Weather-informed probabilistic forecasting and scenario generation in power systems
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Zhang, Hanyu (ORCID:0000000259030883)↗
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Real-Twin
Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.
Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗
Methodology for Generating Conflict Scenarios by Time Shifting Recorded Traffic Data
A methodology is presented for generating conflict scenarios that can be used as test cases to estimate the operational performance of a conflict probe. Recorded air traffic data is time shifted to create traffic scenarios featuring conflicts with characteristic properties similar to those encountered in typical air traffic operations. First, a reference set of conflicts is obtained from trajectories that are computed using birth points and nominal flight plans extracted from recorded traffic data. Distributions are obtained for several primary properties (e.g., encounter angle) that are most likely to affect the performance of a conflict probe. A genetic algorithm is then utilized to determine the values of time shifts for the recorded track data so that the primary properties of conflicts generated by the time shifted data match those of the reference set. This methodology is successfully demonstrated using recorded traffic data for the Memphis Air Route Traffic Control Center; a key result is that the required time shifts are less than 5 min for 99% of the tracks. It is also observed that close matching of the primary properties used in this study additionally provides a good match for some other secondary properties.
Paglione, Mike↗
Air Traffic Management-eXploration Testbed for Urban Air Mobility Research and Development
The presentation will describe the architecture, current capabilities and some future enhancements of the testbed that is being developed at the National Aeronautics and Space Administration (NASA) to enable benefit, impact, safety and cost assessments for accelerating the deployment of air traffic management concept and technologies in the national airspace system. The testbed will support analysis of operational feasibility of urban air mobility operations, a part of NASA's Air Traffic Management eXploration project, and provide the data needed by regulatory agencies charged with public safety. Introduction of concepts and technologies, especially new concepts and technologies, is difficult and often takes decades because of the inability to assess the operational impact of the interaction between the proposed concept and technology and operationally deployed systems in terms of system-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airlines and other operators. To overcome these limitations, the testbed is developing infrastructure to enable mathematical modeling, human-in-the-loop evaluations and testing with operational systems in a simulated environment. In addition to the difficulty of establishing communications between geographically distributed systems, downloading/installing software, and management of startup, error-handling and shutdown, a major impediment for conducting simulations and human-in-the-loop testing with operational systems is the tedious manual scenario generation process. Several of these difficulties have been addressed in the current state of the testbed. The testbed can be described in terms of the following elements (1) web-based frontend and backend, (2) Testbed Builder, (3) Data Distribution Service, (4) Component Library, (5) Simulation Management, and (6) Scenario Generation. The web-based frontend and backend enable the user to interact with the testbed for tasks such as composing a simulation, running a simulation and retrieving output data. The Testbed Builder application launched from the web frontend is a graphical user interface for the user to drag-and-drop and connect predefined blocks for composing a simulation/scenario generation task. The Builder writes a set of instructions for Simulation Management based on the links between the blocks and the block properties such as the component (executable) associated with a particular block. Management of the distributed simulation is accomplished by Execution and Component Managers. Execution Manager interprets the instructions provided by the Builder to instruct the Component Managers to download components from the Component Library to specified computers and to start them up. Once started, the components communicate with each other by publishing messages and subscribing to messages that are delivered by the Data Distribution Service. The Scenario Generation capability can be used for creating traffic scenarios for Multi-Aircraft Control System, which has been used extensively at NASA for human-in-the-loop-based concept evaluations. The presentation will provide a testbed enabled example scenario of Multi-Aircraft Control System based simulation in which the urban air mobility pilot using the conflict detection and resolution system would interact with the air traffic controllers for resolving conflicts with other aircraft during terminal area operations.
Testbed↗
Air Traffic Management-eXploration Testbed for Urban Air Mobility Research and Development
The presentation will describe the architecture, current capabilities and some future enhancements of the testbed that is being developed at the National Aeronautics and Space Administration (NASA) to enable benefit, impact, safety and cost assessments for accelerating the deployment of air traffic management concept and technologies in the national airspace system. The testbed will support analysis of operational feasibility of urban air mobility operations, a part of NASA's Air Traffic Management eXploration project, and provide the data needed by regulatory agencies charged with public safety. Introduction of concepts and technologies, especially new concepts and technologies, is difficult and often takes decades because of the inability to assess the operational impact of the interaction between the proposed concept and technology and operationally deployed systems in terms of system-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airlines and other operators. To overcome these limitations, the testbed is developing infrastructure to enable mathematical modeling, human-in-the-loop evaluations and testing with operational systems in a simulated environment. In addition to the difficulty of establishing communications between geographically distributed systems, downloading/installing software, and management of startup, error-handling and shutdown, a major impediment for conducting simulations and human-in-the-loop testing with operational systems is the tedious manual scenario generation process. Several of these difficulties have been addressed in the current state of the testbed. The testbed can be described in terms of the following elements- (1) web-based frontend and backend, (2) Testbed Builder, (3) Data Distribution Service, (4) Component Library, (5) Simulation Management, and (6) Scenario Generation. The web-based frontend and backend enable the user to interact with the testbed for tasks such as composing a simulation, running a simulation and retrieving output data. The Testbed Builder application launched from the web frontend is a graphical user interface for the user to drag-and-drop and connect predefined blocks for composing a simulation/scenario generation task. The Builder writes a set of instructions for Simulation Management based on the links between the blocks and the block properties such as the component (executable) associated with a particular block. Management of the distributed simulation is accomplished by Execution and Component Managers. Execution Manager interprets the instructions provided by the Builder to instruct the Component Managers to download components from the Component Library to specified computers and to start them up. Once started, the components communicate with each other by publishing messages and subscribing to messages that are delivered by the Data Distribution Service. The Scenario Generation capability can be used for creating traffic scenarios for Multi-Aircraft Control System, which has been used extensively at NASA for human-in-the-loop-based concept evaluations. The presentation will provide a testbed enabled example scenario of Multi-Aircraft Control System based simulation in which the urban air mobility pilot using the conflict detection and resolution system would interact with the air traffic controllers for resolving conflicts with other aircraft during terminal area operations.
Simulation↗
Reducing a Knowledge-Base Search Space When Data Are Missing
This software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.
James, Mark↗
Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies
Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.
decarbonizing↗
Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies
Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.
decarbonizing↗
Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies
Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.
decarbonizing↗
Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies
Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.
emissions↗
An intelligent training system for space shuttle flight controllers
An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (DeplEx for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The DeplEx also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of DeplEx in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.
Loftin, R. Bowen↗
An intelligent training system for space shuttle flight controllers
An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (Dep1Ex for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The Dep1Ex also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of Dep1Ex in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.
Loftin, R. Bowen↗
MisTec: A software application for supporting space exploration scenario options and technology development analysis and planning
The structure and composition of a new, emerging software application, which models and analyzes space exploration scenario options for feasibility based on technology development projections is presented. The software application consists of four main components: a scenario generator for designing and inputting scenario options and constraints; a processor which performs algorithmic coupling and options analyses of mission activity requirements and technology capabilities; a results display which graphically and textually shows coupling and options analysis results; and a data/knowledge base which contains information on a variety of mission activities and (power and propulsion) technology system capabilities. The general long-range study process used by NASA to support recent studies is briefly introduced to provide the primary basis for comparison for discussing the potential advantages to be gained from developing and applying this king of application. A hypothetical example of a scenario option to facilitate the best conceptual understanding of what the application is, how it works, or the operating methodology, and when it might be applied is presented.
Horsham, Gary A. P.↗
MisTec - A software application for supporting space exploration scenario options and technology development analysis and planning
This structure and composition of a new, emerging software application, which models and analyzes space exploration scenario options for feasibility based on technology development projections is presented. The software application consists of four main components: a scenario generator for designing and inputting scenario options and constraints; a processor which performs algorithmic coupling and options analyses of mission activity requirements and technology capabilities; a results display which graphically and textually shows coupling and options analysis results; and a data/knowledge base which contains information on a variety of mission activities and (power and propulsion) technology system capabilities. The general long-range study process used by NASA to support recent studies is briefly introduced to provide the primary basis for comparison for discussing the potential advantages to be gained from developing and applying this kind of application. A hypothetical example of a scenario option to facilitate the best conceptual understanding of what the application is, how it works, or the operating methodology, and when it might be applied is presented.
Horsham, Gary A. P.↗
Scenario Complexity for Unmanned Aircraft System Traffic
This work introduces an approach to estimate the complexity of a low-altitude air traffic scenario involving multiple UASs using mathematical programming. Given a set of multi-point UAS flight trajectories, vehicle dynamics, and a conflict resolution algorithm, an abstract model is developed such that it can be solved quickly using a mathematical programming optimization software without running high-fidelity simulations that can be computationally expensive and may not suit real-time apA quick and accurate assessment of complexity for a given traffic scenario can help plan and schedule flights to alleviate traffic bottleneck and mitigate operation risks, especially for unmanned aerial system traffic management where high traffic density or complexity is expected. This work introduces a traffic scenario complexity metric that was constructed based on the number of potential conflicts weighted by the conflict resolution cost associated. The cost associated with a conflict is calculated based on the corresponding conflict resolution maneuvers. To obtain the conflict resolution maneuvers, a MILP-based optimization was formulated with the vehicle model and conflict management parameters incorporated. To evaluate the complexity metrics, an approach of using measurements from high-fidelity simulations was proposed. The scenario complexity measurements for 920 random-generated scenarios were obtained through high-fidelity simulations and treated as the ground truth. Two statistics methods: Pearson and Alternative Conditional Expectations were applied for analysis. The results showed that the number of flights has low correlation with the scenario complexity according to the correlation coefficients calculated by both methods. The Alternative Conditional Expectations method shows that the proposed scenario complexity metric has better correlation with the ground truth than the number of potential conflicts.plications. In the abstract model, each vehicle is represented by a time-varied vector associated with position, speed, and heading information. The total extra distance that aircraft need to divert from their original routes to avoid collisions is computed and used to setup a quadratic programming formula. The metrics including the number of conflicts and extra distances travelled by all vehicles are then utilized to estimate the complexity of a given UAS flight scenario. Results and verification against high-fidelity simulations will be provided in the final draft.
traffic complexity↗
A Ground-Delay-Based Approach to Reduce Impedance-Based Airspace Complexity
This paper introduces a ground delay based traffic management approach to reduce the impedance based airspace complexity for a given scenario. The preliminary work presented here extends our prior research on developing an impedance based complexity metric for unmanned aircraft system traffic scenario classification. Impedance based metric was evaluated for 1045 randomly-generated scenarios. Scenarios with overall impedance above a certain threshold were declared as no-go. A ground delay approach was developed to be applied to the rest of the scenarios so as to remediate any scenarios with small areas of high impedance on their impedance maps. A sample application is shown for a scenario with sixty flights. The final paper will include detailed trade-offs between overall accrued system delay and the delay tolerance for each aircraft. A sample application to a potential Urban Air Mobility traffic scenario will also be included in the final paper.
Vishwanath Bulusu↗
A Ground-Delay-Based Approach to Reduce Impedance-Based Airspace Complexity
This paper introduces a ground-delay-based traffic management approach to reduce the impedance-based airspace complexity for a given scenario. This work extends our prior research on developing an impedance-based complexity metric for unmanned aircraft system traffic scenario classification. Impedance-based metric was evaluated for 1045 randomly generated scenarios. Scenarios with overall impedance above a certain threshold were declared as not feasible. A ground-delay-based approach was developed to be applied to the rest of the scenarios so as to remediate any scenarios with small areas of high impedance on their impedance maps. A sample application is shown for a scenario with sixty flights. The detailed trade-offs between overall accrued system delay, the number of delayed flights, the total number of conflicts and the highest impedance observed as a function of the delay tolerance for each aircraft are provided. Potential applications to Urban Air Mobility traffic scenarios are also discussed.
ground-delay based traffic management↗
An Impedance-Based Complexity Metric for Unmanned Aircraft System Traffic Scenario Classification
This paper introduces an impedance-based metric to capture the complexity of a given unmanned aircraft system traffic scenario. The metric accounts for both the number of aircraft and the traffic flow pattern. The work presented here extends an earlier approach that introduced another scenario complexity metric based on the number of potential conflicts weighted by the conflict resolution cost associated. Complexity measurements for randomly-generated scenarios were produced through high-fidelity fast-time simulations and treated as baseline. Then the impedance based metric was evaluated, for the same scenarios, without the need for an actual flight simulation and a conflict resolution method. The results show that the impedance-based metric has a strong correlation to the baseline data and performs marginally better than the weighted conflict-based complexity metric introduced in the earlier work. The metric computation generates impedance maps which are useful for identifying high complexity regions in a scenario, where flight plan changes might be necessitated. This metric can therefore be used, in conjunction with other complexity metrics, to inform adequate traffic management strategies and classify a traffic scenario as acceptable, unacceptable or acceptable with changes made to flight plans that pass through the high complexity regions. The metric can also be used as a guidance metric for strategic conflict management methods.
Complexity↗