The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is "generic" in that it makes no assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Accordingly, GRACE is well suited to research applications where alerting and guidance is a central feature and requirements are fluid involving a wide range of aviation technologies. GRACE has been used at NASA in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.
This paper describes simulations of an automated planning system that routes flights around airspace impacted by forecasted convective weather. If the system predicts that a flight will enter a weather-impacted airspace within a predefined time horizon, it generates a new route. Because the forecasts are uncertain, the system periodically generates, using updates of the weather forecasts and radar tracks, new reroutes. The simulations included convective weather in the northeastern quadrant of the United States over a 24-hr period. Multiple simulations investigated the system performance as the planning horizon and planning frequency varied. As the planning horizon and frequency increased, the system successfully routed more traffic around weather but with more route changes. For a planning horizon of 20 to 120 minutes and a planning frequency of four cycles per hour, the reroutes increased flight time by 3.3% and avoided 79% of the weather-impacted airspaces that were detected. Most flights required one to three reroutes to pass by a weather-impacted airspace, while the worst case flights required six reroutes.
A study analyzing the economic and safety impacts of different flight routing methods in the National Airspace System is presented. It compares filed flight routes, wind-optimal routes, and great-circle routes. Routing differences are measured by flight time, fuel burn, sector count, and number of conflicts. Wind-optimal routes exhibit on average approximately one percent less flight time and fuel burn than filed flight routes. In addition, they produce an average of 13 less conflicts in Class A airspace (18,000 feet and above). All three routing methods are qualitatively equivalent in terms of sector count distribution. These results agree with earlier studies, which investigated some combinations of these types of routes and metrics. The contribution of this paper is that it consistently compares the three routing methods across the United States using the four metrics.
This paper documents a crucial piece of the ongoing effort to develop minimum operational performance standards for Unmanned Aircraft System (UAS) Detect-and-Avoid (DAA)-the estimation of the rate of encounters between UAS and manned aircraft operating under Visual Flight Rules (VFR). In fast-time simulations that included both simulated future UAS and actual present-day manned VFR aircraft, UAS encountered VFR aircraft once every 5.3 UAS flight hours. Modeled air traffic controller mitigations for conflicts between UAS and manned aircraft operating under Instrument Flight Rules only reduced the rate of encounters between UAS and VFR aircraft by 0.2% with no statistically significant or practical effect on encounter geometry characteristics. Analysis of the simulations without modeled air traffic controller mitigations showed that the highest rates of encounter and loss of well clear occurred at altitudes below 5000 feet. In addition, a surveillance range of 14.3 nautical miles was needed to detect all encounters between UAS and VFR aircraft. A surveillance range of 3.6 nautical miles was necessary to detect all losses of well clear. These results primarily inform the safety case and the surveillance requirements for UAS DAA systems. Nomenclature DMOD = distance modification * h * mod t = horizontal modified tau threshold mod_r t = slant range modified tau * Small UAS are not covered under the FAA's UAS Integration Concept of Operations [9] and are not within the scope of the RTCA SC-228 MOPS [1]. Furthermore, small UAS do not require IFR operations. As such, this paper does not compute the encounter rate or the LoWC rate for small UAS.
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