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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Towards a Biosynthetic UAV

We are currently working on a series of projects towards the construction of a fully biological unmanned aerial vehicle (UAV) for use in scientific and humanitarian missions. The prospect of a biologically-produced UAV presents numerous advantages over the current manufacturing paradigm. First, a foundational architecture built by cells allows for construction or repair in locations where it would be difficult to bring traditional tools of production. Second, a major limitation of current research with UAVs is the size and high power consumption of analytical instruments, which require bulky electrical components and large fuselages to support their weight. By moving these functions into cells with biosensing capabilities - for example, a series of cells engineered to report GFP, green fluorescent protein, when conditions exceed a certain threshold concentration of a compound of interest, enabling their detection post-flight - these problems of scale can be avoided. To this end, we are working to engineer cells to synthesize cellulose acetate as a novel bioplastic, characterize biological methods of waterproofing the material, and program this material's systemic biodegradation. In addition, we aim to use an "amberless" system to prevent horizontal gene transfer from live cells on the material to microorganisms in the flight environment.

Biological↗
NASA NTRS2016

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗
NASA NTRS2013

Prognostics Applied to Electric Propulsion UAV

Health management plays an important role in operations of UAV. If there is equipment malfunction on critical components, safe operation of the UAV might possibly be compromised. A technology with particular promise in this arena is equipment prognostics. This technology provides a state assessment of the health of components of interest and, if a degraded state has been found, it estimates how long it will take before the equipment will reach a failure threshold, conditional on assumptions about future operating conditions and future environmental conditions. This chapter explores the technical underpinnings of how to perform prognostics and shows an implementation on the propulsion of an electric UAV. A particle filter is shown as the method of choice in performing state assessment and predicting future degradation. The method is then applied to the batteries that provide power to the propeller motors. An accurate run-time battery life prediction algorithm is of critical importance to ensure the safe operation of the vehicle if one wants to maximize in-air time. Current reliability based techniques turn out to be insufficient to manage the use of such batteries where loads vary frequently in uncertain environments.

Batteries↗
NASA NTRS2016

Using Natural Language to Enable Mission Managers to Control Multiple Heterogeneous UAVs

The availability of highly capable, yet relatively cheap, unmanned aerial vehicles (UAVs) is opening up new areas of use for hobbyists and for commercial activities. This research is developing methods beyond classical control-stick pilot inputs, to allow operators to manage complex missions without in-depth vehicle expertise. These missions may entail several heterogeneous UAVs flying coordinated patterns or flying multiple trajectories deconflicted in time or space to predefined locations. This paper describes the functionality and preliminary usability measures of an interface that allows an operator to define a mission using speech inputs. With a defined and simple vocabulary, operators can input the vast majority of mission parameters using simple, intuitive voice commands. Although the operator interface is simple, it is based upon autonomous algorithms that allow the mission to proceed with minimal input from the operator. This paper also describes these underlying algorithms that allow an operator to manage several UAVs.

Trujillo, Anna C.↗
NASA NTRS2018

Technologies and Operations for High Voltage Corona Detection with UAVs

Autonomous UAV transmission line inspection served as a reference mission for NASA demonstration of UAV deployment for economic benefit; this paper reports corona sensing advances in development of that reference mission. Unmanned aerial vehicles can serve as a platform for autonomous sensing and location of high voltage coronal discharge. Simple processing of commercial corona camera imagery can automate discharge localization and documentation. Inexpensive ultraviolet point sensors can sense discharge when carried close to the defect with a UAV. Augmented with a parabolic mirror, point sensor range can be increased to a safe inspection standoff distance, at the cost of a narrowed field of view. Results from a test flight of an augmented UV sensor are described. The imaging approach is superior in sensitivity and acquisition time, while the point sensor approach has superior size, weight, cost and durability advantages.

Moore, Andrew J.↗
NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗
NASA NTRS2003

An L-band SAR for repeat pass deformation measurements on a UAV platform

We are proposing to develop a miniaturized polarimetric L-band synthetic aperture radar (SAR) for repeat-pass differential interferometric measurements of deformation for rapidly deforming surfaces of geophysical interest such as volcanoes or earthquakes that is to be flown on a unmanned aerial vehicle (UAV) or minimally piloted vehicle (MPV). Upon surveying the capabilities and availabilities of such aircraft, the Proteus aircraft and the ALTAIR UAV appear to meet our criteria in terms of payload capabilities, flying altitude, and endurance. To support the repeat pass deformation capability it is necessary to control flight track capability of the aircraft to be within a specified 10 m tube with a goal of 1 m. This requires real-time GPS control of the autopilot to achieve these objectives that has not been demonstrated on these aircraft. Based on the Proteus and ALTAIR's altitude of 13.7 km (45,000 ft), we are designing a fully polarimetric L-band radar with 80 MHz bandwidth and a 16 km range swath. The radar will have an active electronic beam steering antenna to achieve a Doppler centroid stability that is necessary for repeat-pass interferometry. This paper presents some of the trade studies for the platform, instrument and the expected science.

interferometers↗
NASA NTRS2018

High-Fidelity Computational Aerodynamics of the Elytron 4S UAV

High-fidelity Computational Fluid Dynamics (CFD) have been carried out for the Elytron 4S Unmanned Aerial Vehicle (UAV), also known as the converticopter "proto12". It is the scaled wind tunnel model of the Elytron 4S, an Urban Air Mobility (UAM) concept, a tilt-wing, box-wing rotorcraft capable of Vertical Take-Off and Landing (VTOL). The three-dimensional unsteady Navier-Stokes equations are solved on overset grids employing high-order accurate schemes, dual-time stepping, and a hybrid turbulence model using NASA's CFD code OVERFLOW. The Elytron 4S UAV has been simulated in airplane mode and in helicopter mode.

High-Fidelity↗
NASA NTRS2021

Simulation of Radio Frequency Power Received by a UAV Along Its Flight Path

A ray-tracing electromagnetic simulation using ALTAIR WinProp software was performed to calculate the 2.4 GHz power received by a UAV in flight, both from the intended controller transmitter and from a fictitious interference source of equal power located near the ground. The signal-to-interference ratio was then calculated. For the chosen example, the received power from the controller varied from -88.3 to -70.1 dBm, while the signal-to-interference ratio varied from -10.9 to +11.4 db. This work was done in support of the System-Wide Safety Project Technical Challenge 2 - Emerging Operations at NASA Langley, which is studying methods to avoid interference of UAV control and data relay signals.

Electromagnetic Simulation↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
NASA NTRS2024

Integrating Manned Aircraft and UAVs for the Prediction, Tracking, and Eradication of Desert Locust Swarms

Desert locusts (Schistocerca gregaria) present an acute threat to the agriculture of certain regions, capable of annihilating vast expanses of farmland and destabilizing food security. In this paper, an aviation-based framework concept is introduced that integrates both manned aircraft and Unmanned Aerial Vehicles (UAVs) to preemptively forecast, localize, and eliminate locust swarms efficiently. The framework begins by leveraging predictive modeling methods to refine search areas and pinpoint high-value Points of Interest (POIs) where locust swarms are most likely to materialize. From there, the proposed two-step model uses optical, infrared, and hyperspectral sensors onboard manned aircraft to conduct long-range surveying of probable swarm locations. Upon detection of a swarm, UAVs equipped with sophisticated sensors, including pheromone detectors, thermal and optical imaging systems, are deployed directly from the aircraft to carry out targeted biopesticide applications. This approach, which capitalizes on the combined strengths of both vehicles, provides the rapid response and large-scale intervention necessary to control locust outbreaks. By targeting locust swarms during their most destructive phase, this paper aims to disrupt the locust life cycle and mitigate their severe agricultural impact, offering a scalable and sustainable solution for food security in afflicted regions.

locust monitoring systems↗
NASA NTRS1997

The UAV: A unique platform for electrodynamic studies of upward lightning in the middle atmosphere

The use of unmanned aerial vehicles (UAVs), a platform for investigations in an environment hostile to manned spacecraft, is discussed. A program which includes the use of UAVs coupled with ground-based measurements to conduct scientific studies on the electrical state of the atmosphere during electrically active periods is proposed. The radiating power from alternate current and transient components of the storm electrification was investigated.

Goldberg, Richard A.↗
NASA NTRS2001

Use of the Aerosonde Unihabited Aerial Vehicle (UAV) in the Fourth Convection and Moisture Experiment (CAMEX 4)

The Fourth Convection and Moisture Experiment (CAMEX 4) was a scientific field experiment based in Florida in summer 2001 focused on the study of hurricanes off the east coast of the United States. Sponsored by the National Aeronautics and Space Administration's Office of Earth Science, and conducted in collaboration with the National Oceanic and Atmospheric Administration's annual hurricane research program, CAMEX 4 utilized aircraft, ground-based and satellite instrumentation to obtain unprecedented, three dimensional characterizations of these important storms. The Aerosonde UAV was selected by NASA to participate in CAMEX 4 because it provided a unique capability to obtain measurements in the atmospheric boundary layer in and around the storms, unattainable by other platforms or measurement capabilities. This talk focuses on the NASA review process that was followed to coordinate the UAV activity with the conventional aircraft operations, as well as with the other participating agencies and the FAA. We will discuss how Aerosonde addressed the issues of safety, coordination and communication and summarize the lessons learned.

Hipskind, R. Stephen↗
NASA NTRS2003

Towards FAA Certification of UAVs

As of June 30, 2003, all Unmanned Aerial Vehicles (UAV), no matter how small, must adhere to the same FAA regulations as human-piloted aircraft. These regulations include certification for flying in controlled airspace and certification of flight software based on RTCA DO-178B. This paper provides an overview of the steps necessary to obtain certification, as well as a discussion about the challenges UAV's face when trying to meet these requirements. It is divided into two parts: 1) Certifications for Flying in Controlled Airspace; 2) Certification of Flight Software per RTCA DO-178B.

Nelson, Stacy↗
NASA NTRS2004

Human-Interaction Challenges in UAV-Based Autonomous Surveillance

Autonomous UAVs provide a platform for intelligent surveillance in application domains ranging from security and military operations to scientific information gathering and land management. Surveillance tasks are often long duration, requiring that any approach be adaptive to changes in the environment or user needs. We describe a decision- theoretic model of surveillance, appropriate for use on our autonomous helicopter, that provides a basis for optimizing the value of information returned by the UAV. From this approach arise a range of challenges in making this framework practical for use by human operators lacking specialized knowledge of autonomy and mathematics. This paper describes our platform and approach, then describes human-interaction challenges arising from this approach that we have identified and begun to address.

Freed, Michael↗
NASA NTRS2004

Comparing Methods for UAV-Based Autonomous Surveillance

We describe an approach to evaluating algorithmic and human performance in directing UAV-based surveillance. Its key elements are a decision-theoretic framework for measuring the utility of a surveillance schedule and an evaluation testbed consisting of 243 scenarios covering a well-defined space of possible missions. We apply this approach to two example UAV-based surveillance methods, a TSP-based algorithm and a human-directed approach, then compare them to identify general strengths, and weaknesses of each method.

Freed, Michael↗
NASA NTRS2005

Change Detection Experiments Using Low Cost UAVs

This paper presents the progress in the development of a low-cost change-detection system. This system is being developed to provide users with the ability to use a low-cost unmanned aerial vehicle (UAV) and image processing system that can detect changes in specific fixed ground locations using video provided by an autonomous UAV. The results of field experiments conducted with the US Army at Ft. A.P.Hill are presented.

Logan, Michael J.↗