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299 records · Page 17

Vortex Wake Turbulence: Flight Tests Conducted During 1970

A flight test program designed to obtain data on the characteristics of wing-tip vortices generated by large jet aircraft was initiated on 12 February 1970. The objective was to update the interim air traffic separation standards issued on 21 January 1970 restricting the airspace behind the B-747 and C-5A aircraft 60 deg either side and 2,000 feet below to a distance of 10 miles. The program involved flight tests at three different locations, which were conducted simultaneously. (1) At the Edwards Air Force Base test range, a NASA CV-990 and F-104 probed the vortices of a C-5A. This supplemented previous flight tests in U-3A and F-104 behind a B-52 and C-5A. (2) At Seattle, the Boeing Company probed the vortices of a B-747 and a B-707-300 with a B-737 and F-86. In addition, approach, landing, takeoff, and crossing runway tests were conducted with a B-737 trailing a B-747. Immediately following the Edwards tests, the NASA CV-990 proceeded to Seattle and engaged in probing flights behind the same B-747 and B-707-300. (3) At Idaho Falls, Idaho, FAA personnel, utilizing the Atomic Energy Commission and Environmental Science Services Administration facilities and a 200 ft, instrumented tower, conducted 114 flights past the tower. The aircraft were positioned to permit their vortices to descend into the air flow sensors and smoke generated from various levels on the tower. This permitted obtaining measurements and photography of the vortex core diameters, tangential velocities and related characteristics. Immediately following this test phase, analysis of the interrelated data was completed by the project managers representing NASA, Boeing and FAA and a Compilation of Work Papers was issued on 30 April 1970. These data were the basis of a revised General Notice issued on 26 February 1970 which, in essence, modified the restricted airspace to five miles behind heavy jets in the 300,000 lb. gross takeoff weight category.

Source record↗

NextGen Flight Deck Surface Trajectory-Based Operations (STBO): Contingency Holds

The purpose of this pilot-in-the-loop taxi simulation was to investigate a NextGen Surface Trajectory-Based Operations (STBO) concept called "contingency holds." The contingency-hold concept parses a taxi route into segments, allowing an air traffic control (ATC) surface traffic management (STM) system to hold an aircraft when necessary for safety. Under nominal conditions, if the intersection or active runway crossing is clear, the hold is removed, allowing the aircraft to continue taxiing without slowing, thus improving taxi efficiency, while minimizing the excessive brake use, fuel burn, and emissions associated with stop-and-go taxi. However, when a potential traffic conflict exists, the hold remains in place as a fail-safe mechanism. In this departure operations simulation, the taxi clearance included a required time of arrival (RTA) to a specified intersection. The flight deck was equipped with speed-guidance avionics to aid the pilot in safely meeting the RTA. On two trials, the contingency hold was not released, and pilots were required to stop. On two trials the contingency hold was released 15 sec prior to the RTA, and on two trials the contingency hold was released 30 sec prior to the RTA. When the hold remained in place, all pilots complied with the hold. Results also showed that when the hold was released at 15-sec or 30-sec prior to the RTA, the 30-sec release allowed pilots to maintain nominal taxi speed, thus supporting continuous traffic flow; whereas, the 15-sec release did not. The contingency-hold concept, with at least a 30-sec release, allows pilots to improve taxiing efficiency by reducing braking, slowing, and stopping, but still maintains safety in that no pilots "busted" the clearance holds. Overall, the evidence suggests that the contingency-hold concept is a viable concept for optimizing efficiency while maintaining safety.

NextGen↗

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..

Horio, Brant M.↗

An Impedance-Based Complexity Metric for Unmanned Aircraft System Traffic Scenario Classification

This paper introduces an impedance-based metric to capture the complexity of a given unmanned aircraft system traffic scenario. The metric accounts for both the number of aircraft and the traffic flow pattern. The work presented here extends an earlier approach that introduced another scenario complexity metric based on the number of potential conflicts weighted by the conflict resolution cost associated. Complexity measurements for randomly-generated scenarios were produced through high-fidelity fast-time simulations and treated as baseline. Then the impedance based metric was evaluated, for the same scenarios, without the need for an actual flight simulation and a conflict resolution method. The results show that the impedance-based metric has a strong correlation to the baseline data and performs marginally better than the weighted conflict-based complexity metric introduced in the earlier work. The metric computation generates impedance maps which are useful for identifying high complexity regions in a scenario, where flight plan changes might be necessitated. This metric can therefore be used, in conjunction with other complexity metrics, to inform adequate traffic management strategies and classify a traffic scenario as acceptable, unacceptable or acceptable with changes made to flight plans that pass through the high complexity regions. The metric can also be used as a guidance metric for strategic conflict management methods.

Complexity↗

Benefits Analysis of Wind-Optimal Operations For Trans-Atlantic Flights

North Atlantic Tracks are trans-Atlantic routes across the busiest oceanic airspace in the world. This study analyzes and compares current flight-plan routes to wind-optimal routes for trans-Atlantic flights in terms of aircraft fuel burn, emissions and the associated climate impact. The historical flight track data recorded by EUROCONTROL's Central Flow Management Unit is merged with data from FAA's Enhanced Traffic Management System to provide an accurate flight movement database containing the highest available flight path resolution in both systems. The combined database is adopted for airspace simulation integrated with aircraft fuel burn and emissions models, contrail models, simplified climate response models, and a common climate metric to assess the climate impact of flight routes within the Organized Track System (OTS). The fuel burn and emissions for the tracks in the OTS are compared with the corresponding quantities for the wind-optimized routes to evaluate the potential environmental benefits of flying wind-optimal routes in North Atlantic Airspace. The potential fuel savings and reduction in emissions depend on existing inefficiencies in current flight plans, atmospheric conditions and location of the city-pairs. The potential benefits are scaled by comparing them with actual flight tests that have been conducted since 2010 between a few city-pairs in the transatlantic and trans-pacific region to improve fuel consumption and reduce the environmental impact of aviation.

air traffic optimization↗

Accrued Delay Application in Trajectory-Based Operations

The air traffic management system lacks integration among its elements often due to using inconsistent information, models, and metrics about the traffic. Transitioning to trajectory-based operations, whereby flights are managed by full trajectories in space and time, will enable more integration, with the help of increased automation. Building on trajectory-based operations, an "accrued delay" metric is proposed, which continuously measures the amount of delay that a flight has accumulated up to the current time, including delays incurred during the current flight and inherited from previous flights through the turnaround process. Through a time-based metering and scheduling example, we show how using accrued delay as a metric can help integrate the decision-making across multiple decision horizons, leading to more efficient and balanced access to airspace services. We show that when prioritizing flights that have already accrued high delay because of a constrained runway resource, significant gains are achieved in terms of reducing total delay and its variance. We studied the sensitivity of these gains to numerous factors, such as time-based versus distance-based horizons, horizon size, and errors in conformance to scheduled times.

delay; trajectory-based operations; time-based flo↗

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗

Reducing Departure Delays for Adjacent Center Airports using Time-Based Flow Management Scheduler: Checkbox On or Off?

The Federal Aviation Administration (FAA) has enhanced the Time-Based Flow Management (TBFM) scheduling tool with a "Checkbox ON" vs. "OFF" function which allows Traffic Management Coordinators (TMCs) to make room in a crowded arrival stream for a departure. When scheduling a departure, having the checkbox ON can delay the Scheduled Times of Arrivals (STAs) of the airborne flights upstream of the TBFM freeze horizons and can compress these flights to their minimum required spacing, thereby creating a slot for a departure. Hence, having the checkbox ON can reduce the frequent ground delays of aircraft departing near high volume airports but can increase delays for airborne arrivals. A Human-in-the-Loop (HITL) simulation compared arrival and departure delays to Newark Airport (EWR) with the checkbox ON vs. OFF as the default position. Three other conditions in this HITL involved various National Airspace System (NAS)-wide approaches for timely delivery of aircraft to the TBFM region. These conditions were: Baseline, using current Mile-in-Trail (MIT) spacing restrictions; Integrated Demand Management (IDM), where all aircraft were given departure times (Expect Departure Clearance Times, or EDCTs), ultimately based on the EWR Airport Arrival Rate; and IDM plus Required Time of Arrival (RTA), a flight deck tool which allowed some aircraft to meet a controlled time of arrival to the TBFM area more precisely. Results showed that the checkbox tool was powerful: with the checkbox ON, departure delays decreased and airborne delays increased, as predicted. However, assuming that the cost ratio of a minute of airborne delay to a minute of departure delay is in the range of 1.2 to 3, as commonly indicated by the literature, checkbox ON and checkbox OFF conditions showed approximately equal total delay costs, i.e., the cost of delays in the air balanced the cost of the delay on the ground. The three scheduling conditions also had approximately equal total delay costs, although a simulation artifact may have reduced the delays in the Baseline condition. In the debrief following the simulation, the TMCs concluded that the checkbox should be used flexibly depending on the current delay situation, and suggested modifications to the checkbox tool which would help them use it in this way, along with enhanced training. The relatively similar total cost of both checkbox default options in this simulation indicates that this might be a fruitful approach, and replace the necessity to have the checkbox rigidly set to either ON or OFF.

departure delay↗

Analysis of Strategic Conflict Management Approaches as Applied to Simulated UAM Operations

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Strategic Conflict Management↗

A Stochastic Scheduler for Integrated Arrival, Departure and Surface Operations in Los Angeles

In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using Non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles completing resources between different flows, such as runway allocations, runway crossing, departure fixes, and other interaction way points between arrivals and departures. Meanwhile, the scheduler also takes time-varied uncertainties into account when optimizing schedules. The scheduler is run sequentially to identify the best and robust schedule for the next planning window. Resulting schedules decide the routes, speed or delays, and runway assignments with separation constraints at mergingdiverging waypoints in the air and crossing and separations on runways. The Los Angels terminal area was used as an example. The implementation of this stochastic scheduler for integrated arrival, departure and surface operations is completed. And several preliminary runs are finished for over 1,200 flights in LAX in a typical day. Sensitivity studies on various planning window sizes are presented, which shows that trade-off exits between planning window size and achievable minimum delay. Preliminary results on runway usage are also presented in this abstract. Because arrivals on the outer runways have to be followed by crossings on the inner runways, algorithmic runway allocation prefers inner runways for arrivals and outer runways for departures. More results will be presented in the final paper. And current terminal arrival and departure procedures based on first-come-first-serve procedure will also be set up and used as a baseline for comparison.

integrated scheduler↗

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project KDP-C Review

The topics discussed are the UAS-NAS project life-cycle and ARMD thrust flow down, as well as the UAS environments and how we operate in those environments. NASA's Armstrong Flight Research Center at Edwards, CA, is leading a project designed to help integrate unmanned air vehicles into the world around us. The Unmanned Aircraft Systems Integration in the National Airspace System project, or UAS in the NAS, will contribute capabilities designed to reduce technical barriers related to safety and operational challenges associated with enabling routine UAS access to the NAS. The project falls under the Integrated Systems Research Program office managed at NASA Headquarters by the agency's Aeronautics Research Mission Directorate. NASA's four aeronautics research centers - Armstrong, Ames Research Center, Langley Research Center, and Glenn Research Center - are part of the technology development project. With the use and diversity of unmanned aircraft growing rapidly, new uses for these vehicles are constantly being considered. Unmanned aircraft promise new ways of increasing efficiency, reducing costs, enhancing safety and saving lives 460265main_ED10-0132-16_full.jpg Unmanned aircraft systems such as NASA's Global Hawks (above) and Predator B named Ikhana (below), along with numerous other unmanned aircraft systems large and small, are the prime focus of the UAS in the NAS effort to integrate them into the national airspace. Credits: NASA Photos 710580main_ED07-0243-37_full.jpg The UAS in the NAS project envisions performance-based routine access to all segments of the national airspace for all unmanned aircraft system classes, once all safety-related and technical barriers are overcome. The project will provide critical data to such key stakeholders and customers as the Federal Aviation Administration and RTCA Special Committee 203 (formerly the Radio Technical Commission for Aeronautics) by conducting integrated, relevant system-level tests to adequately address safety and operational challenges of national airspace access by unmanned aircraft systems, or UAS. In the process, the project will work with other key stakeholders to define necessary deliverables and products to help enable such access. Within the project, NASA is focusing on five sub-projects. These five focus areas include assurance of safe separation of unmanned aircraft from manned aircraft when flying in the national airspace; safety-critical command and control systems and radio frequencies to enable safe operation of UAS; human factors issues for ground control stations; airworthiness certification standards for UAS avionics and integrated tests and evaluation designed to determine the viability of emerging UAS technology. Five Focus Areas of the UAS Integration in the NAS Project Separation Assurance Provide an assessment of how planned Next Generation Air Transportation System (NextGen) separation assurance systems, with different functional allocations, perform for UAS in mixed operations with manned aircraft Assess the applicability to UAS and the performance of NASA NextGen separation assurance systems in flight tests with realistic latencies and uncertain trajectories Assess functional allocations ranging from today's ground-based, controller-provided aircraft separation to fully autonomous airborne self-separation Communications Develop data and rationale to obtain appropriate frequency spectrum allocations to enable safe and efficient operation of UAS in the NAS Develop and validate candidate secure safety-critical command and control system/subsystem test equipment for UAS that complies with UAS international/national frequency regulations, standards and recommended practices and minimum operational and aviation system performance standards for UAS Perform analysis to support recommendations for integration of safety-critical command and control systems and air traffic control communications to ensure safe and efficient operation of UAS in the NAS Human Systems Integration Develop a research test bed and database to provide data and proof of concept for GCS - ground control station - operations in the NAS Coordinate with standards organizations to develop human-factors guidelines for GCS operation in the NAS Certification Define a UAS classification scheme and approach to determining Federal Aviation Regulation airworthiness requirements applicable to all UAS digital avionics Provide hazard and risk-related data to support development of type design criteria and best development practices Integrated Tests and Evaluation Integrate and test mature concepts from technical elements to demonstrate and test viability Evaluate the performance of technology development in a relevant environment (full-mission, human-in-the-loop simulations and flight tests)

outreach↗