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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace

Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.

Upper Class E Traffic Management↗
NASA NTRS2022

Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace

Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.

Upper Class E Traffic Management, ETM, Cooperative↗
NASA NTRS2023

A Blockchain Case Study for Urban Air Mobility Operational Intent

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case. In this case study, two vehicle operators are operating in the same airspace. Their intent is to fly vehicles that land at a shared vertiport, securely.

Urban Air Mobility↗
NASA NTRSDate not supplied

Confidence-Based Buffer for Strategic Deconfliction with Probabilistic Operational Intent

This paper presents a methodology to expand the 95% confidence level of the elliptical geometry given by Unmanned Aircraft System (UAS) operators planning to fly Beyond Visual Line of Sight (BVLOS) to any confidence level before being fed to the strategic deconfliction (SD) module, effectively increasing the separation buffer between Operational Intents (OIs). To assess the performance of this approach, it is integrated within an adaptation of the Rolling Horizon with K-Position Search volume-based strategic deconfliction approach, previously developed at NASA Ames, preventing the 4D overlapping of OIs shaped by ellipses instead of traditional blocks. Safety and efficiency metrics are evaluated through the deconfliction of four simulated package delivery route network structures across the San Francisco Metropolitan Area with increasing numbers of crossing waypoints (network complexity). Safety assessment entails the in-house creation of a metric to quantify collision occurrences per flight hour based on the frequency at which the probabilistic operational volume segments are sampled, whereas efficiency is measured using ground delay. Results indicate that the largest buffer growth occurs when increasing the confidence level beyond 99.9% and demonstrate the negative impact of network complexity on both metrics, regardless of the OI geometry. Further, the ellipse-based SD adaptation more accurately estimates temporal separation at crossings, allowing deconflicted vehicles to be closer together. It is concluded that the proposed methodology enables the desired confidence level to serve as an effective controller of buffer size.

strategic deconfliction↗
NASA NTRSDate not supplied

Confidence-Based Buffer for Strategic Deconfliction with Probabilistic Operational Intent

This paper presents a methodology to expand the 95% confidence level of the elliptical geometry given by Unmanned Aircraft System (UAS) operators planning to fly Beyond Visual Line of Sight (BVLOS) to any confidence level before being fed to the strategic deconfliction (SD) module, effectively increasing the separation buffer between Operational Intents (OIs). To assess the performance of this approach, it is integrated within an adaptation of the Rolling Horizon with K-Position Search volume-based strategic deconfliction approach, previously developed at NASA Ames, preventing the 4D overlapping of OIs shaped by ellipses instead of traditional blocks. Safety and efficiency metrics are evaluated through the deconfliction of four simulated package delivery route network structures across the San Francisco Metropolitan Area with increasing numbers of crossing waypoints (network complexity). Safety assessment entails the in-house creation of a metric to quantify collision occurrences per flight hour based on the frequency at which the probabilistic operational volume segments are sampled, whereas efficiency is measured using ground delay. Results indicate that the largest buffer growth occurs when increasing the confidence level beyond 99.9% and demonstrate the negative impact of network complexity on both metrics, regardless of the OI geometry. Further, the ellipse-based SD adaptation more accurately estimates temporal separation at crossings, allowing deconflicted vehicles to be closer together. It is concluded that the proposed methodology enables the desired confidence level to serve as an effective controller of buffer size.

safety↗
NASA NTRSDate not supplied

A Blockchain Case Study for Urban Air Mobility Operational Intent

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case.

UAM↗
NASA NTRS2023

A Blockchain Case Study for Urban Air Mobility Operational Intent

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case.

UAM↗
NASA NTRS1988

OFMspert - Inference of operator intentions in supervisory control using a blackboard architecture

The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.

Jones, Patricia S.↗
NASA NTRSDate not supplied

A Blockchain Case Study for Urban Air Mobility Operational Intent

The next generation of aerial passenger and cargo transportation may leverage the concept of Urban Air Mobility (UAM). UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) or short takeoff and landing (STOL) aircraft to overcome increasing surface congestion [1]. The UAM concept leverages a decentralized service-based architecture for airspace solutions. Within the environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. Also, various views of UAM flight information are provided to the public and public safety entities [2]. The Federal Aviation Administration (FAA) can coordinate flight information between the FAA controlled National Airspace System (NAS) and the UAM environments through the FAA-Industry Data Exchange Protocol (FIDXP). To realize the potential of UAM, an assurance of cybersecurity is critical for public acceptance. Cybersecurity has come to the forefront highlighting the need to protect these networks and systems from cyberattacks. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. As these threats evolve, the UAM cybersecurity capabilities must adapt to these changes as well [3]. This research focuses on the secure data exchange and storage of this decentralized UAM environment to address these challenges. This research intends to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment. Blockchain technologies can be used for tracking transactions and verifying negotiated agreements between stakeholders in the NAS environment. For example, the record of the submitted flight plan and the approved flight plan could be verified using the Blockchain-based immutable ledger.

UAM↗
NASA NTRS1988

Intent inferencing by an intelligent operator's associate - A validation study

In the supervisory control of a complex, dynamic system, one potential form of aiding for the human operator is a computer-based operator's associate. The design philosophy of the operator's associate is that of 'amplifying' rather than automating human skills. In particular, the associate possesses understanding and control properties. Understanding allows it to infer operator intentions and thus form the basis for context-dependent advice and reminders; control properties allow the human operator to dynamically delegate individual tasks or subfunctions to the associate. This paper focuses on the design, implementation, and validation of the intent inferencing function. Two validation studies are described which empirically demonstrate the viability of the proposed approach to intent inferencing.

Jones, Patricia M.↗
NASA NTRS2022

Intent Modeling and Conflict Probability Calculation for Operations in Upper Class E Airspace

This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.

Air traffic management↗
NASA NTRS2022

Intent Modeling and Conflict Probability Calculation for Operations in Upper Class E Airspace

This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.

Air traffic management, operational intent, confli↗
NASA NTRS2024

A Method of Compliance for Achieving Target Collision Risk in UTM Operations

This work proposes a method of compliance to ensure that the collision risks among small unmanned aircraft systems meet the target level of safety. This method presents what is needed for a strategic conflict detection service to achieve the target level of safety when conflict between operational intents are not permitted in nominal situations. A volume-based collision risk model is first developed to calculate the UA-to-UA collision risk given any two operational intent volumes. With this collision risk model, a test strategy is then proposed to assess if a strategic conflict detection service can reduce the collision risk and meet the target level of safety. The method also specifies operational data that are required to be collected to verify if requirements on conformance are being met. Additionally, two new requirements are identified and proposed by this method beyond the current standard for strategic conflict detection. In the sensitivity analysis, three main factors contributing to the collision risk are investigated. The analysis shows that buffers should be considered in a strategic conflict detection service when deconflicting operational intents. The results also reveal that the selection of test cases plays an important role in evaluating the strategic conflict detection service, and they should be representative and sufficiently complex in evaluation tests.

Collision Risk↗
NASA NTRSDate not supplied

A Method of Compliance for Achieving Target Collision Risk in UTM Operations

This work proposes a method of compliance to ensure that the collision risks among small unmanned aircraft systems meet the target level of safety. This method presents what is needed for a strategic conflict detection service to achieve the target level of safety when conflict between operational intents are not permitted in nominal situations. A volume-based collision risk model is first developed to calculate the UA-to-UA collision risk given any two operational intent volumes. With this collision risk model, a test strategy is then proposed to assess if a strategic conflict detection service can reduce the collision risk and meet the target level of safety. The method also specifies operational data that are required to be collected to verify if requirements on conformance are being met. Additionally, two new requirements are identified and proposed by this method beyond the current standard for strategic conflict detection. In the sensitivity analysis, three main factors contributing to the collision risk are investigated. The analysis shows that buffers should be considered in a strategic conflict detection service when deconflicting operational intents. The results also reveal that the selection of test cases plays an important role in evaluating the strategic conflict detection service, and they should be representative and sufficiently complex in evaluation tests.

UTM, Method of Compliance, collision risk↗
NASA NTRSDate not supplied

A Method of Compliance for Achieving Target Collision Risk in UTM Operations

This work proposes a method of compliance to ensure that the collision risks among small unmanned aircraft systems meet the target level of safety. This method presents what is needed for a strategic conflict detection service to achieve the target level of safety when conflict between operational intents are not permitted in nominal situations. A volume-based collision risk model is first developed to calculate the UA-to-UA collision risk given any two operational intent volumes. With this collision risk model, a test strategy is then proposed to assess if a strategic conflict detection service can reduce the collision risk and meet the target level of safety. The method also specifies operational data that are required to be collected to verify if requirements on conformance are being met. Additionally, two new requirements are identified and proposed by this method beyond the current standard for strategic conflict detection. In the sensitivity analysis, three main factors contributing to the collision risk are investigated. The analysis shows that buffers should be considered in a strategic conflict detection service when deconflicting operational intents. The results also reveal that the selection of test cases plays an important role in evaluating the strategic conflict detection service, and they should be representative and sufficiently complex in evaluation tests.

UTM↗
NASA NTRSDate not supplied

Multi-Party Flight Trajectory Negotiation for Upper Class E Traffic Management

A new operational concept has been proposed in Upper Class E airspace at or above 60,000 feet (Flight Level / FL600), which will allow operators of diverse vehicle characteristics to cooperatively manage and share their operational intents with neighboring operators to avoid conflict. There is a consensus in the community that negotiation for strategic deconfliction is needed, but there are no specific guidelines for how the negotiation should be conducted. There is a need for a structured and cooperative way to resolve the conflict between a wide variety of aircraft projected to be operating in Upper Class E for the negotiation to be carried out routinely. The use of negotiation models are a promising solution that can resolve conflict risks during flight in real-time while taking into account the uncertainty of future vehicle positions and dynamic business considerations. A two-party negotiation model has been researched, but as the traffic demand grows, there is a higher likelihood of conflict involving multiple aircraft that would require a method to handle multi-party conflict. This paper proposes a cooperative multi-party negotiation model inspired by game theory concepts for application to flight trajectory negotiation in Upper Class E traffic management. This model can be applied in flight with operators communicating directly after a potential conflict is detected. Some of the model’s benefits include allowing business costs to be private to operators, allowing operators to collaborate together to find conflict-free flight trajectories, and being compatible with different aircraft and operation types. This model provides a structured procedure for conflict resolution that can handle conflict involving multiple parties, assuming each operator is willing to take on a small cost to themselves in order to reduce the total cost to the group.

Upper Class E Traffic Management (ETM)↗
NASA NTRSDate not supplied

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class-E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution. Overall, the ETM tabletop provided insights into how the community wanted to instantiate the generation and sharing of operational intent, detect strategic conflicts and resolve those conflicts using a preliminary set of procedural community-agreed upon COPs.

Upper Class-E Traffic Management (ETM)↗
NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗