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Wes Ryan

Publications and source records attributed to Wes Ryan.

18 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Digital Flight: A New Cooperative Operating Mode to Complement VFR and IFR

Digital Flight is a proposed new operating mode for all airspace users, complementing and adding to the existing operating modes of visual and instrument flight rules (VFR and IFR) and providing for cooperative integration in controlled airspace. Under new regulations (Digital Flight Rules, DFR) that set requirements for its sustained use, qualified operators employ Digital Flight to enhance their airspace access and operational flexibility in all visibility conditions and eventually all airspace classes without requiring segregation from incumbent operations. Enabled by connected digital information and technologies, Digital Flight operators employ cooperative practices and self-separation to ensure flight path safety in lieu of visual procedures or receiving separation services from Air Traffic Control. Its distributed structure and automated functions enable traffic densities and operational tempos not achievable with the existing operating modes. Various ongoing and emerging industry initiatives for enabling new entrants (e.g., unmanned aircraft systems, urban air taxis) and enhanced use of underserved airspace (e.g., ultra-high altitude), are pursuing various alternative operating modes with significant similarities, creating a unique and time-limited opportunity for harmonization and convergence. Digital Flight is proposed to serve that harmonizing role, creating a common new paradigm for airspace operations. Such convergence would not only bring together these emerging market segments but will also bolster the existing operators with access to this new operating mode and its advantages. Digital Flight provides an opportunity to focus regulatory development and benefit the aviation industry. This paper describes Digital Flight in sufficient detail to initiate community engagement and deliberation on a common new operating mode. It describes the essential elements, principal capabilities, and operational integration of DFR in shared airspace. It describes the value proposition from multiple perspectives and presents initial thoughts on the path forward.

Digital flight↗
NASA NTRSDate not supplied

FAA – NASA PNT* Workshop

Addressing the Need for -> Resilient PNT Services in Challenging and Under-Served Airspace to -> Enable New Capabilities. *PNT=Positioning Navigation and Timing

GPS↗
NASA NTRSDate not supplied

Runway Configuration Management with Offline Reinforcement Learning

Runway configuration management (RCM) is a challenging task, and it affects the efficiency of the National Airspace System (NAS) and airport surface operations significantly. Each airport, depending on the geometry, capacity, local climate patterns, etc. has multiple configurations for the runway usage for arriving and departing flights. Many factors such as the incoming/outgoing traffic load, wind direction and speed, convective weather, cloud ceiling and other environmental factors might affect the choice of a runway configuration at any point in time. However, other factors such as safety measures and regulations, noise abatement, capacity of each configuration, and preference of the air traffic controllers (ATCs) can also play a significant role in selecting the configuration. A sub-optimal selection of the runway configuration, or delay in making configuration changes might result in significant increase in taxi times for aircraft on the surface of the airport, fuel and energy use of the aircraft, and maintenance costs. It can also lead to safety concerns, such as an aircraft performing one or more go-arounds before being able to land. All these factors make RCM an extremely important and challenging decision-making process for the ATCs. The current state of practice sets the runway configuration by the ATCs based on relevant information available at the time including weather, traffic, noise abatement, safety bounds, etc. This makes the decision-making process subjective based on the accuracy of the available information and the bias in human decision making. Unfortunately, this approach yields poor results (e.g., significant delays) if the predicted outcomes are uncertain and their relative impact is not well understood. This is especially evident when the uncertainty increases the size of possible predicted outcomes (combinatorial explosion in possible scenarios) that cannot be handled by human reasoning. On the other hand, an automated approach based on machine intelligence can make use of historical data and search through all (or significant amount of) possible scenarios under uncertainty and make well-informed decisions.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Mission Profiles for the SUSAN Electrofan Concept

The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a novel aircraft concept which utilizes a single aft-mounted engine, electrified aircraft propulsion, an emergency backup battery, as well as state-of-the-art aerodynamic design and thermal management systems to reduce the overall environmental impact of the aircraft. Mission profiles, which define the aircraft state and flight characteristics throughout various phases of flight, are an important component of aircraft conceptual design. These defined characteristics can serve as sizing cases or design constraints. By defining the mission profiles to be within airworthiness certification standards and regulations, aircraft designers can ensure the conceptual design is predicted to be within compliance of these regulations throughout the design process. This paper will present mission profiles for the SUSAN Electrofan aircraft, including the basis for defining the characteristics required during each phase of flight.

Conceptual Design↗
NASA NTRSDate not supplied

Mission Profiles for the SUSAN Electrofan Concept

The Subsonic Single Aft engine (SUSAN) Electrofan is a novel aircraft concept which utilizes a single aft-mounted engine, electrified aircraft propulsion, an emergency backup battery, as well as state-of-the-art aerodynamic design and thermal management systems to reduce the overall environmental impact of the aircraft. Mission profiles, which define the aircraft state and flight characteristics throughout various phases of flight, are an important component of aircraft conceptual design. These defined characteristics can serve as sizing cases or design constraints. By defining the mission profiles to be within airworthiness certification standards and regulations, aircraft designers can ensure the conceptual design is predicted to be within compliance of these regulations throughout the design process. This paper will present mission profiles for the SUSAN Electrofan aircraft, including the basis for defining the characteristics required during each phase of flight.

Conceptual Design↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗