Information Display Protocol

Objective: Human Factors Engineers and air traffic control Subject Matter Experts (SMEs) from the Federal Aviation Administration (FAA) developed a protocol to support in decisions on how to present needed information on the en route controller\u2019s visual displays. The protocol provides guidance for determining the criticality of the information and uses this criticality to determine where and how the information should be displayed. Background: Human Factors Engineers and air traffic control SMEs developed and validated the protocol with current and upcoming FAA Next Generation Air Transportation System informational items and scenarios. Application: The model provides a systematic method for integrating the informational needs of controllers and supports the decision process for designing air traffic control displays

Energy absorption in lattice structures in dynamics: Experiments

Lattice structures offer the potential to relatively easily engineer specific (meso-scale properties (cell level)), to produce desirable macro-scale material properties for a wide variety of engineering applications including wave filters, blast and impact protection systems, thermal insulation, structural aircraft and vehicle components, and body implants. The work presented here focuses on characterising the quasi-static and, in particular, the dynamic load-deformation behaviour of lattice samples. First, cubic, diamond and re-entrant cube lattice structures were tested under quasi-static conditions to investigate failure process and stress–strain response of such materials. Following the quasi-static tests, Hopkinson pressure bar (HPB) tests were carried out to evaluate the impact response of these materials under high deformation rates. The HPB tests show that the lattice structures are able to spread impact loading in time and to reduce the peak impact stress. A significant rate dependency of load-deformation characteristics was identified. This is believed to be the first published results of experimental load-deformation studies of additively manufactured lattice structures. The cubic and diamond lattices are, by a small margin, the most effective of those lattices investigated to achieve this

Development of a Framework to Compare Low-Altitude Unmanned Air Traffic Management Systems

Presented at the AIAA SciTech 2021 ForumSeveral reports forecast a very high demand for Urban Air Mobility services such as package delivery and air taxi. This would lead to very dense low-altitude operations which cannot be safely accommodated by the current air traffic management system. Many different architectures for low-altitude air traffic management have been proposed in the literature, however, the lack of a common framework makes it difficult to compare strategies. The work presented here establishes efficiency, safety and capacity metrics, defines the components of an automated traffic management system architecture and introduces a preliminary framework to compare different alternatives. This common framework allows for the evaluation and comparison of different alternatives for unmanned traffic management. The framework is showcased on different strategies with different architectures. The impact of algorithmic choices and airspace architectures is evaluated. A decoupled approach to 4D trajectory planning is shown to scale poorly with agents density. The impact of segregating traffic by heading is shown to be very different depending on the algorithms and airspace access rules chosen

A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

Presented at AIAA Aviation 2020 ForumAs aviation traffic continues to grow, most airlines are concerned about flight delays, which increase operating costs for the airlines. Since most delays are caused by weather, pilots and flight dispatchers typically gather all available weather information prior to departure to create an efficient and safe flight plan. However, they may have to perform in-flight re-planning because weather information can significantly change after the original flight plan is created. One potential issue is that weather forecasts being currently used in the aviation industry may provide relatively unreliable information and are not accessible fast enough so that it challenges pilots to perform in-flight re-planning more accurately and frequently. In this paper, we propose a data-driven approach that uses an unsupervised machine learning technique to provide a more reliable and up-to-date area of convective weather. To evaluate the proposed methodology, we collect the American Airlines flight (AA1300) information and actual weather-related data on October 6th, 2019. Preliminary results show that the proposed methodology provides a better picture of the nearby convective weather activity compared to the most well-known convective weather product