final compilation and printing were completed under the supervision of NASA.

AEROSPACE MEDICINE↗
NASA NTRS1968

Apollo Program Management, Kennedy Space Center, Florida

The evolution of the Kennedy Space Center as the launch organization for Apollo/ Saturn V involved the concurrent solution of numerous complex problems. A significant increase in manpower was involved. Large and complex checkout and launch facilities were to be designed and constructed. Expansion of operational capabilities required the establishment and integration of a Government-Contractor operational team. From an initial cadre of approximately 200 civil service personnel of the Army Ballistic Missile Agency, transferred to NASA in 1960 following its establishment, expansion to the present civil service level of 2,900 occurred in the last seven years. Established within NASA as a directorate of the Marshall Space Flight Center, KSC achieved center status in 1962. With its designation as a Center, KSC accomplished the development and staffing of an organization that could perform procurement, resources, financial, and other management requirements formerly provided by the parent organization. In addition to continuing launch operations for established programs, KSC undertook the design and construction of large, new, and unique launch facilities for Apollo/Saturn V. With the expansion of the civil service work force, KSC integrated contractor organizations employing 23,000 personnel at the Center to perform specific operational and support missions under the technical supervision and observation of the Government team. The management techniques, organizational concepts, and continuing efforts utilized to meet the Apollo goals and challenges are discussed in this document.

Source record↗
NASA NTRS1972

Topological solution of bilateral switching networks

Topological method uses the eye as pattern detector to trace path of transmission on truth table. Pathway selection is continually supervised by logician, allowing him to seek planar iterative solution desirable for fabrication of monolithic circuits. Method applies to parity generators, multiple output functions, full adders, and bit comparators.

Mazer, L.↗