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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 NTRS2019

Reducing The Noise Impact of Unmanned Aerial Vehicles by Flight Control System Augmentation

The aim of this thesis is to explore methods to reduce the noise impact of unmanned aerial vehicles operating within acoustically sensitive environments by flight control systemaugmentation. Two methods are investigated and include: (i) reduction of sound generatedby vehicle speed control while flying along a nominal path and (ii) reduction of acousticexposure by vehicle path control while flying at a nominal speed. Both methods requireincorporation of an acoustic model into the flight control system as an additional controlobjective and an acoustic metric to characterize primary noise sources dependent on vehiclestate. An acoustic model was developed based on Gutin’s work to estimate propeller noise,both to estimate source noise and observer noise using two separate acoustic metrics. Thesemethods can potentially mitigate the noise impact of unmanned aerial systems operatingnear residential communities.The baseline flight control system of a representative aircraft was augmented with acontrol law to reduce propeller noise using feedback control of the commanded flight speeduntil an acoustic target was met, based on the propeller noise model. This control approachfocuses on modifying flight speed only, with no perturbation to the trajectory. Multipleflight simulations were performed and the results showed that integrating an acoustic metricinto the flight control system of an unmanned aerial system is possible and useful. A secondmethod to mitigate the effects of noise on an observer was also pursued to optimize a tra-jectory in order to avoid an acoustically sensitive region during the path planning process.After the propeller noise model was incorporated into the vehicle system, simulations showedthat it is possible to reduce the noise impact on an observer through an optimization of thetrajectory with no perturbation to the flight speed.

Matthew B Galles↗
NASA NTRS1994

Photovoltaic electric power applied to Unmanned Aerial Vehicles (UAV)

Photovoltaic electric-powered flight is receiving a great deal of attention in the context of the United States' Unmanned Aerial Vehicle (UAV) program. This paper addresses some of the enabling technical areas and their potential solutions. Of particular interest are the long-duration, high-altitude class of UAV's whose mission it is to achieve altitudes between 60,000 and 100,000 feet, and to remain at those altitudes for prolonged periods performing various mapping and surveillance activities. Addressed herein are studies which reveal the need for extremely light-weight and efficient solar cells, high-efficiency electric motor-driven propeller modules, and power management and distribution control elements. Since the potential payloads vary dramatically in their power consumption and duty cycles, a typical load profile has been selected to provide commonality for the propulsion power comparisons. Since missions vary widely with respect to ground coverage requirements, from repeated orbiting over a localized target to long-distance routes over irregular terrain, we have also averaged the power requirements for on-board guidance and control power, as well as ground control and communication link utilization. In the context of the national technology reinvestment program, wherever possible we modeled components and materials which have been qualified for space and defense applications, yet are compatible with civilian UAV activities. These include, but are not limited to, solar cell developments, electric storage technology for diurnal operation, local and ground communications, power management and distribution, and control servo design. And finally, the results of tests conducted by Wright Laboratory on ultralight, highly efficient MOCVD GaAs solar cells purchased from EPI Materials Ltd. (EML) of the UK are presented. These cells were also used for modeling the flight characteristics of UAV aircraft.

Geis, Jack↗
NASA NTRS2004

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation thru the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The application of many different types of AI techniques for flight will be explored during this research effort. The research concentration will be directed to the application of different AI methods within the UAV arena. By evaluating AI approaches, which will include Expert Systems, Neural Networks, Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI techniques applied to achieving true autonomous operation of these systems thus providing new intellectual merit to this research field. The major area of discussion will be limited to the UAV. The systems of interest include small aircraft, insects, and miniature aircraft. Although flight systems will be explored, the benefits should apply to many Unmanned Vehicles such as: Rovers, Ocean Explorers, Robots, and autonomous operation systems. The flight system will be broken down into control agents that will represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework of applying a Security Overseer will be added in an attempt to address errors, emergencies, failures, damage, or over dynamic environment. The chosen control problem was the landing phase of UAV operation. The initial results from simulation in FlightGear are presented.

Dufrene, Warren R., Jr.↗
NASA NTRS2004

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation through the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The applications of several different types of AI techniques for flight are explored during this research effort. The research concentration is directed to the application of different AI methods within the UAV arena. By evaluating AI and biological system approaches. which include Expert Systems, Neural Networks. Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI and CAS techniques applied to achieving true autonomous operation of these systems. Although flight systems were explored, the benefits should apply to many Unmanned Vehicles such as: Rovers. Ocean Explorers, Robots, and autonomous operation systems. A portion of the flight system is broken down into control agents that represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework for applying an intelligent agent is presented. The initial results from simulation of a security agent for communication are presented.

Dufrene, Warren R., Jr.↗
NASA NTRS2011

AVIATR - Aerial Vehicle for In-situ and Airborne Titan Reconnaissance A Titan Airplane Mission Concept

We describe a mission concept for a stand-alone Titan airplane mission: Aerial Vehicle for In-situ and Airborne Titan Reconnaissance (AVIATR). With independent delivery and direct-to-Earth communications, AVIATR could contribute to Titan science either alone or as part of a sustained Titan Exploration Program. As a focused mission, AVIATR as we have envisioned it would concentrate on the science that an airplane can do best: exploration of Titan's global diversity. We focus on surface geology/hydrology and lower-atmospheric structure and dynamics. With a carefully chosen set of seven instruments-2 near-IR cameras, 1 near-IR spectrometer, a RADAR altimeter, an atmospheric structure suite, a haze sensor, and a raindrop detector-AVIATR could accomplish a significant subset of the scientific objectives of the aerial element of flagship studies. The AVIATR spacecraft stack is composed of a Space Vehicle (SV) for cruise, an Entry Vehicle (EV) for entry and descent, and the Air Vehicle (AV) to fly in Titan's atmosphere. Using an Earth-Jupiter gravity assist trajectory delivers the spacecraft to Titan in 7.5 years, after which the AVIATR AV would operate for a 1-Earth-year nominal mission. We propose a novel 'gravity battery' climb-then-glide strategy to store energy for optimal use during telecommunications sessions. We would optimize our science by using the flexibility of the airplane platform, generating context data and stereo pairs by flying and banking the AV instead of using gimbaled cameras. AVIATR would climb up to 14 km altitude and descend down to 3.5 km altitude once per Earth day, allowing for repeated atmospheric structure and wind measurements all over the globe. An initial Team-X run at JPL priced the AVIATR mission at FY10 $715M based on the rules stipulated in the recent Discovery announcement of opportunity. Hence we find that a standalone Titan airplane mission can achieve important science building on Cassini's discoveries and can likely do so within a New Frontiers budget.

UAV↗
NASA NTRS1993

Transition aerodynamics for 20-percent-scale VTOL unmanned aerial vehicle

An investigation was conducted in the Langley 14- by 22-Foot Subsonic Tunnel to establish a transition data base for an unmanned aerial vehicle utilizing a powered-lift ejector system and to evaluate alterations to the ejector system for improved vehicle performance. The model used in this investigation was a 20-percent-scale, blended-body, arrow-wing configuration with integrated twin rectangular ejectors. The test was conducted from hover through transition conditions with variations in angle of attack, angle of sideslip, free-stream dynamic pressure, nozzle pressure ratio, and model ground height. Force and moment data along with extensive surface pressure data were obtained. A laser velocimeter technique for measuring inlet flow velocities was demonstrated at a single flow condition, and also a low order panel method was successfully used to numerically simulate the ejector inlet flow.

Kjerstad, Kevin J.↗
NASA NTRSDate not supplied

Define Minimum Safe Operational Volume for Aerial Vehicles in Upper Class E Airspace

This work presents both analytical and simulation studies for defining the minimum safe operational volume needed for aerial vehicles operating in upper Class E airspace. The analytical study is conducted first to investigate factors that affect the minimum safe operational volume, such as position errors, wake/vortex, vehicle maneuverability, communication and surveillance, control/operator response time. A mathematical approach is developed to compute the boundary of the safe operational volume required by the limited aircraft maneuverability and an analytical sensitivity study is then performed with varying parameters. After the analytical study, simulations are then conducted to verify the results from the analytical study. Simulation results will be included in the final paper.

Separation standard, well-clear definition, upper ↗
NASA NTRS1960

Wind-Tunnel Investigation of a Small-Scale Model of an Aerial Vehicle Supported by Tilting Ducted Fans

A wind-tunnel investigation has been made to study the static longitudinal and lateral stability characteristics of a simplified aerial vehicle supported by ducted fans that tilt relative to the airframe. The ducts were in a triangular arrangement with one duct in front and two at the rear in order to minimize the influence of the downwash of the front duct on the rear ducts. The results of the investigation were compared with those of a similar investigation for a tandem two-duct arrangement in which the ducts were fixed (rather than tiltable) relative to the airframe, since the three-duct configuration had been devised in an attempt to avoid some of the deficiencies of the tandem fixed-duct configuration. The results of the investigation indicated that the tilting-duct arrangement had less noseup pitching moment for a given forward speed than the tandem fixed-duct arrangement. The model had less angle-of-attack instability than the tandem fixed-duct arrangement. The model was directionally unstable but had a positive dihedral effect throughout the test speed range.

Smith, Charles C., Jr.↗
NASA NTRSDate not supplied

Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics

This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.

Physics-Informed Machine Learning↗
NASA NTRS2007

An Analysis of Fuel Cell Options for an All-electric Unmanned Aerial Vehicle

A study was conducted to assess the performance characteristics of both PEM and SOFC-based fuel cell systems for an all-electric high altitude, long endurance Unmanned Aerial Vehicle (UAV). Primary and hybrid systems were considered. Fuel options include methane, hydrogen, and jet fuel. Excel-based models were used to calculate component mass as a function of power level and mission duration. Total system mass and stored volume as a function of mission duration for an aircraft operating at 65 kft altitude were determined and compared.

Kohout, Lisa L.↗
NASA NTRS2003

Holarchical Systems and Emotional Holons : Biologically-Inspired System Designs for Control of Autonomous Aerial Vehicles

The BEES (Bio-inspired Engineering for Exploration Systems) for Mars project at NASA Ames Research Center has the goal of developing bio-inspired flight control strategies to enable aerial explorers for Mars scientific investigations. This paper presents a summary of our ongoing research into biologically inspired system designs for control of unmanned autonomous aerial vehicle communities for Mars exploration. First, we present cooperative design considerations for robotic explorers based on the holarchical nature of biological systems and communities. Second, an outline of an architecture for cognitive decision making and control of individual robotic explorers is presented, modeled after the emotional nervous system of cognitive biological systems. Keywords: Holarchy, Biologically Inspired, Emotional UAV Flight Control

Ippolito, Corey↗
NASA NTRS2004

Human Systems Integration and Automation Issues in Small Unmanned Aerial Vehicles

The goal of this report is to identify Human System Integration (HSI) and automation issues that contribute to improved effectiveness and efficiency in the operation of U.S. military Small Unmanned Aerial Vehicles (SUAVs). HSI issues relevant to SUAV operations are reviewed and observations from field trials are summarized. Short-term improvements are suggested research issues are identified and an overview is provided of automation technologies applicable to future SUAV design.

DRONES↗
NASA NTRSDate not supplied

Sensing Small Uncrewed Aerial Vehicles with Distributed Radars for Advanced Air Mobility Surveillance

In the context of Advanced and Urban Air Mobility major attention is being reserved to the development of sensing strategies for small Uncrewed Aerial Vehicles (sUAVs) to enable their safe operations in and around urban areas. Such strategies should rely on non-cooperative distributed sensors to strengthen the surveillance solution towards the unreliability of Global Navigation Satellite System (GNSS) positioning information, which is typical for low-altitude-flying platforms in urban regions, and increase the monitored airspace volume. To this aim, this paper proposes a fusion solution for a network of distributed ground-based radars, which can be exploited to not only increase the coverage over large airspace volumes but also improve the overall detectability and traceability of sUAVs by leveraging on multiple views over the same area. The solution exploits a centralized fusion scheme in which measurements collected by each radar are shared with a Fusion Center where Kalman Filtering is exploited to build a unique, fused track. Tests conducted on experimental data collected using two sUAVs as flying targets and three distributed radars showed that the proposed solution can produce an increase in coverage from about 20 % (single radar configuration) to about 80 % of the targets’ flight path, as well as a finer accuracy yielding meter and meter-per-second root mean square error values on position and velocity components.

Federica Vitiello↗
NASA NTRS2015

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗
NASA NTRS2010

Moments of Inertia - Uninhabited Aerial Vehicle (UAV) Dryden Remotely Operated Integrated Drone (DROID)

The objective of this research effort is to determine the most appropriate, cost efficient, and effective method to utilize for finding moments of inertia for the Uninhabited Aerial Vehicle (UAV) Dryden Remotely Operated Integrated Drone (DROID). A moment is a measure of the body's tendency to turn about its center of gravity (CG) and inertia is the resistance of a body to changes in its momentum. Therefore, the moment of inertia (MOI) is a body's resistance to change in rotation about its CG. The inertial characteristics of an UAV have direct consequences on aerodynamics, propulsion, structures, and control. Therefore, it is imperative to determine the precise inertial characteristics of the DROID.

Haro, Helida C.↗
NASA NTRS2010

Moments of Inertia: Uninhabited Aerial Vehicle (UAV) Dryden Remotely Operated Integrated Drone (DROID)

The objective of this research effort is to determine the most appropriate, cost efficient, and effective method to utilize for finding moments of inertia for the Uninhabited Aerial Vehicle (UAV) Dryden Remotely Operated Integrated Drone (DROID). A moment is a measure of the body's tendency to turn about its center of gravity (CG) and inertia is the resistance of a body to changes in its momentum. Therefore, the moment of inertia (MOI) is a body's resistance to change in rotation about its CG. The inertial characteristics of an UAV have direct consequences on aerodynamics, propulsion, structures, and control. Therefore, it is imperative to determine the precise inertial characteristics of the DROID.

Haro, Helida C.↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗