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NASA NTRS2000

Turbulence Climatology at Dallas/Ft.Worth (DFW) Airport: Implications for a Departure Wake Vortex Spacing System

Potential adaptive wake vortex spacing systems may need to rely on wake vortex decay rather than wake vortex transport in reducing wake separations. A wake vortex takeoff-spacing system in particular will need to rely on wake decay. Ambient turbulence is the primary influence on wake decay away from the ground. This study evaluated 18 months of ambient turbulence measurements at Dallas/Ft. Worth (DFW) Airport. The measurements show minor variation in the turbulence levels at various times of the year or times of the day for time periods when a departure system could be used. Arrival system operation was also examined, and a slightly lower overall turbulence level was found as compared to departure system benefit periods. The Sarpkaya model, a validated model of wake vortex behavior, was applied to various turbulence levels and compared to the DFW turbulence statistics. The results show that wake vortices from heavy aircraft on takeoff should dissipate within one minute for the majority of the time and will rarely last two minutes. These results will need to be verified by wake vortex measurements on departure.

Perras, G. H.↗

NASA NTRS1998

Aircraft Vortex Spacing System (AVOSS) Initial 1997 System Deployment at Dallas/Ft. Worth (DFW) Airport

The potential hazard of aircraft encounters with the wake turbulence of preceding aircraft requires the use of minimum separations on landing that are significant constraint on airport arrival capacity during instrument flight rules (IFR) conditions. The National Aeronautics and Space Administration (NASA) Langley research Center has been researching the development of the Aircraft Vortex Spacing System (AVOSS) which would dynamically change aircraft arrival separations based on the forecasted weather conditions and vortex behavior.

Dasey, T. J.↗

NASA NTRS2008

Effect of Surface Traffic Count on Taxi Time at Dallas-Fort Worth (DFW) International Airport

As the amount of air traffic increases over the years, most airports simply do not have the means of expanding to handle the intensified traffic on the surface that will ensue. Precise surveillance equipment and automation concepts, as well as advanced surface traffic algorithms are being developed to improve airport efficiency. These surface algorithms require inputs unique to each airport to ensure maximum efficiency, and minimal taxi delay. This study analyzes surface traffic at Dallas-Fort Worth International Airport (DFW) to determine the effect of the number of aircraft on the surface and the amount of stop and go situations they experience to the amount of additional taxi time encountered. If the surface capacity of an airport is known, minimal delay can be accomplished by limiting the number of taxiing aircraft to that capacity. This concept is related to highways, where traffic flow drastically decreases as more cars occupy the road. An attempt to minimize this effect on highways is seen with the use of metering lights at freeway on-ramps. Since the surface traffic at airports is highly regulated, and aircraft are less mobile on the ground, limiting the surface count to a certain number can greatly reduce the amount of additional taxi time encountered, as well as reduce hazardous emissions. This study will also find the regions of an airport that encounter the most additional taxi time when the number of aircraft in that area is increased. This could help surface traffic algorithms avoid congesting that area, or re-route aircraft to different runways when that area reaches its capacity. The relationship between the amount of stop and go situations an aircraft encounters and their effect on the taxi time of that aircraft will also be investigated. This will help to determine the effect of holding an aircraft on the taxiway as opposed to re-routing it. The lesser of the two should be used when developing surface traffic algorithms to further minimize the delay encountered. The fields investigated in this study include taxi time, the number of aircraft on the surface, the number of stop and go situations, and the time stopped for each aircraft. Taxi time is defined as spot to runway for departures, and runway to spot for arrivals. It does not include ramp area taxi time because the ramp area is controlled differently, and surface traffic schedulers do not currently incorporate them. Taxi time is found by finding the difference between take-off time (OFF) and spot crossing time for departures, and spot crossing time and landing time (ON) for arrivals. All surface data was either found directly using the Surface Operations Data Analysis and Adaptation (SODAA), a tool to analyze the Surface Management System (SMS) generated log files, or indirectly from SODAA using Matlab to derive values from SODAA data. The number of aircraft on the surface is found by looping through the ON times, OFF times, and spot times for each aircraft during a particular day. For each departure aircraft, surface counts are taken at its spot crossing and OFF time. The average of these two is used as the surface count for that aircraft. For arrivals, surface counts are taken at its ON time and its spot crossing time. The average of these two is used.

Kistler, Matthew Stephen↗

NASA NTRS2022

Near Term Urban Air Mobility Use Cases in the Dallas Fort-Worth Area

NASA is conducting research on airspace management and flight procedures concepts for Urban Air Mobility (UAM). A series of collaborative meetings between NASA and Joby, informed by FAA and DFW airport subject matter experts, spanned several years and developed five use cases for operations in the Dallas-Fort Worth Airport (DFW) area. This report describes how UAM aircraft could navigate between vertiports within a region with a high volume of conventional air traffic in the neighboring areas and provides recommendations for introducing UAM operations at DFW. The objective of this effort is to evaluate the extent to which UAM flights are possible in the current National Airspace System and identify whether existing approaches to expanded operations will work for UAM.

Urban Air Mobility↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

machine learning↗

NASA NTRS2010

Analysis of DFW Perimeter Taxiway Operations

This study examines operations of the perimeter taxiway system at Dallas/Fort Worth International Airport (DFW) to characterize and understand the impact of the perimeter taxiway system and to provide operational decision makers with guidance on use of this new airport resource. DFW s perimeter taxiway entered service in December 2008 and is representative of perimeter or end-around taxiways currently in use at several other airports worldwide. This perimeter taxiway analysis is a collaborative effort between NASA and various DFW stakeholders including the FAA, air carriers and the airport operator. The initial investigation has focused on quantifying perimeter taxiway usage and assessing effects on taxi times at both the local and global levels. Local-level results show taxi times via the perimeter taxiway to be about forty-five seconds longer on average, but with significantly less variability. Global-level results show average perimeter taxiway times to be a little more than one minute longer with variability that is comparable to that for other taxi paths.

Engelland, Shawn A.↗

NASA NTRS2004

Controllers and Pilots Play a Key Role in Runway Safety Initiatives Through Real-Time Simulation

A new and innovative way to evaluate runway safety initiatives for airports is through the use of interactive real-time simulation. The National Aeronautics and Space Administration (NASA) operates an integrated suite of simulators that can give both pilots and tower controllers the ability to simultaneously "try out" ideas in the safety of virtual reality. In February of 2003, the FAA conducted a demonstration in the NASA facilities for Dallas/Fort Worth International Airport (DFW) of a concept to reduce runway crossings and enhance the efficiency of the airport. Currently DFW experiences about 1,700 runway crossings per day, which contribute to arrival and departure delays and the potential for runway incursions. The proposed concept included the addition of new perimeter taxiways on the East and West sides of the airport. Through use of NASA's unique simulation capabilities, DFW controllers and commercial pilots provided expert feedback on the safety and operational implications by directly experiencing the proposed changes. Overall, the data collected from the participants and the simulators demonstrated that the concept would improve operations at DFW, if implemented. Improvements were observed in many areas including departure rates, taxi duration, runway crossings, and controller and pilot communications.

Madson, Mike↗

NASA NTRS2014

Throughput Benefit Assessment for Tactical Runway Configuration Management (TRCM)

The System-Oriented Runway Management (SORM) concept is a collection of needed capabilities focused on a more efficient use of runways while considering all of the factors that affect runway use. Tactical Runway Configuration Management (TRCM), one of the SORM capabilities, provides runway configuration and runway usage recommendations, monitoring the active runway configuration for suitability given existing factors, based on a 90 minute planning horizon. This study evaluates the throughput benefits using a representative sample of today's traffic volumes at three airports: Memphis International Airport (MEM), Dallas-Fort Worth International Airport (DFW), and John F. Kennedy International Airport (JFK). Based on this initial assessment, there are statistical throughput benefits for both arrivals and departures at MEM with an average of 4% for arrivals, and 6% for departures. For DFW, there is a statistical benefit for arrivals with an average of 3%. Although there is an average of 1% benefit observed for departures, it is not statistically significant. For JFK, there is a 12% benefit for arrivals, but a 2% penalty for departures. The results obtained are for current traffic volumes and should show greater benefit for increased future demand. This paper also proposes some potential TRCM algorithm improvements for future research. A continued research plan is being worked to implement these improvements and to re-assess the throughput benefit for today and future projected traffic volumes.

Phojanamongkolkij, Nipa↗

NASA NTRSDate not supplied

Objective Measurement Assessment of Departure Advisories for Ramp Controllers from a Human-In-The-Loop Simulation

NASA has developed and demonstrated an integrated arrival, departure, and surface concept and technology for efficient air traffic management in busy terminal environments under the Airspace Technology Demonstration 2 (ATD-2) subproject. In April/May 2019, NASA conducted a human-in-the-loop (HITL) simulation with ramp and tower controllers having experience at Dallas/Fort Worth International Airport (DFW). The purpose of this HITL was to evaluate the impacts of various surface metering goals in ramp operations at DFW and test new features of the ATD-2 ramp controller decision support tools. This paper evaluates the quantitative metrics from the simulation results related to airport performance and surface metering given various departure scheduling advisories for ramp controllers. The objective measurements compared include the controller’s compliance with the target times for pushback and spot arrival, the number of metered flights, aircraft gate hold and taxi time, runway throughput, and the number of aircraft on the surface. The simulation results show that there were no statistically significant benefits from surface metering at DFW for airport performance due to several simulation artifacts notably different from real operations. Considering controller workload and situation awareness, following either gate pushback or spot arrival advisory could be a better metering option than utilizing both advisories when surface metering is applied at DFW.

airport ramp traffic control decision support↗

NASA NTRS2004

Positioning System Accuracy Assessment for the Runway Incursion Prevention System Flight Test at the Dallas/Ft. Worth International Airport

NASA/Langley Research Center collaborated with the Federal Aviation Administration (FAA) to test a Runway Incursion Prevention System (RIPS) at the Dallas Fort Worth International Airport (DFW) in October 2000. The RIPS combines airborne and ground sensor data with various cockpit displays to improve pilots' awareness of traffic conditions on the airport surface. The systems tested at DFW involved surface radar and data systems that gather and send surface traffic information to a research aircraft outfitted with the RIPS software, cockpit displays, and data link transceivers. The data sent to the airborne systems contained identification and GPS location of traffic. This information was compared with the own-ship location from airborne GPS receivers to generate incursion alerts. A total of 93 test tracks were flown while operating RIPS. This report compares the accuracy of the airborne GPS systems that gave the own-ship position of the research aircraft for the 93 test tracks.

Quach, Cuong C.↗

NASA NTRS2016

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5% per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM(Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas/Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte/Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASA's Airspace Technology Demonstration - 2 (ATD-2) project for technology demonstration of Integrated Arrival/Departure/Surface (ADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement)/KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASA's fast-time simulation tool.

scheduling↗

NASA NTRS2016

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5 percent per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM (Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas-Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte-Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASA's Airspace Technology Demonstration-2 (ATD-2) project for technology demonstration of Integrated Arrival-Departure-Surface (IADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement), KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASA's fast-time simulation tool.

surface operations analysis↗

NASA NTRS1990

DFW microburst model based on AA-539 data

Analysis of the August 2, 1985 crash for an L-1011 jumbo jet (DL-191) on approach to the Dallas-Ft. Worth International Airport (DFW) in a thunderstorm indicates that the severe windshear microburst that caused the crash was composed not only of a strong downflow and outflow but also included several large-scale vortex rings entrained in the flowfield. This paper presents a detailed two-dimensional model of the DFW microburst based on data from the MD-80 (AA-539) that followed behind DL-191 and flew through the microburst about two minutes after the crash of DL-191. The model was developed using wind-vector and flight-path data reconstructed by NASA Ames Research Center and a combination of interactive graphics and least-squares error best fit between the modeled and measured wind vectors along the AA-539 flight path. The model indicates that the flowfield contains some significant elements and vortices not previously reported. The alternating direction of rotation of the vortices in the model suggests a microburst structure based on a von Karman vortex street rather than on a Kelvin-Helmholtz instability. The model also indicates that the reconstructed wind-vector data contain a time lag of at least one second in the horizontal winds.

Grantham, Walter J.↗

NASA NTRS1990

A DFW microburst model based on DL-191 data

Analysis of the August 2, 1985 crash of an L-1011 jumbo jet (DL-191), on approach to the Dallas-Ft. Worth International Airport (DFW) in a thunderstorm indicates that the severe windshear microburst which caused the crash was composed not only of a strong downflow and outflow but also included several large-scale vortex rings entrained in the flowfield. A detailed two-dimensional model of the DFW microburst, based on data from DL-191, is presented. The model uses wind vector and flight path data reconstructed by NASA-Ames Research Center from the L-1011's digital flight data records and ground-based radar measurements. The model was developed using a combination of interactive graphics and a least-square-error best fit between the modeled and measured wind vectors along the DL-191 flight path. The model confirms a microburst structure based on a von Karman vortex street rather than on a Kelvin-Helmholtz instability and indicates that the L-1011 flew near or directly through several strong vortices. In addition, the results also confirm that the reconstructed wind vector data contain a time lag in the horizontal winds. For the DL-191 this lag is approximately three seconds.

Grantham, Walter J.↗

NASA NTRSDate not supplied

Human Factors Impact of Different Ramp Controller Scheduling Advisories for ATD-2 Surface Metering in a Human-in-the-Loop Simulation

A human-in-the-loop (HITL) simulation took place in 2019 at NASA Ames Research Center’s Future Flight Central, an airport simulator with a full-scale 360-degree view that showed real-time airport operations at Dallas/Fort Worth International Airport (DFW). The HITL was part of the NASA/FAA Airspace Technology Demonstration 2 (ATD-2) project which involves an on-going field demonstration at Charlotte Douglas International Airport (CLT). In the field demonstration, metering on the airport surface takes place with one of the goals being to reduce runway queue length by holding aircraft at the gate during busy times, thereby reducing fuel burn and CO2 emissions. This goal has been successfully accomplished at CLT by showing Ramp Controllers when to release aircraft at the gate so as to reduce aircraft waiting in the queue. Among the questions that the current simulation at DFW was designed to address were, "Which types of advisories to the Ramp Controllers work best, those that show a ±2 minute window for a release time from the gate, or a ±5 minute window for an arrival time at the spot (where Air Traffic Control takes over) or both?" and "How do these different types of advisories affect Ramp Controller workload and situation awareness?" Another question was, “Does a ramp that is less restrictive than the one at CLT affect compliance with these advisories?”

Bonny K Parke↗

NASA NTRS1998

A Fast-Time Study of Aircraft Reordering in Arrival Sequencing and Scheduling

In order to ensure that the safe capacity of the terminal area is not exceeded, Air Traffic Management ATM often places restrictions on arriving flights transitioning from en route airspace to terminal airspace. This restriction of arrival traffic is commonly referred to as arrival flow management, and includes techniques such as metering, vectoring, fix-load balancing, and the imposition of miles-in-trail separations. These restrictions are enacted without regard for the relative priority which airlines may be placing on individual flights based on factors such as crew criticality, passenger connectivity, critical turn times, gate availability, on-time performance, fuel status, or runway preference. The development of new arrival flow management techniques which take into consideration priorities expressed by air carriers will likely reduce the economic impact of ATM restrictions on the airlines and lead to increased airline economic efficiency by allowing airlines to have greater control over their individual arrival banks of aircraft. NASA and the Federal Aviation Administration (FAA) have designed and developed a suite of software decision support tools (DSTs) collectively known as the Center TRACON Automation System (CTAS). One of these tools, the Traffic Management Advisor (TMA) is currently being used at the Fort Worth Air Route Traffic Control Center to perform arrival flow management of traffic into the Dallas/Fort Worth airport (DFW). The TMA is a time-based strategic planning tool that assists Traffic Management Coordinators (TMCs) and En Route Air Traffic Controllers in efficiently balancing arrival demand with airport capacity. The primary algorithm in the TMA is a real-time scheduler which generates efficient landing sequences and landing times for arrivals within about 200 no a. from touchdown. This scheduler will sequence aircraft so that they arrive in a first- come - first-served (FCFS) order. While FCFS sequencing establishes a fair order based on estimated times of arrival, it does not take into account individual airline priorities among incoming flights. NASA is exploring the possibility of allowing airlines to express relative arrival priorities to air traffic management through the development of new CTAS scheduling algorithms which take into consideration airline arrival preferences. The accommodation of airline priorities in arrival sequencing and scheduling would under most circumstances result in a deviation from a "natural" or FCFS arrival order. As a First step toward developing airline influenced sequencing algorithms, an investigation was conducted to determine the feasibility of reordering arrival traffic from a strict FCFS sequence. A fast-time simulation has been developed which allows statistical evaluation of sequencing and scheduling algorithms for arrival traffic at the Dallas/Fort Worth Airport. In contrast to real-time simulation or field tests, which would require on the order of ninety minutes to examine a single traffic rush period, the fast-time simulation allows examination of multiple rush periods in a matter of seconds.

Carr, Greg↗

NASA NTRS2004

The NASA Aircraft VOrtex Spacing System (AVOSS): Concept Demonstration Results and Future Direction

Since the late 1990s the national airspace system has been recognized as approaching a capacity crisis. In the light of this condition, industry, government, user organizations, and educational institutions have been working on procedural and technological solutions to the problem. One aspect of system operations that holds potential for improvement is the separation criteria applied to aircraft for wake vortex avoidance. These criteria, applied when operations are conducted under instrument flight rules (IFR), were designed to represent safe spacing under weather conditions conducive to the longest wake hazards. It is well understood that wake behavior is dependent on meteorological conditions as well as the physical parameters of the generating aircraft. Under many ambient conditions, such as moderate crosswinds or turbulence, wake hazard durations are substantially reduced. To realize this reduction NASA has developed a proof-of-concept Aircraft VOrtex Spacing System (AVOSS). Successfully demonstrated in a realtime field demonstration during July 2000 at the Dallas Ft. Worth International Airport (DFW), AVOSS is a novel integration of weather sensors, wake sensors, and analytical wake prediction algorithms. AVOSS provides dynamic wake separation criteria that are a function of the ambient weather conditions for a particular airport, and the predicted wake behavior under those conditions. Wake sensing subsystems provide safety checks and validation for the predictions. The AVOSS was demonstrated in shadow mode; no actual spacing changes were applied to aircraft. This paper briefly reviews the system architecture and operation, reports the latest performance results from the DFW deployment, and describes the future direction of the project.

Rutishauser, David K.↗