Hanbong Lee — Page 2

NASA NTRSDate not supplied

Simulated Evaluation of Strategic Conflict Management Capabilities for Urban Air Mobility Operations

Urban Air Mobility (UAM) is a new air transportation service concept to carry passengers or cargo in metropolitan areas, leveraged by innovative aircraft and automation technologies. NASA has conducted a series of simulations to evaluate the UAM concept of operations and inform the development of airspace procedures and services for UAM operations. The latest set of simulations called “X5” were conducted to test a Provider of Services for UAM (PSU) prototype that NASA developed for UAM flight planning, strategic conflict management support, and data exchange between UAM operators. In these simulations, two strategic conflict management capabilities, Demand-Capacity Balancing (DCB) and Sequencing and Scheduling (S&S), were further investigated. This paper describes the system architecture designed for the X5 simulation activities, the sequence diagram for strategic conflict management, and the simulation environment in the Dallas/Fort Worth urban area. The simulation results based on several system performance metrics for evaluation show that a sequential application of DCB and S&S effectively works to distribute traffic demand and meet sequencing and spacing criteria by assigning ground delays, compared to the DCB only and S&S only cases.

Urban Air Mobility, Strategic Conflict Management,↗

NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

NASA NTRSDate not supplied

Demand-Capacity Balancing Algorithms for Urban Air Mobility Operations

This paper proposes new Demand/Capacity Balancing (DCB) algorithms that resolve imbalances at enroute waypoints, such as crossing, merging, and UAM corridor entry or exit waypoints, in addition to vertiports; we refer to this algorithm as DCB-Waypoint, still using pre-departure delay as the sole resolution mechanism. Like DCB-Vertiport, DCB-Waypoint takes one flight at a time and resolves imbalances one waypoint at a time, starting from the origin vertiport followed by the sequence of constrained waypoints and, finally, the destination vertiport. The next advancement assigns airborne delays, in addition to pre-departure delays at vertiports. This algorithm, called DCB-Airborne-Delays, uses information on flight speeds to ensure that the assigned delays are feasible, meaning that variations in flight speeds are within feasible aircraft speed ranges. The proposed algorithms are being implemented in a new fast-time simulation tool developed especially for simulating UAM operations. The full paper will provide additional details of the new DCB algorithms that have been developed, as well as the results from simulations using these algorithms. Finally, the DCB algorithms will be compared against one another to obtain insights and recommendations for the future development of more advanced DCB algorithms.

urban air mobility, demand-capacity balancing↗

NASA NTRSDate not supplied

Simulated Evaluation of Strategic Conflict Management Capabilities for Urban Air Mobility Operations

Urban Air Mobility (UAM) is a new air transportation service concept to carry passengers or cargo in metropolitan areas, leveraged by innovative aircraft and automation technologies. NASA has conducted a series of simulations to evaluate the UAM concept of operations and inform the development of airspace procedures and services for UAM operations. The latest set of simulations called “X5” were conducted to test a Provider of Services for UAM (PSU) prototype that NASA developed for UAM flight planning, strategic conflict management support, and data exchange between UAM operators. In these simulations, two strategic conflict management capabilities, Demand-Capacity Balancing (DCB) and Sequencing and Scheduling (S&S), were further investigated. This paper describes the system architecture designed for the X5 simulation activities, the sequence diagram for strategic conflict management, and the simulation environment in the Dallas/Fort Worth urban area. The simulation results based on several system performance metrics for evaluation show that a sequential application of DCB and S&S effectively works to distribute traffic demand and meet sequencing and spacing criteria by assigning ground delays, compared to the DCB only and S&S only cases.

Simulation↗

NASA NTRSDate not supplied

Demand-Capacity Balancing Algorithms for Urban Air Mobility Operations

This paper proposes new Demand/Capacity Balancing (DCB) algorithms that resolve imbalances at enroute waypoints, such as crossing, merging, and UAM corridor entry or exit waypoints, in addition to vertiports; we refer to this algorithm as DCB-Waypoint, still using pre-departure delay as the sole resolution mechanism. Like DCB-Vertiport, DCB-Waypoint takes one flight at a time and resolves imbalances one waypoint at a time, starting from the origin vertiport followed by the sequence of constrained waypoints and, finally, the destination vertiport. The next advancement assigns airborne delays, in addition to pre-departure delays at vertiports. This algorithm, called DCB-Airborne-Delays, uses information on flight speeds to ensure that the assigned delays are feasible, meaning that variations in flight speeds are within feasible aircraft speed ranges. The proposed algorithms are being implemented in a new fast-time simulation tool developed especially for simulating UAM operations. The full paper will provide additional details of the new DCB algorithms that have been developed, as well as the results from simulations using these algorithms. Finally, the DCB algorithms will be compared against one another to obtain insights and recommendations for the future development of more advanced DCB algorithms.

urban air mobility↗

NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗