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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of a Compact Lidar Sensor for Terrain Relative Navigation and Terrain Hazard Avoidance

A Lidar sensor utilizing linear-mode flash lidar technology and a novel Super-Resolution technique has been developed for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard landing vehicles. Processing algorithms for precision navigation and safe landing location identification take advantage of the uniform fixed pixels property of generated high resolution Digital Elevation Maps to achieve high reliability operation in near real-time. This paper describes the results of drone and helicopter flight tests of a breadboard system, explains the design and capabilities of a recently built compact prototype unit, and proposes a concepts of operation for future landing missions.

3-D Imaging↗
NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗
NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗
NASA NTRSDate not supplied

High Resolution Terrain Sensing Lidar for Precision Navigation and Safe Landing of Space and Aerial Vehicles

A 3-D imaging flash lidar sensor employing a resolution enhancement algorithm is being developed at NASA Langley Research Center for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard spacecraft landing on the Moon, Mars, and other planetary bodies. This lidar sensor, we refer to as Terrain Sensing Lidar (TSL), is a solution for future missions that require landing at pre-designated sites near high value resources or at areas of high scientific value, while avoiding hazardous terrain features, such as escarpments, craters, slopes, and rocks, or pre-deployed assets. TSL can also benefit terrestrial applications such as autonomous aerial vehicles without reliance on signals from Global Positioning System (GPS). The feasibility of the TSL concept has been shown through a series of drone, fixed-wing aircraft, and helicopter flight tests. A prototype version of the TSL has been recently assembled for conducting another set of flight tests to demonstrate its readiness for upcoming landing missions. This paper describes the TSL, provides its performance parameters, and explains its operational concepts for landing missions.

Precision Navigation↗
DOE OSTI2018

Advances in UAV Applications and Propulsion Technologies Drive the Development of UAV Propulsion Standards

The number of small unmanned aerial vehicles (UAVs) used for private and commercial applications is growing exponentially, beyond the domain of hobby enthusiasts. However, standards development has not been keeping up with the applications and technologies now available. The advent of inexpensive battery-powered quadcopters as stable aerial photography and remote-viewing platforms has expanded the utility of these systems into commercial and private applications for inspection and surveillance. With drone-delivered packages on the horizon, the potential for expansion will be even higher. These developments need to be incorporated into standards being produced for UAVs and propulsion systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

A Hybrid Tour-Based Model For Energy Analysis Of Multi-Modal Intra-City Freight: A Case Study Of Autonomous Electric Vehicles: Preprint

With the emergence of cutting-edge transportation technologies, such as electric vehicles, connected and autonomous vehicles, and drones, the adoption of multimodal freight mobility has the potential to improve efficiency and save more energy. This paper proposes a hybrid tour-based model to evaluate the energy impact of multimodal intra-city freight movement for future scenarios. The model is built based on the traveling salesman problem model and clustering techniques. A case study using autonomous electric vehicles for package delivery is evaluated. The study was conducted using data analyzed from the Columbus, Ohio, metropolitan area. The initial results show that multimodal package delivery using autonomous electric vehicles reduced total travel time by 45% and saved total energy use by 19%, but increased total vehicle miles traveled by 15% when compared with the baseline scenario.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Sensor payloads for small unmanned aerial vehicles

Small unmanned aerial systems (sUAS) are ideal for delivering sensors into areas that are inaccessible or too dangerous for human entry or manned aircraft over-flights. The systems can fly low and slow for enhanced sensitivity, and they can get into tight spaces where ordinary aircraft would never attempt to fly. We have configured and flown sensor payloads for radiation, chemical, and optical detection on small fixed wing and rotary wing aircraft. For radiation detection we have flown packages consisting of sodium iodide (NaI), lanthanum bromide (LaBr3), and cadmium-zinc-telluride (CZT) gamma spectrometers, and we have also flown a CZT gamma imaging detector. The chemical sensing package consists of surface catalytic detectors for real-time chemical sensing, and a multi-tube chemical sampler that can be post-flight analyzed with a gas chromatograph mass spectrometer (GCMS). The optical package comprises a small spectrometer with wavelength sensitivity from 200 nm to 900 nm, and it has successfully collected burst light spectra from small explosive detonations. All the sensor packages provide telemetry, GPS coordinates, LIDAR distance ranging, and absolute time of day to microsecond accuracy. Payload weights range from less than 1 kg up to 5.5 kg, and they have flown on Sandstorm and T-28 fixed wing drones, on a custom-built heavy-lift hex-copter, a 3DR Solo quad-copter, and a T-Rex 700 helicopter. Communications are over 2.4 GHz, 915 MHz radios, and 4G LTE cellular data links. Real time data feeds from the sensor payloads to web browser clients anywhere in the world are possible via a virtual server on the Amazon AWS cloud.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

A Hybrid Tour-Based Model for Energy Analysis of Multi-Modal Intra-City Freight: A Case Study of Autonomous Electric Vehicles

With the emergence of cutting-edge transportation technologies, such as electric vehicles (EVs), connected and autonomous vehicles, and drones, the adoption of multimodal freight mobility has the potential to improve efficiency and save more energy. This paper proposes a hybrid tour-based model to evaluate the energy impact of multimodal intra-city freight movement for future scenarios. The model is built based on the traveling salesman problem model and clustering techniques. A case study using autonomous electric vehicles for package delivery is evaluated. The study was conducted using data analyzed from the Columbus, Ohio, metropolitan area. The initial results show that multimodal package delivery using autonomous EVs reduced total travel time by 45% and saved total energy use by 19%, but increased total vehicle miles traveled by 15% when compared with the baseline scenario.

25 ENERGY STORAGE↗
DOE OSTI2019

EFRC 10@10: Making More Powerful, Longer-Lived Batteries

Lithium ion batteries power your phone, your laptop, and the growing number of electric vehicles on the road. Such batteries are also critical to the rapidly expanding market for airborne drones. However, current versions of these batteries don’t hold as much power as their users would like, need frequent recharging (a lengthy process, especially for vehicles), and wear out in a few years when the materials in the battery degrade. To understand why and to find better materials, the U.S. Department of Energy’s Office of Science funded an Energy Frontier Research Center (EFRC), the Center for Electrochemical Energy Science, led by Argonne National Laboratory with Northwestern University and other university partners.

25 ENERGY STORAGE↗
DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗
DOE OSTI2020

Multicube retroreflector and retroreflector-based communication system

A “multicube” retroreflector may include “nested” corner cubes. Such a design combines the ease of fabrication of conventional corner cubes (e.g., stamping from a sheet) and the ability to easily and densely package them with a greater range of angles over which light will be reflected back towards the source. Such multicube retroreflectors may reflect 50% more light than corner cube retroreflectors. A retroreflector, or an array of retroreflectors, may be used as part of a communication system. A modulated retroreflector or an array thereof can form the basis of a unique communication system. For instance, by using a modulated retroreflector, or an array of such retroreflectors, a host system (e.g., an aircraft) can receive data from a remote system (e.g., a drone, an air-dropped sensor package, etc.) in a way that is difficult to intercept, and minimizes the power, computation, and antenna pointing requirements for the remote system.

42 ENGINEERING↗
DOE OSTI2020

Detecting gas leaks using unmanned aerial vehicles

Methods, systems and computer program products for detecting gas leaks using a drone are provided. Aspects include capturing a first set of data regarding a presence of a gas in the geographic area while flying along the initial flight path. Aspects also include creating secondary flight paths through regions in the geographic area in which the presence of the gas exceeds a threshold amount and capturing a second set of data regarding a concentration of the gas in the one or more regions while flying along the secondary flight paths. Aspects further include capturing wind data while flying along the initial and second flight paths and creating a three-dimensional gas plume model for gas leaks identified in the geographic area based on the first set of data, the second set of data and the wind data, wherein the three-dimensional gas plume model identifies a source of the gas leaks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Solar panel field array support system and apparatus and method for construction use

A combined PV panel assembly jig and forklift transport pallet is used to assemble PV panels for transport to a field array from a protected manufacturing environment. The panels are assembled to have adhesively applied rails for transport by a robotic drone on a ground-support rack and are pre-wired. The PV panel assembly jig holds, protects, and aligns the PV panels in an upside down position, opposite to their operational position, for ease of wiring in order to decrease the manual labor required in the field. Once the pallet is transported to the load station at the end of a row of solar panel racks in the field array, a robotic loader lifts the upside down PV panels from the combined PV panel assembly jig and forklift transport pallet in an arcing overhead motion that lifts, tilts, and deposits the PV panels in an upright position at the loading station of a railed rack support as ground-mounted in a solar panel field array.

14 SOLAR ENERGY↗
DOE OSTI2016

Aboard the R/V Oceanus: Carbon Flux Explorers

Jim Bishop, senior scientist at Berkeley Lab and professor at UC Berkeley, is leading a project to deploy robotic floats that provide data on how microorganisms sequester carbon in the ocean. He recently led a research team on a 10-day voyage, funded by the National Science Foundation, to put the Carbon Flux Explorers to the test. Read more at http://oceanbots.lbl.gov (Video edited by Roxanne Makasdjian and Stephen McNally/UC Berkeley. Video shot by Jessica Kendall-Bar. Drone footage provided by Mark Booher.)

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2021

Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems

In a nuclear accident, or in the aftermath of an improvised nuclear device or radiological dispersal device detonation, radioactive materials may pose a severe health threat to individuals near the site of the incident. The Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems (ICARUS) does the dull, dirty, and dangerous (and, sometimes, deep) work of mapping and characterizing valuable information to enable a safe and appropriate response. ICARUS provides timely answers to the questions, “What is it?” “Where is it?”, and “How far does it extend?” Using commercial, off-the-shelf proven components, the integrated chemical, biological, radiological, nuclear, and explosives detection architecture for our drones provides a solution for collecting radiological, chemical, and optical information on developing complex situations, returning actionable data for determining if it is safe for further response efforts. ICARUS can determine and transmit the location, identity, and intensity of radionuclide contamination to a remote base station. It can deliver on the unexpected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Multi-sensor agent devices

Apparatus, methods, and systems implementing multi-sensor agent devices are described herein. The agent devices can each include a plurality of sensors for measuring parameters of interest to an entity such as an electric power utility. The sensors can be organized in individually-IP-addressable sensor clusters, with each sensor cluster including an associated microcontroller. The agent devices can be controlled by a control center of the entity to operate in a coordinated manner, such as to gather and transmit data regarding parameters of interest. The agent devices can be transported to desired areas for data collection by unmanned aerial systems such as drones, and the collected data can be stored in a distributed blockchain ledger.

Fuhr, Peter L.↗