Daekeun Jeon

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILPbased optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

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 NASAs 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), NASAs fast-time simulation tool.

Comparison of First-Come First-Served and Optimization Based Scheduling Algorithms for Integrated Departure and Arrival Management [STUB]

Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA) are investigating scheduling algorithms that will be a part of an integrated arrival and departure management system. Inha University, one of the Korean collaborators of KARI, developed an Extended First-Come First-Served (EFCFS) algorithm that is robust and efficient. However, since the EFCFS algorithm sequentially computes the schedule based on priority, the end results may not be optimal for system efficiency. The approach based on Mixed Integer Linear Programming (MILP) originally developed by NASA and modified by KARI is known to produce better schedules at the expense of computational cost. In this paper, the two different scheduling approaches are compared using common traffic scenarios and constraints at Incheon International Airport. Capabilities to apply weight class based wake turbulence runway separation minima and Miles-in-Trail (MIT) restrictions at selected meter fixes are added to the previously developed EFCFS scheduler. Based on historic data, 40 departures and 20 arrivals are chosen in a one-hour period and 100 scenarios were created by randomly assigning gate numbers, gate departure times, and runway landing times. With the current runway separation requirements, MILP resulted in about ten to twenty percent smaller average delays depending on the constraints. With artificially increased separation minima, the difference between MILP and EFCFS became more noticeable. However, the EFCFS was about ten times faster with smaller variations among different scenarios and constraints. The comparison suggests that the MILP-based algorithm has a small advantage at the current traffic level; however, has potential to be more effective in higher demand or severe weather situations. The EFCFS algorithm may be better suited for real-time applications or investigating larger scale scheduling problems.

Research/Development Trend and Technical Enablers of Trajectory-based Operations in Air Traffic Management

The research and development trend of Trajectory-based Operations(TBO), which is considered as a key concept of future Air Traffic Management(ATM), is presented in this paper. The operational concepts of TBO in ASBU(Aviation System Block Upgrade) from ICAO(International Civil Aviation Organization) have been summarized, and the detailed operational concepts and procedures, which can be realized in the near future, are described through the investigations of operational concept development and related research/development activities of TBO in USA and Europe. The technical enablers, which have been identified based on understanding of TBO operational concepts, are introduced, and related research/development status of each technical enabler has been presented.

MULTI-SENSOR DATA PROCESSING FOR AIR TRAFFIC CONTROL SYSTEM

Multi-sensor data processing (MSDP) software is developed as a core module of the surveillance data processing system (SDPS) for air traffic control. MSDP processes not only radar input but also data from ADS-B and multilateration (MLAT), allowing it to produce system tracks for other applications in the air traffic control system. The multiple plot variable update (MPVU) and the interacting multiple model (IMM) are adopted in the MSDP in order to process data from multiple sensors and to support multiple flight modes. Tracking performance test results based on EUROCONTROL surveillance standards indicate that the MSDP meets EUROCONTROL requirements.

Performance Improvement of Maneuvering Target Tracking with Radar Measurement Noise Estimation

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

Development of FAA AC120-63 Level C Flight Simulation Model for KA-32T

Flight simulation model for helicopter simulator is one of the most important models which affect flight performance and handling quality. It is typical to develop the model based on the raw data and models from the helicopter designers/manufacturers. The approaches in this study were to develop the basic model based on the available resources regarding helicopter operation/maintenance and to tune and validate it based on the flight test results. The basic model was developed with maintenance manuals, flight manuals, analyses, measurements, papers and so on considering that KA-32T data could not be obtained from the manufacturer. The flight test for KA-32T was performed and the reference data for the simulation validation tests were acquired. The flight simulation model was validated to have the fidelity compatible with level C of FAA AC120-63 after comparison and tuning with flight test results.

A Study on the Model of Thermal Plume Flow in the Forest Fire

A study is made of thermal plume flow model for the development of helicopter simulator over the forest fire. For the numerical analysis, a line fire model with Boussinesq fluid approximation, which is idealized by the spreading shape of forest fire on the ground, is adopted. Comparing full 2-D and 3-D numerical solutions with 2-D similarity solution, it has been built a new model that is useful for temperature prediction along the symmetric vertical axis of fire model for both cases of laminar and turbulent flow.