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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

SIG: Multiple Views on Safety-Critical Automation: Aircraft, Autonomous Vehicles, Air Traffic Management and Satellite Ground Segments Perspectives

This SIG focuses on the engineering of automation in interactive critical systems. Automation has already been studied in a number of (sub-) disciplines and application fields: design, human factors, psychology, (software) engineering, aviation, health care, games. One distinguishing feature of the area we are focusing on is that in the field of interactive critical systems properties such as reliability, dependability, fault tolerance are as important as usability, user experience or overall acceptance issues. The SIG targets at two problem areas: first the engineering of the user interaction with (partly-) autonomous systems: how to design, build and assess autonomous behavior, especially in cases where there is a need to represent on the user interface both autonomous and interactive objects. An example of such integration is the representation of an unmanned aerial vehicle (UAV) (where no direct interaction is possible), together with aircrafts (that have to be instructed by an air traffic controller to avoid the UAV). Second the design and engineering of user interaction in general for autonomous objects/systems (for example a cruise control in a car or an autopilot in an aircraft). The goal of the SIG is to raise interest in the CHI community on the general aspects of automation and to identify a community of researchers and practitioners interested in those increasingly prominent issues of interfaces towards (semi)-autonomous systems. The expected audience should be interested in addressing the issues of integration of mainly unconnected research domains to formulate a new joint research agenda.

human-automation interaction↗
NASA NTRS2016

Measured Noise from Small Unmanned Aerial Vehicles

Proposed uses of small unmanned aerial vehicles (UAVs), including home package delivery, have the potential to expose large portions of communities to a new noise source. This paper discusses results of flyover noise measurements of four small UAVs, including an internal combustion-powered model airplane and three battery-powered multicopters. Basic noise characteristics of these vehicles are discussed, including spectral properties and sound level metrics such as sound pressure level, effective perceived noise level, and sound exposure level. The size and aerodynamic characteristics of the multicopters in particular make their flight path susceptible to atmospheric disturbances such as wind gusts. These gusts, coupled with a flight control system that varies rotor speed to maintain vehicle stability, create an unsteady acoustic signature. The spectral variations resulting from this unsteadiness are explored, in both hover and flyover conditions for the multicopters. The time varying noise, which differs from the relatively steady noise generated by large transport aircraft, may complicate the prediction of human annoyance using conventional sound level metrics.

Cabell, Randolph↗
NASA NTRS2016

Multiple Views on Safety-Critical Automation: Aircraft, Autonomous Vehicles, Air Traffic Management and Satellite Ground Segments Perspectives

This SIG focuses on the engineering of automation in interactive critical systems. Automation has already been studied in a number of (sub-) disciplines and application fields: design, human factors, psychology, (software) engineering, aviation, health care, games. One distinguishing feature of the area we are focusing on is that in the field of interactive critical systems properties such as reliability, dependability, fault-tolerance are as important as usability, user experience or overall acceptance issues. The SIG targets at two problem areas: first the engineering of the user interaction with (partly-) autonomous systems: how to design, build and assess autonomous behavior, especially in cases where there is a need to represent on the user interface both autonomous and interactive objects. An example of such integration is the representation of an unmanned aerial vehicle (UAV) (where no direct interaction is possible), together with aircrafts (that have to be instructed by an air traffic controller to avoid the UAV). Second the design and engineering of user interaction in general for autonomous objects systems (for example a cruise control in a car or an autopilot in an aircraft). The goal of the SIG is to raise interest in the CHI community on the general aspects of automation and to identify a community of researchers and practitioners interested in those increasingly prominent issues of interfaces towards (semi)-autonomous systems. The expected audience should be interested in addressing the issues of integration of mainly unconnected research domains to formulate a new joint research agenda.

safety-critical automation↗
NASA NTRS2017

Conformal Lightweight Antenna Structures for Aeronautical Communication Technologies

This project is to develop antennas which enable beyond line of sight (BLOS) command and control for UAVs. We will take advantage of newly assigned provisional Ku-band spectrum for UAVs and use unique antenna designs to avoid interference with ground systems. This will involve designing antennas with high isotropic effective radiated power (EIRP) and ultra-low sidelobes. The antennas will be made with polymer aerogel as a substrate to both reduce weight and improve performance, as demonstrated in an Aero Seedling. In addition, designing the antennas to be conformal to the aircraft fuselage will reduce drag.

antennas↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2018

Conformal Lightweight Antenna Structures for Aeronautical Communication Technologies

This project is to develop antennas which enable beyond line of sight (BLOS) command and control for UAVs (Unmanned Aerial Vehicles). We will take advantage of newly assigned provisional Ku-band spectrum for UAVs and use unique antenna designs to avoid interference with ground systems. This will involve designing antennas with high isotropic effective radiated power (EIRP) and ultra-low sidelobes. The antennas will be made with polymer aerogel as a substrate to both reduce weight and improve performance, as demonstrated in an Aero Seedling. In addition, designing the antennas to be conformal to the aircraft fuselage will reduce drag.

aerogels↗
NASA NTRS2015

Urban SAFE50: Modeling, Controlling, and Testing Safe UAS Operations in Low Altitude Settings

The research and development of UAVs (Unmanned Aerial Vehicles) are quickly progressing as industries and hobbyist societies recognize their utility. NASA is focused on the technology development and safety considerations surrounding commercial use of UAVs. Urban SAFE50 (Safe Autonomous Flight Environment within the notional last 50 feet of operation of 55 pound class UAS (Small Unmanned Aircraft Systems)) is focused on the necessary real-­time decision algorithms and flight models prevalent in low-­altitude, high-­density city environments. Construction of on-­board controls to respond to motor failure and wind dynamics as well as a database of computational flight models and battery discharge profiles will help policymakers to predict and regulate unmanned aircraft flight safely and effectively.

SAFE↗
NASA NTRS2019

Modular Unmanned Aerial System with Multi-Mode Propulsion

A modular Unmanned Aerial System (UAS) includes an Unmanned Aerial Vehicle (UAV) parent module and UAV child modules. A main wing extends from a respective fuselage of the modules. The UAS includes docking mechanisms coupled to wingtips of the main wings. The child modules dock with the wingtips of the parent or an adjacent child module. Docking forms a linked-flight configuration, with undocking and separation from the parent or adjacent child module achieving an independent-flight configuration. The modules have booms arranged transverse to the main wings and parallel to the longitudinal axis, as well as front and rear rotors/propellers. The front and rear propellers have axes of rotation that are normal to a plane of the longitudinal axis in a vertical takeoff and landing (VTOL) configuration, with the axis of rotation of the rear propellers parallel to the longitudinal axis in a forward-flight configuration.

Patterson, Michael D.↗
NASA NTRS2019

System and Method for Modular Unmanned Aerial System

A modular Unmanned Aerial System (UAS) has first and second flight configurations, and includes an Unmanned Aerial Vehicle (UAV) parent module and a plurality of UAV child modules. The parent module may have a fuselage, forward and aft wings connected to the fuselage, and a first plurality of flight propulsion devices. The child modules have a corresponding second plurality of flight propulsion devices. Each child module docks wingtip-to-wingtip with the parent module or an adjacent edge of a child module using the docking mechanisms. The child modules undock and separate from the forward wing and each other, and achieve controlled flight independently of the parent module while in the second flight configuration. A method for controlling the modular UAS is also disclosed.

Quinlan, Jesse R.↗
NASA NTRS2018

Tonal Noise Prediction of a Distributed Propulsion Unmanned Aerial Vehicle

Noise is expected to be a major barrier of unmanned aerial vehicles (UAVs) to public acceptance. A noise prediction scheme is introduced in this paper and applied to a specific vehicle configuration, namely, the Greased Lightning-10. Results herein will be used to demonstrate the feasibility of incorporating a noise constraint within the flight control system of a distributed electric propulsion vehicle by modifying commands for low-noise operation over sensitive areas, e.g., communities, schools, etc. Steady loading and thickness noise signatures of single propellers are computed using the Propeller Analysis System of the NASA Aircraft NOise Prediction Program. The individual signatures are then superposed at far field observers after applying corrections for spherical spreading and phase delays based on straight ray propagation. Two-propeller source fields are verified using analytical directivity patterns of monopoles. Notional effects of rotation rate, rotation direction, and relative propeller phase are given. Under ideal circumstances and equivalent RPM, random phasing, which occurs in most small UAVs, can produce up to 20 decibels uncertainty in the tonal sound pressure level at a given ground observer. Additionally, directivity modification via relative propeller phase control is shown to have great potential as a noise reduction technique. This paper will focus on the forward flight mode but will also briefly discuss the vertical flight mode.

Pascioni, Kyle A.↗
NASA NTRS2015

Towards Characterizing the Variability in the Loading Demands of an Unmanned Aerial Vehicle

This paper presents a computational methodology to characterize and quantify the variability in the power demands during the take-off of an unmanned aerial vehicle (UAV). A lithium-ion battery-based power system is used to power the unmanned aerial vehicle, and the capabilities of the unmanned aerial vehicle are driven by the amount of charge in this battery. In order to design the power system, it is necessary to analyze the power and charge requirements of the UAV. This paper focuses on the take-off segment, and aims to quantify the amount of charge that is required for this particular segment. Sparse data is available through different flight tests and this data is used to analyze the flight profile and the charge requirement during take-off. The amount of charge required for take-off depends on several factors that are not only variable but cannot be controlled in reality, and hence, the entire flight profile and the corresponding charge requirement are variable in nature. The information available through flight tests is converted into multi-dimensional sparse data and a new method is developed in this paper for variability characterization using multi-dimensional sparse data. This analysis is useful for prognostics and health management where it is necessary to anticipate future charge requirements in order to compute the end-of-discharge of the battery, and hence, the remaining useful life of the power system.

unmanned aerial vehicle↗
NASA NTRS2019

Vortex Radiometry: Enabling Frequency Agile Communications

Exponential proliferation of UAVs and CubeSats puts NASA's high priority communication systems at risk. UAVs and CubeSats are easy to acquire, modify and build, making it extremely likely that some will be used in unauthorized ways. While these systems provide several benefits to society, their increased use also increases the probability of unintentional signal interference, and even deliberate jamming of NASA's communication systems.This paper presents an algorithm to determine 1) when an interference induced fade will occur, 2) how long the fade will persist for and 3) how intense the fade will be. The algorithm requires data collected from Vortex Radiometers (VRs), which probe the RF environment using annular antenna beam patterns. A set of numerical simulations show that a multi-beam VR system can instruct a cognitive antenna to switch between Ka- and X-Band communications, in order to avert interference from small diameter noise sources. Analysis of the simulation results indicate that practical VR systems will require several concentric annular beam patterns, in order to mitigate fades from noise sources of various sizes. The paper concludes by identifying technology challenges that need to be overcome to achieve these capabilities.

Orbital Angular Momentum↗
NASA NTRS2019

Conformal Lightweight Antenna Structures for Aeronautical Communication Technologies

This project developed an antenna to enable beyond line of sight (BLOS) command and control for small UAVs. We took advantage of a newly assigned provisional Ku-band spectrum for UAVs and used unique antenna design to avoid interference with ground systems. This required the design of an antenna with high isotropic effective radiated power (EIRP) and ultra-low sidelobes. The antenna was made with polymer aerogel as a substrate to both reduce weight and improve performance, as demonstrated in an Aero Seedling. In addition, designing the antennas to be conformal to the aircraft fuselage will reduce drag.

polyimide↗
NASA NTRS2020

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process.

Lee, Ritchie↗
NASA NTRS2020

NASA Next-Generation Sensing Technologies for Earth & Planetary Science

Dr. Ved Chirayath's seminar will highlight new sensing technologies and airborne platforms he is developing for Earth & Space Science as director of the NASA Silicon Valley Laboratory for Advanced Sensing. His talk will feature recent work on Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, MiDAR, a next-generation active hyperspectral remote sensing and optical communications instrument, airborne gas sensing of multipollutant combustion sources, and a plasma actuated unmanned aerial vehicle (UAV) that utilized high-voltage dielectric discharge devices to achieve the first plasma controlled flight in history.Fluid Lensing and the NASA FluidCam instrument have been used extensively to provide distortion-free 3D multispectral imagery from UAVs of shallow marine systems around the world. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments as an analog for future Ocean Worlds robotic exploration missions. Finally, Chirayath will present preliminary results from NeMO-Net, a supercomputer-based neural network that uses a citizen science video game for global multimodal coral reef benthic habitat mapping, fusing remote sensing data from Fluid Lensing, MiDAR, NASA's Earth Observing System, and commercial satellites, to better understand the present and past dynamics of shallow marine systems. Together, these maturing technologies present promising new ways in which to explore terrestrial, marine, and aerial systems on Earth, and, ultimately, aid in the search for extraterrestrial life within our solar system and beyond.

Chirayath, Ved↗
NASA NTRS2019

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds - NASA FluidCam, MiDAR, and NeMO-Net

We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object’s spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10(exp 3) times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally using active learning for citizen-science based training. Preliminary results from four-class Q17 coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

Ved Chirayath↗
NASA NTRSDate not supplied

Some NASA Perspectives on H2

This presentation provides the NASA perspective for government and industry technology developers world-wide who are exploring the potential for hydrogen (H2) in aviation applications including electric aircraft, airport vehicles, and airport related equipment. NASA’s H2 interest has been related to vehicle-level technology development and integration only, but NASA is keenly aware of the necessity of work focused on solving the challenges of H2 airport infrastructure. Some historical perspectives are provided on NASA efforts in H2 fuel cell development for space and aeronautics applications. NASA’s current research portfolio includes some investigation of H2 energy storage and hydrocarbon conversion for fuel cells on electric aircraft. There is no current emphasis on the other H2-related challenges. Over the years NASA has conducted research into “Quiet Green Transport” with a H2 fuel cell powered aircraft, an Emissionless Aircraft Study with Non Flow Through – Regenerative Fuel Cell (NFT-RFC) technology which captures the water, and an H2 fuel cell powered Unmanned Aerial Vehicle (UAV) study. NASA has flown a long endurance UAV using a RFC in the Low Emissions Alternative Power (LEAP) program. Recently, NASA conducted feasibility studies on hybrid SOFC with onboard hydrocarbon fuel reformation for aircraft primary propulsive power and secondary power under the Fostering Ultra-Efficient, Low-Emitting Aviation Power (FUELEAP) program. NASA currently has a multiyear program with the Center for Cryogenic High-Efficiency Electrical Technologies for Aircraft (CHEETA) to explore the benefits of using cryogenic H2 on aircraft for fuel cell propulsion and to provide superconductivity in electrical power management on the aircraft.

Power↗