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Sridhar, Banavar

Publications and source records attributed to Sridhar, Banavar.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: NASA Case Studies

There is an increasing interest in applying methods based on Machine Learning Techniques(MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Observations on the Application of Machine Learning Techniques to Aviation Operations

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories: (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: A Case Study

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories 58; (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2019

Application of Machine Learning Techniques to Aviation Operations: Promises and Challenges

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This paper compares the methodology used in and issues to be addressed in applying either model-driven or data-driven methods. Some aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for data-driven methods. The application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT is the favored approach, (b) marginal difference between regression methods using physics-based models and MLT and (c) better results using a blend of physics-based methods combined with MLT. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2019

Analysis of Interactions Between Urban Air Mobility (UAM) Operations and Conventional Traffic in Urban Areas: Traffic Alert and Collision Avoidance System (TCAS) Study for UAM Operations

This paper presents results on potential interaction effects from UAM (Urban Air Mobility) operations integrated into current operational scenarios by evaluating if/where/how Traffic alert and Collision Avoidance System (TCAS) alerts are triggered on-board commercial aircraft. A range of operational scenarios are evaluated with combinations of UAM vehicle route, speed, altitude, and direction along the DFW (Dallas-Fort Worth) "spine route." The effect of UAM altitude uncertainty on the above is also explored. The analysis was done for both South flow and North flow configurations of DFW. When UAM operations are deterministic, no TCAS RAs (Resolution Advisories) are issued. However, UAM altitude uncertainties point out geographic areas of concern with the associated severity of interactions.

Airspace↗
NASA NTRS2019

Analysis of Interactions Between Urban Air Mobility (UAM) Operations and Conventional Traffic in Urban Areas: Traffic Alert and Collision Avoidance (TCAS) Study for UAM Operations

This paper presents a preliminary modeling and analysis of interactions between proposed UAM operations and present-day conventional traffic if UAM operations occurred along FAA- approved helicopter routes and altitude ceilings. It assesses the extent to which the UAM operations will trigger TCAS resolution advisories (RA) aboard the conventional aircraft in the Dallas/Fort Worth (DFW) terminal airspace. It is observed that under deterministic UAM operational conditions, no RAs will be triggered. Furthermore, the impact of UAM altitude uncertainty is also evaluated. It is observed that restricting the UAM cruise altitudes to 990 feet above Mean Sea Level (MSL) or below reduced the chance of triggering an RA to under five percent throughout the day, even in the presence of maximum altitude error of 30 feet.

Bulusu, Vishwanath↗
NASA NTRS2019

Complex Dynamics of Air Traffic Flow

Air traffic in the United States has continued to grow at a steady pace since 1980, except for a dip immediately after the tragic events of September 11, 2001. There are different growth scenarios associated both with the magnitude and the composition of the future air traffic. The Terminal Area Forecast (TAF), prepared every year by the FAA, projects the growth of traffic in the United States. Both Boeing and Airbus publish market outlooks for air travel annually. Although predicting the future growth of traffic is difficult, there are two significant trends: heavily congested major airports continue to see an increase in traffic, and the emergence of regional jets and other smaller aircraft with fewer passengers operating directly between non-major airports. The interaction between air traffic demand and the ability of the system to provide the necessary airport and airspace resources can be modeled as a network. The size of the resulting network varies depending on the choice of its nodes. It would be useful to understand the properties of this network to guide future design and development. Many questions, such as the growth of delay with increasing traffic demand and impact of the en route weather on future air traffic, require a systematic understanding of the properties of the air traffic network. There has been a major advance in the understanding of the behavior of networks with a large number of components. Several theories have been advanced about the evolution of large biological and engineering networks by authors in diversified disciplines like physics, mathematics, biology and computer science. Several networks exhibit a scale-free property in the sense that the probabilistic distribution of their nodes as a function of connections decreases slower than an exponential. These networks are characterized by the fact that a small number of components have a disproportionate influence on the performance of the network. Scale-free networks are tolerant to random failure of components, but are vulnerable to selective attack on components. This paper examines two network representations for the baseline air traffic system. A network defined with the 40 major airports as nodes and with standard flight routes as links has a characteristic scale: all nodes have 60 or more links and no node has more than 460 links. Another network is defined with baseline aircraft routing structure exhibits an exponentially truncated scale-free behavior. Its degree ranges from 2 connections to 2900 connections, and 225 nodes have more than 250 connections. Furthermore, those high-degree nodes are homogeneously distributed in the airspace. A consequence of this scale-free behavior is that the random loss of a single node has little impact, but the loss of multiple high-degree nodes (such as occurs during major storms in busy airspace) can adversely impact the system. Two future scenarios of air traffic growth are used to predict the growth of air traffic in the United States. It is shown that a three-times growth in the overall traffic may result in a ten-times impact on the density of traffic in certain parts of the United States.

Scale-free Networks↗
NASA NTRS2018

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↗
NASA NTRS2018

TCAS Alerts from Simulated Urban Air Mobility Flights Along FAA Helicopter Routes in Dallas-Fort Worth

This is a presentation of the current state of a TCAS study being conducted by the ATM-X project. This study examines the rate at which an urban air mobility vehicle flying along existing helicopter routes near DFW will trigger alerts on the TCAS II systems equipped on commercial airliners flying in the D10 TRACON of DFW airspace. The presentation will cover initial results, as well as next steps on the path towards producing a full paper. These initial results show that some aircraft could fly along published helicopter routes, while obeying published restrictions, and fly into and out of DFW airport without triggering any TCAS RAs, so long as the airport is operating in South Flow and the UAM vehicles approach and depart; the airport in the same direction as commercial traffic. Attempting to take off opposite the direction of commercial traffic produces a large number of TCAS RAs. Future work will examine North Flow, check sensitivity to aircraft performance, and examine alternative routes.

helicopter routes↗
NASA NTRS2018

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↗
NASA NTRS2017

Evolution of a Simulation Testbed into an Operational Tool

This paper describes the evolution over a 20-year period of the Future ATM (Air Traffic Management) Concepts Evaluation Tool (FACET) from a National Airspace System (NAS) based simulation testbed into an operational tool. FACET was developed as a testbed for assessing futuristic ATM concepts, e.g., automated conflict detection and resolution. NAS Constraint Evaluation and Notification Tool (NASCENT) is an application, within FACET, for alerting airspace users of inefficiencies in flight operations and advising time- and fuel-saving reroutes.It is currently in use at American Airlines Integrated Operations Center in Fort Worth, TX. The concepts assessed,research conducted, and the operational capability developed, along with the NASA support and achievements are presented in this paper.

Sheth, Kapil↗
NASA NTRS2017

Efficient Planning of Wind-Optimal Routes in North Atlantic Oceanic Airspace

The North Atlantic oceanic airspace (NAT) is crossed daily by more than a thousand flights, which are greatly affected by strong jet stream air currents. Several studies devoted to generating wind-optimal (WO) aircraft trajectories in the NAT demonstrated great efficiency of such an approach for individual flights. However, because of the large separation norms imposed in the NAT, previously proposed WO trajectories induce a large number of potential conflicts. Much work has been done on strategic conflict detection and resolution (CDR) in the NAT. The work presented here extends previous methods and attempts to take advantage of the NAT traffic structure to simplify the problem and improve the results of CDR. Four approaches are studied in this work: 1) subdividing the existing CDR problem into sub-problems of smaller sizes, which are easier to handle; 2) more efficient data reorganization within the considered time period; 3) problem localization, i.e. concentrating the resolution effort in the most conflicted regions; 4) applying CDR to the pre-tactical decision horizon (a couple of hours in advance). Obtained results show that these methods efficiently resolve potential conflicts at the strategic and pre-tactical levels by keeping the resulting trajectories close to the initial WO ones.

Rodionova, Olga↗
NASA NTRS2017

Efficient Trajectory Options Allocation for the Collaborative Trajectory Options Program

The Collaborative Trajectory Options Program (CTOP) is a Traffic Management Initiative (TMI) intended to control the air traffic flow rates at multiple specified Flow Constrained Areas (FCAs), where demand exceeds capacity. CTOP allows flight operators to submit the desired Trajectory Options Set (TOS) for each affected flight with associated Relative Trajectory Cost (RTC) for each option. CTOP then creates a feasible schedule that complies with capacity constraints by assigning affected flights with routes and departure delays in such a way as to minimize the total cost while maintaining equity across flight operators. The current version of CTOP implements a Ration-by-Schedule (RBS) scheme, which assigns the best available options to flights based on a First-Scheduled-First-Served heuristic. In the present study, an alternative flight scheduling approach is developed based on linear optimization. Results suggest that such an approach can significantly reduce flight delays, in the deterministic case, while maintaining equity as defined using a Max-Min fairness scheme.

Traffic Management Initiative (TMI)↗
NASA NTRS2016

Strategic Design of Long-Haul and Oceanic Aircraft Trajectories in Aviation Operations

Long-Haul Aircraft consume most of their fuel during the cruise phase of flight. The inefficiency in cruise flights compared to efficient routes varies is around 3 to 4. The efficiency of oceanic flights is low due to limited navigational and communication equipment, congestion and airspace restrictions. The availability of Automated Dependent Surveillance-Broadcast (ADS-B) and other improvements provides opportunity for better strategic planning of trajectories. Transatlantic flights between US and Europe constitute one of the busiest oceanic airspace regions in the world. This talk examines the benefits of a wind-optimal trajectory concept with a strategic de-confliction component compared to the current flight planning using the North Atlantic Tracks. The analysis is based on air traffic between US and Europe during July 2012. The potential fuel savings are in the range of (420-970) kg per flight for a Boeing 767-300, the most widely used aircraft between the city-pairs in this study. The talk also describes a global simulation of aviation operations combining flight plans and real air traffic data with historical commercial city-pair aircraft type and schedule data and global atmospheric data. The resulting capability extends the simulation and optimization functions of NASAs Future Air Traffic Management Concept Evaluation Tool (FACET) to global scale. This new capability is used to characterize the evolution of global air traffic, analyze fuel savings and seasonal variations in the long-haul wind-optimal traffic patterns in six major regions of the world.

strategic trajectory design↗
NASA NTRS2016

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

wind uncertainties↗
NASA NTRS2016

Application of Aviation Turbulence Information to Air-Traffic Management (ATM): Chapter - 24

Unexpected turbulence especially in the upper troposphere and lower stratosphere where cabin crews and passengers in cruising aircraft are likely to unbuckle causes in-flight injuries, structural damage, and flight delay. Therefore, turbulence information can be used to improve safety while pursuing efficiency in Air-Traffic Management (ATM). In this chapter, simple modeling of aircraft trajectories combined with wind and turbulence predictions can suggest the optimal solution of flight plans that minimizes both total flight time (e.g., fuel consumption) and potential encounters of turbulence from departure to arrival airports. Also, probabilistic ensemble turbulence forecasts are applied to suggest an optimal strategic and tactical ATM route planning in a given weather and turbulence condition in the United States which are evaluated against in situ Eddy Dissipation Rate observations from commercial aircraft. Finally, variations of long-haul trans-Oceanic flight routes and their turbulence potentials are investigated using a global reanalysis data to understand how the upper-level large-scale flow patterns can affect the long-term ATM planning through the changes of winds and turbulence conditions.

Wind Optimal↗
NASA NTRS2016

Improving Operational Acceptability of Dynamic Weather Routes Through Analysis of Commonly Use Routings

The Dynamic Weather Routes (DWR) tool is a ground-based trajectory automation system that continuously and automatically analyzes active in-flight aircraft in en route airspace to find simple modifications to flight plan routes that can save significant flying time, while avoiding weather and considering traffic conflicts, airspace sector congestion, special use airspace, and FAA routing restrictions. Trials of the DWR system have shown that significant delay savings are possible. However, some DWR advised routes are also rejected by dispatchers or modified before being accepted. Similarly, of those sent by dispatchers to flight crews as proposed route change requests, many are not accepted by air traffic control, or are modified before implementation as Center route amendments. Such actions suggest that the operational acceptability of DWR advised route corrections could be improved, which may reduce workload and increase delay savings. This paper analyzes the historical usage of different flight routings, varying from simple waypoint pairs to lengthy strings of waypoints incorporating jet routes, in order to improve DWR route acceptability. An approach is developed that can be incorporated into DWR, advising routings with high historical usage and savings potential similar to that of the nominal DWR advisory. It is hypothesized that modifying a nominal DWR routing to one that is commonly used, and nearby, will result in more actual savings since common routings are generally familiar and operationally acceptable to air traffic control. The approach allows routing segments with high historical usage to be concatenated to form routes that meet all DWR constraints. The relevance of a route's historical usage to its acceptance by dispatchers and air traffic control is quantified by analyzing historical DWR data. Results indicate that while historical usage may be less of a concern to flight dispatchers accepting or rejecting DWR advised route corrections, it may be important to air traffic control acceptance of DWR routes.

dynamic weather routes↗