Multi-fidelity Computational Noise Prediction of Representative Small Unmanned Aerial System (sUAS) Rotors
Explore the source record for details and available documents.
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
An onboard risk management automation design is presented based on run-time assurance principles, as well as the concept for In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. The automation is designed to operate independently of the autopilot and perform real-time risk assessment spanning multiple classes of hazards, predict constraint violations, and track autopilot states. In the event of elevated risk conditions or predicted constraint violations, the automation will select from a set of available contingencies and trigger autopilot mode changes if necessary to mitigate risk exposure. The onboard automation also informs the remote operator/pilot of what the independent monitor is observing and any contingency decisions or actions that may arise during flight. Details of an implementation of this design and results of verification and validation activities, as required to meet stringent NASA software and system assurance standards, are also presented. This includes simulation and flight testing using small unmanned aircraft systems.
Explore the source record for details and available documents.
The Supplemental Data Services Provider-Consolidated Dashboard (SDSP-CD) is a graphical user interface (GUI) that displays the results of predictive tools in a single location. It is intended to be used in the preflight planning phase of an operation to allow users to proactively assess predicted flight hazards off-line; expanding an operator’s overall situational awareness of a flight plan and providing an opportunity for decision making and assessing the associated risks prior to flight. Hazard data and risk predictions are informative but can be complex to read and understand. However, presented visually and in relation to flight parameters (such as flight path), the nature and significance of hazards become much more evident. The SDSP-Consolidation Dashboard interface was designed to offer a means to present the results of hazard services in an easy to-use format. Two usability studies were run to explore what features might make a suite of hazard assessment services easy to use, and to assess the SDSP-CD interface. The first study evaluated the presentation of information on the GUI and the second evaluated users’ ability to understand and use the information. The studies gathered valuable information about how users approach a hazard assessment task and interpret information from the interface. Many suggestions were given for improving the interface’s information display, to allow users to more quickly understand and interpret the information being presented.
The Supplemental Data Services Provider-Consolidated Dashboard (SDSP-CD) is a graphical user interface (GUI) that displays the results of predictive tools in a single location. It is intended to be used in the prefight planning phase of an operation to allow users to proactively assess predicted flight hazards off-line; expanding an operator’s overall situational awareness of a flight plan and providing an opportunity for decision making and assessing the associated risks prior to flight. Hazard data and risk predictions are informative but can be complex to read and understand. However, presented visually and in relation to flight parameters (such as flight path), the nature and significance of hazards become much more evident. The SDSP-Consolidation Dashboard interface was designed to offer a means to present the results of hazard services in an easy to-use format. Two usability studies were run to explore what features might make a suite of hazard assessment services easy to use, and to assess the SDSP-CD interface. The first study evaluated the presentation of information on the GUI and the second evaluated users’ ability to understand and use the information. The studies gathered valuable information about how users approach a hazard assessment task and interpret information from the interface. Many suggestions were given for improving the interface’s information display, to allow users to more quickly understand and interpret the information being presented
Explore the source record for details and available documents.
Small unmanned aerial systems (sUAS) have the potential for a large array of highly-beneficial applications. These applications are too numerous to comprehensively list, but include search and rescue, fire spotting, precision agriculture, etc. to name a few. Typically sUAS vehicles weigh less than 55 lbs and will be performing flight operations in the National Air Space (NAS). Certain sUAS applications, such as package delivery, will include operations in the close proximity of the general public. The full benefit from sUAS is contingent upon the resolution of several technological areas in order to provide an acceptable level of risk for widespread sUAS operations. Operations of sUAS vehicles pose risks to people and property on the ground as well as manned aviation. Several of the more significant sUAS technological areas include, but are not limited to: autonomous sense and avoid and deconfliction of sUAS from other sUAS and manned aircraft, communications and interfaces between the vehicle and human operators, and the overall reliability of the sUAS and constituent subsystems. While all of the technological areas listed contribute significantly to the safe execution of the sUAS flight operations, contingency or emergency systems can greatly contribute to sUAS risk mitigations to manage situations where the vehicle is in distress. The Safe2Ditch (S2D) system is an autonomous crash management system for sUAS. Its function is to enable sUAS to execute emergency landings and avoid injuring people on the ground, damaging property, and lastly preserving the sUAS and payload. A sUAS flight test effort was performed to test the integration of sub-elements of the S2D system with a representative sUAS multi-rotor.
This Cognitive Task Analysis (CTA) study was designed to understand the capability of the m:N Tactical Operator (TO) interfaces developed by the Human-Autonomy Teaming Laboratory at NASA Ames to support operators responsible for simplified pilot operations of 100 independently operated small UAS (sUAS) in a constrained geographic area. The m:N sUAS TO interface includes a central Tactical Situation Display (TSD) digital map with moving icons reflecting the sUAS location and planned flight route. The interface also has two side panels. The left panel includes a tabular list of UAS assets and mission tasking, a list of recently viewed assets, and a list of events and alerts. The right panel includes a tabular list of UAS assets and their associated telemetry, text-based chat communication window, and a tabbed checklist window. This CTA was adapted from the incident-based applied cognitive task analysis (Militello & Hutton, 1998) and included demographics questions, scenario-based simulations, a task diagram and knowledge audit methods. In addition to examining the support provided by this m:N sUAS TO interface, this CTA study, conducted with aviation subject matter experts in analogous roles to the future tactical operator, was designed to illuminate and project likely cognitive requirements of the tactical operator. Interviewees participated in two scenario-based simulations using the m:N sUAS TO interfaces. In the first simulation, the interviewees supervised 12 sUAS operating in downtown San Diego, California transiting to and from a central sUAS Hive, restaurant locations, and customer drop off locations. Interviewees were asked to react to a UAS Volume Reservation (UVR) event with a two-phase impact on food delivery operations. In the second scenario, the interviewees supervised 100 sUAS operating in the same airspace and with the same mission. Interviewees used the interfaces to recognize and react to two sUAS air vehicle problems. After each scenario, we asked the interviewees a semi-structured list of questions to elicit their reflections about using the interfaces. Interviewees were confident in their ability to respond to two off-nominal situations in each simulation. Interviewees felt that, given high levels of automation on the sUAS, they would be able to manage the events without requiring additional support or handing off the sUAS to a colleague or supervisor in both the n=12 or n=100 sUAS settings. In the n=12 sUAS condition, interviewees used the center map to understand the asset location and progress along with mission tasking. An additional display window, Asset Telemetry, helped interviewees understand battery state and sUAS altitude. When the number of sUAS increased, interviewees altered their behavior. Rather than maintaining awareness of individual assets, interviewees appeared to become more reactive, managing exceptions. Interviewees reported that they spent less time looking at the nominal aircraft, and focused their attention primarily on the off-nominal aircraft. In addition, in the second simulation with n=100 sUAS, interviewees reported that they relied more on the side panels (Mission Timeline and Asset Telemetry) to gather information.
With many applications envisioned for small Unmanned Aircraft Systems (sUAS), and potentially millions of sUAS expected to be in operation in the future, the electromagnetic interference environment associated with the sUAS is of interest to understanding the potential performance impacts on the sUAS command and control communications link as well as the sUAS payload and payload links. As part of NASA's UAS Traffic Management (UTM) Project, flight experiments are planned to characterize the RF environment at altitudes up to 400 ft to better understand how UTM command and control links can be expected to perform. The flight experiments will use an RF channel sensing payload attached to an sUAS. In terms of the payload being capable of measuring relatively low level signals at altitude, electromagnetic interference (EMI) emanating from the sUAS vehicle itself could potentially complicate the measurement process. For this reason, NASA was interested in measuring the EMI performance of the sUAS planned for these flight experiments, a DJI model S1000. The S1000 was thus measured in a controlled EMI test chamber at the NASA Ames Research Center. The S1000 is a carbon fiber based platform with eight rotors. As such, the EMI test results represent potential performance of a number of similar sUAS types. sUAS platforms significantly different from the S1000 may also require EMI testing, and the method employed for NASA's S1000 EMI tests can be applied to other platforms. In this paper we describe the UTM project, the RF channel sensing payload, the EMI testing method and EMI test results for the S1000, and discuss the implications of these results.
With many applications envisioned for small Unmanned Aircraft Systems (sUAS), and potentially millions of sUAS expected to be in operation in the future, the electromagnetic interference environment associated with the sUAS is of interest to understanding the potential performance impacts on the sUAS command and control communications link as well as the sUAS payload and payload links. As part of NASAâ€"TM"s UAS Traffic Management (UTM) Project, flight experiments are planned to characterize the RF environment at altitudes up to 400 ft to better understand how UTM command and control links can be expected to perform. The flight experiments will use an RF channel sensing payload attached to an sUAS. In terms of the payload being capable of measuring relatively low level signals at altitude, electromagnetic interference (EMI) emanating from the sUAS vehicle itself could potentially complicate the measurement process. For this reason, NASA was interested in measuring the EMI performance of the sUAS planned for these flight experiments, a DJI model S1000. The S1000 was thus measured in a controlled EMI test chamber at the NASA Ames Research Center. The S1000 is a carbon fiber based platform with eight rotors. As such, the EMI test results represent potential performance of a number of similar sUAS types. sUAS platforms significantly different from the S1000 may also require EMI testing, and the method employed for NASAâ€"TM"s S1000 EMI tests can be applied to other platforms. In this paper we describe the UTM project, the RF channel sensing payload, the EMI testing method and EMI test results for the S1000, and discuss the implications of these results.
Small unmanned aerial systems (sUAS) have been studied and results indicate that there is a large array of highly-beneficial applications. These applications are too numerous to list, but include search and rescue, fire spotting, precision agriculture, etc. to name a few. Typically sUAS vehicles weigh less than 55 pounds and will be performing flight operations in the presence of manned aircraft and other sUAS. Certain sUAS applications, such as package delivery, will include operations in the close proximity of the general public. The full benefit from sUAS is contingent upon the resolution of several technological areas to enable free and widespread use of these vehicles. Technological areas in question include, but are not limited to: autonomous sense and avoid and deconfliction of sUAS from other sUAS and manned aircraft, communications and interfaces between the vehicle and human operators, and high-reliability autonomous systems. The NASA UAS Traffic Management (UTM) project is endeavoring to develop a traffic management system and concept of operations for these types of vehicles. An extensive sUAS flight test effort was performed to partially address vehicle-related technological areas and to shape an understanding of future developmental and test efforts for vehicles intended to use the UTM traffic management system. The flight testing described herein had the following objectives: 1) Install and test Dedicated Short Range Communications (DSRC) systems developed for the automotive industry for potential sense and avoid sUAS applications; 2) Evaluate the use of cellular 4G systems to provide vehicle control; 3) Obtain high-resolution video imagery in support of image-based optical detection sense and avoid systems; 4) Acquire data in fixed-wing flight to support validation and maturation of an autonomous range containment system known as Safeguard in fixed-wing flight. A total of 53 flights were performed over 12 operational days at Beaver Dam Airpark in Elberon, VA. This work was sponsored by the UTM project that is part of the Aviation Operations and Safety Program (AOSP) at NASA.
The projected use of small unmanned aerial systems (SUAS) in military operations will produce training requirements which go beyond current capabilities. The paper describes the development of prototype training procedures and accompanying research simulations to address this need. We initially constructed a testbed to develop simulation-based training for an SUAS operator equipped with a simulated vertical-lift and land SUAS. However, the required training will go beyond merely training an operator how to pilot an SUAS. In addition to tactics, techniques, and procedures for employment of SUASs, collective training methods must be trained. Moreover, the leader of a unit equipped with SUAS will need to learn how to plan missions which incorporate the SUAS, and take into account air space and frequency management considerations. The demands of the task require the leader to allocate personnel to the SUAS mission, communicate and coordinate with those personnel during the mission, and make use of the information provided. To help address these training issues, we expanded our research testbed to include a command and control node (C2 node), to enable communications between a leader and the SUAS operator. In addition, we added a virtual environment in which dismounted infantry missions can be conducted. This virtual environment provides the opportunity for interactions among human-controlled avatars and non-player characters (NPCs), plus authoring tools to construct scenarios. Using these NPCs, a collective exercise involving friendly, enemy, and civilian personnel can be conducted without the need for a human role-player for every entity. We will describe the results of our first experiment, which examined the ability of players to negotiate use of the C2 node and the virtual environment at the same time, in order to see if this is a feasible combination of tools for training development.
In the marine boundary layer, the exchange of momentum, heat, and moisture occurs between the atmosphere and ocean. Since it is too dangerous for a crewed aircraft to fly close to the ocean surface to directly obtain these measurements, a sUAS (small Uncrewed Aircraft System) is one of the only viable options. On 24 March 2023 a Black Swift Technologies S0 sUAS was deployed from the NOAA P-3 on a calm clear day off the west coast of Florida. For 23 min at the end of the mission, the sUAS flew 8 straight line legs with an average length of 2.15 km, at roughly 10 m above the ocean surface, with wind speeds between 3.0 and 4.5 m s −1 . For the first time over the open ocean using a sUAS, the 4-Hz wind and thermodynamic data was used to calculate surface momentum flux, sensible heat flux, and latent flux using both direct covariance methods and the bulk aerodynamic formulas. Since all the flux quantities can be found using both direct and indirect methods, we are able to calculate the exchange coefficients of momentum flux (C D ), latent heat flux (C E ), and sensible heat flux (C H ) with results that are generally in good agreement with previous studies over the same wind speed range. This study demonstrates the ability of sUAS to measure air-sea interactions. Future intention is to use sUAS to obtain similar measurements in high wind events such as hurricanes which could better help understand hurricane intensification and improve model physics.