Jerry D. Welch

A Safety Analysis Process for the Traffic Alert and Collision Avoidance System (TCAS) and See-and-Avoid Systems on Remotely Piloted Vehicles

The integration of Remotely Piloted Vehicles (RPVs) into civil airspace will require new methods of ensuring traffic avoidance. This paper discusses issues affecting requirements for RPV traffic avoidance systems and describes the safety evaluation process that the international community has deemed necessary to certify such systems. Alternative methods for RPVs to perform traffic avoidance are discussed, including the potential use of new seeand-avoid sensors or the Traffic Alert and Collision Avoidance System (TCAS). Concerns that must be addressed to allow the use of TCAS on RPVs are presented. The paper then details the safety evaluation process that is being implemented to evaluate the safety of TCAS on Global Hawk. The same evaluation process can be extended to other RPVs and traffic avoidance systems for which thorough safety analyses will also be required.

Remotely piloted vehicles in civil airspace: requirements and analysis methods for the traffic alert and collision avoidance system (TCAS) and see-and-avoid systems

The integration of remotely piloted vehicles (RPVs) into civil airspace requires new methods of ensuring aircraft separation. This work discusses issues affecting requirements for RPV traffic avoidance systems and for performing the safety evaluations that are necessary to certify such systems. The paper outlines current ways in which traffic avoidance is assured depending on the type of airspace and type of traffic that is encountered. Alternative methods for RPVs to perform traffic avoidance are discussed, including the potential use of new see-and-avoid sensors or the traffic alert and collision avoidance system (TCAS). Finally, the paper outlines an established safety evaluation process that can be adapted to assure regulatory authorities that RPVs meet level of safety requirements.

Improving air traffic management during thunderstorms

This paper discusses inter-related studies and development activities that address the significant challenges of implementing air traffic management initiatives in airspace impacted by thunderstorms. We briefly describe current thrusts that will improve the quality and precision of thunderstorm forecasts, work in progress to convert these forecasts into estimates of future airspace capacity, and an initiative to develop a robust ATM optimization model based on future capacity estimates with associated uncertainty bounds. We conclude with a discussion of the thunderstorm ATM problem in the context of future advanced airspace management concepts.

Beam Lead Tunnel Diode Amplifiers on Microstrip

In terms of the number of components, the tunnel diode amplifier is one of the least complex RF amplifiers available. It has never achieved broad acceptance because of high cost, poor reliability, and problems of instability. The recent success of planar fabrication techniques promises to eliminate these drawbacks. By integrating planar tunnel diodes with printed microwave circuitry, reliable low-cost, high-performance tunnel diode amplifiers should be feasible.

ASSESSING DELAY BENEFITS OF THE FINAL APPROACH SPACING TOOL (FAST)

Air traffic delay grows each year. NASA is developing the Final Approach Spacing Tool (FAST) to help reduce airport arrival delays. FAST is intended to increase throughput and reduce delays. Analysis and field trials have suggested that FAST can help controllers increase arrival throughput on busy runways by several aircraft per hour. Published simulation studies have predicted that delay reductions from such throughput increases would save several hundred million dollars annually. However, these predictions disagree on delay savings for some airports and omit other airports of interest. Their predicted delay savings for some airports are higher than actual reported delays for those airports. They do not consider hazardous weather disruptions to arrival routes, and they do not address downstream delays caused by schedule disruption. This paper focuses on simple statistical and analytical measures of delay to resolve these problems. It develops a rule for ranking benefits and compares delay reduction predictions against actual reported delays. It relates delay to ceiling and visibility and thunderstorms. It examines the correlation of delay between airports and estimates the impact of downstream delay on FAST benefits.

Applications of a Macroscopic Model for En Route Sector Capacity

Airspace capacity estimates are important both for airspace design and for operational air traffic management. Considerable effort has gone into understanding the complexity factors that reduce sector capacity by increasing controller workload. Yet no analytical means is available for accurately estimating the maximum capacity of an en route sector. The Monitor Alert Parameter (MAP) values that determine the operational traffic limit of en route sectors in the United States account only for workload from inter-sector coordination tasks. We propose a more complete sector capacity model that also accounts for workload from conflict avoidance and recurring tasks. We use mean closing speeds and airspace separation standards to estimate aircraft conflict rates. We estimate the mean controller service times for all three task types by fitting the model against observed peak traffic counts for hundreds of en route airspace volumes in the Northeastern United States. This macroscopic approach provides numerical capacity predictions that closely bound peak observed traffic densities for those airspace volumes. This paper reviews recent efforts to improve the accuracy of the bound by replacing certain global parameters with measured data from individual sectors. It also compares the model capacity with MAP values for sectors in the New York Center. It concludes by illustrating the use of the model to predict the capacity benefits of proposed technological and operational improvements to the air traffic management system.

Macroscopic Capacity Model with Individual Sector Closing Speed Estimates

Reliable airspace capacity estimates are important both for operational air traffic management and for airspace design. Air traffic management relies on manual procedures. Hence, controller workload determines the capacity of most sectors. Yet the current operational model for estimating capacity in United States airspace does not account for workload from conflict avoidance tasks. Aircraft closing speeds and airspace separation standards determine the rate of aircraft conflicts. Numerically, conflict workload intensity is the product of the conflict rate and the mean controller time required to service a conflict. As workload intensity approaches unity, the sector reaches capacity. We determine unknown model parameters by fitting capacity calculations against peak traffic observations for en route sectors. The result is an analytical model for capacity that is more accurate than the current operational model. The mean closing speed of all pairs of aircraft in a volume of airspace determines the conflict rate. This paper reports an effort to refine the conflict component of the model by replacing its original global closing speed estimate with local traffic-based closing speed estimates for individual sectors. Exact calculation of mean closing speed requires full position and velocity information for all flight tracks. The database that we used to obtain the peak traffic counts includes initial heading, speed, and altitude fields for all traffic entering a sector. Without positional coordinates and intersector track information, these data fields provide only crude closing speed estimates. We examined these estimates as possible indicators of sector route and altitude complexity in the New York Center. Individual sector closing speed estimates based on these observations did not improve the model fit for the 30 New York sectors.