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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Air Traffic Controller Performance and Acceptability of Multiple UAS in a Simulated NAS Environment

Previously, we showed that air traffic controllers (ATCos) rated UAS pilot verbal response latencies as acceptable when a 1.5 s delay was added to the UAS pilot responses, but a 5 s delay was rated as mostly unacceptable. In the present study we determined whether a 1.5 s added delay in the UAS pilots' verbal communications would affect ATCos interactions with UAS and other conventional aircraft when the number and speed of the UAS were manipulated. Eight radar-certified ATCos participated in this simulation. The ATCos managed a medium altitude sector containing arrival aircraft, en route aircraft, and one to four UAS. The UAS were conducting a surveillance mission and flew at either a "slow" or "fast" speed. We measured both UAS and conventional pilots' verbal communication latencies, and obtained ATCos' acceptability ratings for these latencies. Although the UAS pilot response latencies were longer than those of conventional pilots, the ATCos rated UAS pilot verbal communication latencies to be as acceptable as those of conventional pilots. Because the overall traffic load within the sector was held constant, ATCos only performed slightly worse when multiple UAS were in their sector compared to when only one UAS was in the sector. Implications of these findings for UAS integration in the NAS are discussed.

measured response↗
NASA NTRS2016

Identification and Reconfigurable Control of Impaired Multi-Rotor Drones

The paper presents an algorithm for control and safe landing of impaired multi-rotor drones when one or more motors fail simultaneously or in any sequence. It includes three main components: an identification block, a reconfigurable control block, and a decisions making block. The identification block monitors each motor load characteristics and the current drawn, based on which the failures are detected. The control block generates the required total thrust and three axis torques for the altitude, horizontal position and/or orientation control of the drone based on the time scale separation and nonlinear dynamic inversion. The horizontal displacement is controlled by modulating the roll and pitch angles. The decision making algorithm maps the total thrust and three torques into the individual motor thrusts based on the information provided by the identification block. The drone continues the mission execution as long as the number of functioning motors provide controllability of it. Otherwise, the controller is switched to the safe mode, which gives up the yaw control, commands a safe landing spot and descent rate while maintaining the horizontal attitude.

Unmanned aerial system↗
NASA NTRS2021

Fit2Fly Project

NASA’s Fit2Fly project wants to make future commercial drone fleet operations routine, safe, and boring. How can future operators of commercial drone fleets know without a doubt that their aircraft are fit to fly? It is a tough problem, because every flight will effectively break the chain of custody for their aircraft, and having technicians do detailed inspections after each flight is not viable for large, busy fleets.

airworthiness↗
NASA NTRS2021

Assuring Intelligent Systems: Contingency Management for UAS

Unmanned aircraft systems (UAS) collaborate with humans to operate in diverse, safety-critical applications. However, assurance technologies need to be integrated into the design process in order to guarantee safe behavior, thereby enabling UAS operations in the National Airspace System (NAS). In this paper, formal methods are integrated with learning-enabled systems representations. The generation and representation of knowledge are captured via monadic second-order logic rules in the cognitive architecture Soar. These rules are translated into timed automata, and a proof of correctness for the translation is provided so that safety and liveness properties can be checked in the formal verification environment Uppaal. This approach is agnostic to the learning mechanism used to generate the learned rules (e.g., chunking, etc.). An example of a fault-tolerant, learning-enabled UAS deciding which of four contingency procedures to execute under a lost link scenario while overflying an urban area is used to illustrate the approach.

Intelligent Systems↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRS2007

UAVSAR System Overview

The Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project began as an Instrument Incubator Program (IIP) out of the NASA ESTO Program Office. After a year of study JPL presented to NASA an instrument concept that could be accommodated on the desired class of platforms, that would meet the original IIP science and instrument objectives and could be expanded to meet future airborne radar science needs. The UAVSAR project is a four year program consisting of a 3 year phase in which the radar system is designed and fabricated, the platform is modified, radar is installed on the aircraft and an initial flight testing program is begun. The last year of the program is designed to collect repeat pass data, to improve system robustness and to validate that the scientific objectives of the sensor are being met.

Cutler, Frank↗
NASA NTRS2012

An Autonomous Autopilot Control System Design for Small-Scale UAVs

This paper describes the design and implementation of a fully autonomous and programmable autopilot system for small scale autonomous unmanned aerial vehicle (UAV) aircraft. This system was implemented in Reflection and has flown on the Exploration Aerial Vehicle (EAV) platform at NASA Ames Research Center, currently only as a safety backup for an experimental autopilot. The EAV and ground station are built on a component-based architecture called the Reflection Architecture. The Reflection Architecture is a prototype for a real-time embedded plug-and-play avionics system architecture which provides a transport layer for real-time communications between hardware and software components, allowing each component to focus solely on its implementation. The autopilot module described here, although developed in Reflection, contains no design elements dependent on this architecture.

Ippolito, Corey↗
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 NTRS2009

Regenerative Performance of the NASA Symmetrical Solid Oxide Fuel Cell Design

The NASA Glenn Research Center is developing both a novel cell design (BSC) and a novel ceramic fabrication technique to produce fuel cells predicted to exceed a specific power density of 1.0 kW/kg. The NASA Glenn cell design has taken a completely different approach among planar designs by removing the metal interconnect and returning to the use of a thin, doped LaCrO3 interconnect. The cell is structurally symmetrical. Both electrodes support the thin electrolyte and contain micro-channels for gas flow-- a geometry referred to as a bi-electrode supported cell or BSC. The cell characteristics have been demonstrated under both SOFC and SOE conditions. Electrolysis tests verify that this cell design operates at very high electrochemical voltage efficiencies (EVE) and high H2O conversion percentages, even at the low flow rates predicted for closed loop systems encountered in unmanned aerial vehicle (UAV) applications. For UAVs the volume, weight and the efficiency are critical as they determine the size of the water tank, the solar panel size, and other system requirements. For UAVs, regenerative solid oxide fuel cell stacks (RSOFC) use solar panels during daylight to generate power for electrolysis and then operate in fuel cell mode during the night to power the UAV and electronics. Recent studies, performed by NASA for a more electric commercial aircraft, evaluated SOFCs for auxiliary power units (APUs). System studies were also conducted for regenerative RSOFC systems. One common requirement for aerospace SOFCs and RSOFCs, determined independently in each application study, was the need for high specific power density and volume density, on the order of 1.0 kW/kg and greater than 1.0 kW/L. Until recently the best reported performance for SOFCs was 0.2 kW/kg or less for stacks. NASA Glenn is working to prototype the light weight, low volume BSC design for such high specific power aerospace applications.

Cable, Thomas L.↗
NASA NTRSDate not supplied

Examining the Role of IMU Health Characterization and Monitoring in UAS Safety

Inertial measurement units (IMUs) can be vital for vehicle attitude and position estimation in unmanned aerial vehicles (UAVs). Degradation in these units could result in incorrect position estimates that are utilized in vehicle control, trajectory prediction, and other critical systems; however, the modes and effects of degradation in these IMUs are not well understood. In order to quantify the risk posed by degradation in these sensors, a study was conducted on the types of IMUs prevalent in the commercial market, their known failure modes, and the applicability to health monitoring methods for risk reduction and increased safety in an increasingly autonomous airspace. First, use cases and failure modes of the various types and reliability of commercially available IMUs were reviewed, and knowledge gaps and issues in the field were identified. Noting that inexpensive and lightweight MEMS (Microelectromechanical Systems) IMUs are some of the most commonly used but least reliable sensors in sUAS (small Unmanned Aerial Systems), five inexpensive MEMS IMUs were chosen for a performance evaluation study, selected from the Pixhawk autopilot systems, hobby sUAS, and prior NASA experimental studies. For each of these IMUs, a 10-minute bias test and a 12-hour drift test were performed for the accelerometers in a benchtop setting, using a BeagleBone Black for data collection. Using these results, the sensors’ performance is compared to their reported specifications, and the utility of implementing diagnostic methods for MEMS IMUs for research and commercial applications is evaluated. The paper concludes with a planned study for the evaluation of vibration-induced degradation for the selected sensors.

IMUs↗
NASA NTRS2009

Wrap-Around Out-the-Window Sensor Fusion System

The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.

Fox, Jeffrey↗
NASA NTRS2004

A Water Vapor Differential Absorption LIDAR Design for Unpiloted Aerial Vehicles

This system study proposes the deployment of a water vapor Differential Absorption LIDAR (DIAL) system on an Altair unmanned aerial vehicle (UAV) platform. The Altair offers improved payload weight and volume performance, and longer total flight time as compared to other commercial UAV's. This study has generated a preliminary design for an Altair based water vapor DIAL system. The design includes a proposed DIAL schematic, a review of mechanical challenges such as temperature and humidity stresses on UAV deployed DIAL systems, an assessment of the available capacity for additional instrumentation (based on the proposed design), and an overview of possible weight and volume improvements associated with the use of customized electronic and computer hardware, and through the integration of advanced fiber-optic and laser products. The results of the study show that less than 17% of the available weight, less than 19% of the volume capacity, and approximately 11% of the electrical capacity is utilized by the proposed water vapor DIAL system on the Altair UAV.

DeYoung, Russell J.↗
NASA NTRS2000

Coherent Doppler Laser Radar: Technology Development and Applications

NASA's Marshall Space Flight Center has been investigating, developing, and applying coherent Doppler laser radar technology for over 30 years. These efforts have included the first wind measurement in 1967, the first airborne flights in 1972, the first airborne wind field mapping in 1981, and the first measurement of hurricane eyewall winds in 1998. A parallel effort at MSFC since 1982 has been the study, modeling and technology development for a space-based global wind measurement system. These endeavors to date have resulted in compact, robust, eyesafe lidars at 2 micron wavelength based on solid-state laser technology; in a factor of 6 volume reduction in near diffraction limited, space-qualifiable telescopes; in sophisticated airborne scanners with full platform motion subtraction; in local oscillator lasers capable of rapid tuning of 25 GHz for removal of relative laser radar to target velocities over a 25 km/s range; in performance prediction theory and simulations that have been validated experimentally; and in extensive field campaign experience. We have also begun efforts to dramatically improve the fundamental photon efficiency of the laser radar, to demonstrate advanced lower mass laser radar telescopes and scanners; to develop laser and laser radar system alignment maintenance technologies; and to greatly improve the electrical efficiency, cooling technique, and robustness of the pulsed laser. This coherent Doppler laser radar technology is suitable for high resolution, high accuracy wind mapping; for aerosol and cloud measurement; for Differential Absorption Lidar (DIAL) measurements of atmospheric and trace gases; for hard target range and velocity measurement; and for hard target vibration spectra measurement. It is also suitable for a number of aircraft operations applications such as clear air turbulence (CAT) detection; dangerous wind shear (microburst) detection; airspeed, angle of attack, and sideslip measurement; and fuel savings through headwind minimization. In addition to the airborne and space platforms, a coherent Doppler laser radar system in an unmanned aerial vehicle (UAV) could provide battlefield weather and target identification.

Kavaya, Michael J.↗
NASA NTRS2017

Natural Language Based Multimodal Interface for UAV Mission Planning

As the number of viable applications for unmanned aerial vehicle (UAV) systems increases at an exponential rate, interfaces that reduce the reliance on highly skilled engineers and pilots must be developed. Recent work aims to make use of common human communication modalities such as speech and gesture. This paper explores a multimodal natural language interface that uses a combination of speech and gesture input modalities to build complex UAV flight paths by defining trajectory segment primitives. Gesture inputs are used to define the general shape of a segment while speech inputs provide additional geometric information needed to fully characterize a trajectory segment. A user study is conducted in order to evaluate the efficacy of the multimodal interface.

Chandarana, Meghan↗