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At least 91 records · Page 5

Human Factors of Remotely Piloted Aircraft Systems: Lessons from Incident Reports

It has been estimated that aviation accidents are typically preceded by numerous minor incidents arising from the same causal factors that ultimately produced the accident. Accident databases provide in-depth information on a relatively small number of occurrences, however incident databases have the potential to provide insights into the human factors of Remotely Piloted Aircraft System (RPAS) operations based on a larger volume of less-detailed reports. Currently, there is a lack of incident data dealing with the human factors of unmanned aircraft systems. An exploratory study is being conducted to examine the feasibility of collecting voluntary critical incident reports from RPAS pilots. Twenty-three experienced RPAS pilots volunteered to participate in focus groups in which they described critical incidents from their own experience. Participants were asked to recall (1) incidents that revealed a system flaw, or (2) highlighted a case where the human operator contributed to system resilience or mission success. A total of 90 incidents were reported. This presentation begins with an overview of RPAS human factors and then considers the emerging issues identified by pilot reports.

incidents↗

Assessing the Vulnerability of Unmanned Aircraft Systems to Directed Acoustic Energy

The increasingly large payloads of Unmanned Aircraft Systems (UASs) are exponentially increasing the threat to the nuclear enterprise. Current mitigation using RF interference is effective, but not feasible for fully autonomous systems and is prohibited in many areas. A new approach to UAS threat mitigation is needed that does not create radio interference but is effective against any type of vehicle. At the present time there is no commercial counter-UAS system that directly assaults the mems gyros and accelerometers in the Inertial Measurement Unit on the aircraft. But lab testing has revealed resonances in some IMUs that make them susceptible to moderate amplitude acoustic monotones. Sandia's energetic materials facility has enabled a quick and thorough exploration of UAS vulnerability to directed acoustic energy by using intense acoustic impulses to destabilize or down a UAS. We have: 1) detonated/deflagrated explosive charges of various sizes; 2) accurately measured impulse pressure and pulse duration; 3) determined what magnitude of acoustic insult to the IMU disrupts flight and for how long and; 4) determined if the air blast/shock wave on aircraft/propellers disrupts flight.

42 ENGINEERING↗

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project

Over the past 5 years, the UAS integration into the NAS project has worked to reduce technical barriers to integration. A major focus of this work has been in support of RTCA SC-228. This committee has recently published the first UAS integration minimum performance standards (MOPS). This work has spanned detect and avoid (DAA) as well as command and control comm datalinks. I will discuss DAA efforts with focus on the human systems work. I will discuss how automation was discussed and addressed within this context. ICAO stood up a remotely piloted aircraft systems (RPAS) panel in 2014. They have developed an RPAS manual and are now working to revise existing annexes and standards and recommended practices. The Human In The System (HITS) has worked to infuse human factors guidelines into those documents. I will discuss that effort as well as how ICAO has defined and address autonomy. There is a great deal of interest in the control of multiple vehicles by a single operator. The UAS EXCOM Science and Research Panel (SARP) is holding a workshop on this topic in late June. I will discuss research performed on this topic when I worked for the Army and on-going work within the division and a NATO working group on Human-Autonomy Teaming.

Unmanned Aircraft Systems (UAS)↗

High Altitude Long Endurance (HALE) Unmanned Aircraft System (UAS): Pilot Knowledge, Skills and Abilities

This report summarizes the initial work accomplished by the ACCESS 5 Human System Integration (HSI) team to identify Unmanned Aircraft System (UAS) Pilot Knowledge, Skill and Ability (KSA), Training and Medical requirements. To derive this information the following tasks were accomplished: a) Mission and Function analyses were performed; b) Applicable FARs and FAA Advisory Circulars (ACs) were reviewed; c) Meetings were conducted with NASA and FAA Human Factors personnel; d) Surveys were completed by ACCESS 5 HSI Working group UA Pilots; e) Coordination meetings were conducted with the ACCESS 5 Policy IPT. The results of these efforts were used to develop a summary of the current qualifications. for an individual to function as a Pilot In Command (PIC) for UAs currently flown by UNITE companies, to develop preliminary Pilot KSAs for each phase of flight, and to delineate preliminary Pilot Training and Medical requirements. These results are to be provided to the Policy IPT to support their development of recommendations for UA Pilot Rating Criteria, training and medical qualifications. It is expected that the initially an instrument rated pilot will be required to serve as the PIC. However, as operational experience is gained, and automation is applied to accomplish various system functions, it is expected that pilot rating criteria could be lessened.

Source record↗

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) National Campaign II

The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations-the second or 'Technical Capability Level (TCL 2)' National Campaign of UTM testing-was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; ground-based surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.

Aweiss, Arwa S.↗

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) National Campaign II

The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations—the second or "Technical Capability Level (TCL 2)" National Campaign of UTM testing—was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; ground-based surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.

Aweiss, Arwa↗

Automated Management of Small Unmanned Aircraft System Communications and Navigation Contingency

The number of small Unmanned Aircraft System (sUAS) operating in the low-altitude of the National Airspace System (NAS) has been rapidly increasing in the past few years and this number is expected to grow in the future. However, aside from a few special cases, all sUAS must fly within visual line-of-sight (VLOS) of their operators and this limitation is blocking highly anticipated beyond visual line-of-sight (BVLOS) sUAS applications such as package delivery from practice. To enable routine low-altitude BVLOS operations, there needs to be a traffic management ecosystem that complements the FAA’s Air Traffic Management (ATM) system, which does not provide air traffic services under 400 feet above ground level (AGL). NASA has been pioneering research and development of this ecosystem under UAS Traffic Management (UTM) project since 2015 in a series of Technical Capability Levels (TCL) activities that are increasingly complex. In TCL1, completed in 2015, visual line-of-sight operations such as agriculture, firefighting, and infrastructure monitoring were addressed with a focus on geofencing and operations scheduling. Technologies and requirements needed for BVLOS operations in sparsely populated areas were examined in TCL2 in 2016, and those for operations over moderately populated areas in TCL3 in 2017 and 2018. TCL4 is building on the earlier TCLs and focuses on technologies and requirements for operations in higher-density urban areas for tasks such as newsgathering and package delivery and for managing large-scale contingencies. To coordinate and facilitate the incremental implementation of the UTM ecosystem in the NAS, a Research Transition Team (RTT) has been formed between the FAA, NASA, and industry. The RTT is divided into four subgroups, concept and use case development, data exchange and information architecture, sense and avoid, and communications and navigation (C&N). This paper focuses on C&N subgroup activities, in particular about the development of automated sUAS communications and navigation contingency management. The goal of this development is to prepare sUAS to display predictable behavior while handling C&N off-nominal events. It is expected that the adoption of the presented automated contingency management by the sUAS community will accommodate and inform rulemaking towards governing low-altitude BVLOS operations.

Unmanned Aviation System (UAS) Traffic Management↗

Acoustic Characterization and Prediction of Representative, Small-Scale Rotary-Wing Unmanned Aircraft System Components

In this study, hover performance and acoustic measurements are taken on two different isolated rotors representative of small-scale rotary-wing unmanned aircraft systems (UAS) for a range of rotation rates. Each rotor system consists of two fixed-pitch blades powered by a brushless motor. For nearly the same thrust condition, significant differences in overall sound pressure level (OASPL), up to 8 dB, and directivity were observed between the two rotor systems. Differences are shown to be in part attributed to different rotor tip speeds, along with increased broadband and motor noise levels. In addition to acoustic measurements, aeroacoustic predictions were implemented in order to better understand the noise content of the rotor systems. Numerical aerodynamic predictions were computed using the unsteady Reynoldsaveraged Navier Stokes code OVERFLOW2 on one of the isolated rotors, while analytical predictions were computed using the Propeller Analysis System of the Aircraft NOise Prediction Program (ANOPP-PAS) on the two rotor configurations. Preliminary semi-empirical frequency domain broadband noise predictions were also carried out based on airfoil self-noise theory in a rotational reference frame. The prediction techniques further supported trends identified in the experimental data analysis. The brushless motors were observed to be important noise contributors and warrant further investigation. It is believed that UAS acoustic prediction capabilities must consider both rotor and motor components as part of a combined noise-generating system.

Zawodny, Nikolas S.↗

Aircraft System Noise Prediction Uncertainty Quantification for a Hybrid Wing Body Subsonic Transport Concept

Aircraft system level noise prediction for advanced, unconventional concepts has undergone significant improvement over the past two decades. The prediction modeling uncertainty must be quantified so that potential benefits of unconventional configurations, which are outside of the range of empirical models, can be reliably assessed. This paper builds on previous work in an effort to improve estimates of element prediction uncertainties where the prediction methodology has been improved, or new experimental validation data are available, to provide an estimate of the system level uncertainty in the prediction process. In general, the uncertainty of the prediction will be strongly dependent on the aircraft configuration as well as which technologies are integrated. While the quantitative uncertainty values contained here are specific to the hybrid wing body design presented, the underlying process is the same regardless of configuration. A refined process for determining the uncertainty for each element of the noise prediction is detailed in this paper. The system level uncertainty in the prediction of the aircraft noise is determined at the three certification points, using a Monte Carlo method. Comparisons with previous work show a reduction of 1 EPNdB in the 95%coverage interval of the cumulative noise level. The largest impediment for continued reduction in uncertainty for the hybrid wing body concept is the need for improved modeling and validation experiments for fan noise, propulsion airframe aeroacoustic effects, and the Krueger flap, which comprise the bulk of the uncertainty in the cumulative certification noise level.

June, Jason C.↗

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: Terminal Operations HITL 1: Primary Results

This presentation covers the primary results of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project Terminal Operations Foundational Human-in-the-Loop (HITL) simulation. The study tasked 16 pilots (half with manned piloting experience, and the other half with unmanned piloting experience) with maintaining "well clear" from other traffic while performing three different types of approaches into the Santa Rosa airport. A detect and avoid (DAA) system was provided to pilots to assist their ability to manage separation. The DAA system used in this test conformed to the criteria defined by RTCA Special Committee 228 (SC-228) in their Phase 1 Minimum Operational Performance Standards (MOPS) for UAS intending to operate in the NAS. The Phase 1 system was not designed to account for terminal operations, focusing instead on en route operations. To account for this, three different alerting and guidance configurations were presently tested in order to determine their effect on pilots operating the system in the terminal area. Results indicated that pilots with the alerting and guidance condition that provided the least amount of assistance (fewer alert levels and guidance types) experienced slightly increased pilot response times and rates of losses of separation. Additional data is presented on the effects of approach type and descriptive data on pilot maneuver preferences and ATC interoperability.

terminal environmen↗

Practical Aspects of the Frequency Domain Approach for Aircraft System Identification

Practical aspects of the frequency-domain approach for aircraft system identification are explained and demonstrated. Topics related to experiment design, flight data analysis, and dynamic modeling are included. For demonstration purposes, simulated time series data and simulated flight data from an F-16 nonlinear simulation with realistic noise are used. This approach enables detailed evaluations of the techniques and results, because the true characteristics of the data and aircraft dynamics are known for the simulated data. Analytical techniques and practical considerations are examined for the finite Fourier transform, nonparametric frequency response estimation, parametric modeling in the frequency domain, experiment design for frequency-domain modeling, data analysis and modeling in the frequency domain, and real-time calculations. Flight data from a subscale jet transport aircraft are used to demonstrate some of the techniques and technical issues.

Morelli, Eugene A.↗

Supporting the Use of Unmanned Aircraft Systems(UAS) for Global Science Observations in Civil and Segregated Airspace

Unmanned Aircraft Systems (UAS) are growing more popular within the earth science community as a way to augment measurements currently made with manned aircraft. UAS arc uniquely suited for applications that require long dwell times and/or in locations that are generally too dangerous for manned aircraft. Environmental monitoring in areas like the Arctic or obtaining data within a hurricane are just a couple of examples of many applications to which UAS are ideally suited. However, UAS are not without their challenges. Most unmanned aircraft are unable to meet current airspace regulations that are in place for manned aircraft, and specific airspace standards and regulations for unmanned aircraft do not exist. As a result, gaining access to civil airspace for flights is very difficult around the world. Under Term of Reference 48 within the ISPRS Commission 1, WGI/I: Standardization of Aircraft Interfaces, efforts have been made to understand and quantify the current state of UAS airspace access on a global scale. The results of these efforts will be presented along with examples of successful science missions that have been conducted internationally during the past year.

Mulac, B. L.↗

Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

fuzzy logic↗

Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

UAS↗

Automated Management of Small Unmanned Aircraft System (UAS) Communications and Navigation Contingency

The number of small Unmanned Aircraft System (sUAS) operating in the low-altitude of the National Airspace System (NAS) has been rapidly increasing in the past few years and this number is expected to grow in the future. However, aside from a few special cases, all sUAS must fly within visual line-of-sight (VLOS) of their operators and this limitation is blocking highly anticipated beyond visual line-of-sight (BVLOS) sUAS applications such as package delivery from practice. To enable routine low-altitude BVLOS operations, there needs to be a traffic management ecosystem that complements the FAA’s Air Traffic Management (ATM) system, which does not provide air traffic services under 400 feet above ground level (AGL). NASA has been pioneering research and development of this ecosystem under UAS Traffic Management (UTM) project since 2015 in a series of Technical Capability Levels (TCL) activities that are increasingly complex. In TCL1, completed in 2015, visual line-of-sight operations such as agriculture, firefighting, and infrastructure monitoring were addressed with a focus on geofencing and operations scheduling. Technologies and requirements needed for BVLOS operations in sparsely populated areas were examined in TCL2 in 2016, and those for operations over moderately populated areas in TCL3 in 2017 and 2018. TCL4 is building on the earlier TCLs and focuses on technologies and requirements for operations in higher-density urban areas for tasks such as newsgathering and package delivery and for managing large-scale contingencies. To coordinate and facilitate the incremental implementation of the UTM ecosystem in the NAS, a Research Transition Team (RTT) has been formed between the FAA, NASA, and industry. The RTT is divided into four subgroups, concept and use case development, data exchange and information architecture, sense and avoid, and communications and navigation (C&N). This paper focuses on C&N subgroup activities, in particular about the development of automated sUAS communications and navigation contingency management. The goal of this development is to prepare sUAS to display predictable behavior while handling C&N off-nominal events. It is expected that the adoption of the presented automated contingency management by the sUAS community will accommodate and inform rulemaking towards governing low-altitude BVLOS operations.

Unmanned Aviation Systems (UAS) Traffic Management↗

A parametric analysis of transport aircraft system weights and costs

In determining unit and operating costs for advanced aircraft, it has been found that by having first-order weight and performance approximations for the aircraft systems and structural components, a step increase in cost prediction accuracy results. This paper presents first-order approximation equations for these systems and components. These equations were developed from data for most current jet transports, and they have been ordered to use a minimum number of performance parameters such as aircraft style, number of passengers, empty and gross weight, cargo load, and operating range. A NASA Ames Research Center aircraft cost program has been used to compare calculated and actual weights for the same aircraft. Good aircraft cost correlation is shown to exist between calculated first-order and actual aircraft weight data.

Anderson, J. L.↗