1,523 research outputs found
Identification of Robust Routes using Convective Weather Forcasts
Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System (NAS), especially during summer months when travel demand is high. This has been the motivation for Air Traffic Flow Management (ATFM) algorithms that optimize flight routes in the presence of reduced airspace and airport capacities. These models assume either the availability of reliable probabilistic weather forecasts or accurate predictions of robust routes; unfortunately, such forecasts do not currently exist. This paper adopts a data-driven approach that identifies robust routes and derives stochastic capacity forecasts from deterministic convective weather forecasts. Using techniques from machine learning and extensive data sets of forecast and observed convective weather, the proposed approach classifies routes that are likely to be viable in reality. The resultant model for route robustness can also be mapped into probabilistic airspace capacity forecasts.National Science Foundation (ECCS- 0745237)National Aeronautics and Space Administration NGATSATM Airspace Program (NNA06CN24A
A Concept for Robust, High Density Terminal Air Traffic Operations
This paper describes a concept for future high-density, terminal air traffic operations that has been developed by interpreting the Joint Planning and Development Office s vision for the Next Generation (NextGen) Air Transportation System and coupling it with emergent NASA and other technologies and procedures during the NextGen timeframe. The concept described in this paper includes five core capabilities: 1) Extended Terminal Area Routing, 2) Precision Scheduling Along Routes, 3) Merging and Spacing, 4) Tactical Separation, and 5) Off-Nominal Recovery. Gradual changes are introduced to the National Airspace System (NAS) by phased enhancements to the core capabilities in the form of increased levels of automation and decision support as well as targeted task delegation. NASA will be evaluating these conceptual technological enhancements in a series of human-in-the-loop simulations and will accelerate development of the most promising capabilities in cooperation with the FAA through the Efficient Flows Into Congested Airspace Research Transition Team
Robust flight planning impact assessment considering convective phenomena
Thunderstorms are one of the leading causes of Air Traffic Management delays. In this paper, we assess how incorporating convective information into flight planning algorithms can lead to reductions in reroutings due to storm encounters during the execution of the flight. We use robust open-loop optimal control methodology at the flight planning level and incorporate meteorological uncertainties based on Ensemble Prediction System forecasts. Convective risk areas can be derived from the latter to be included in the objective function. At the execution level, the planned trajectories are included in an air traffic simulator (NAVSIM) under observed weather (wind and storms). In this simulation process, track modifications might be triggered in case of encountering an observed thunderstorm. A tool termed DIVMET based on pathfinding algorithms has been integrated into NAVSIM is considered to that end. Results show that planning robust trajectories (avoiding thus convective areas) reduces the number of storms encounters and increases predictability. This increase in predictability is at a cost in terms of fuel and time, also quantified. © 2021 Elsevier Lt
Human Performance Contributions to Safety in Commercial Aviation
In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Optimization of airport terminal-area air traffic operations under uncertain weather conditions
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 153-158).Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Although Air Traffic Flow Management algorithms exist to schedule and route traffic in the face of disruptions, they require reliable forecasts of airspace capacity. However, there exists a gap between the spatial and temporal accuracy of aviation weather forecasts (and existing capacity models) and what these algorithms assume. In this thesis we consider the problem of integrating currently available convective weather forecasts with air traffic management in terminal airspace (near airports). We first demonstrate how raw convective weather forecasts, which provide deterministic predictions of the Vertically Integrated Liquid (the precipitation content in a column of airspace) can be translated into reliable and accurate probabilistic fore- casts of whether or not a terminal-area route will be blocked. Given a flight route through the terminal-area, we apply techniques from machine learning to determine the probability that the route will be open in actual weather. This probabilistic route blockage predictor is then used to optimize terminal-area operations. We develop an integer programming formulation for a 2-dimensional model of terminal airspace that dynamically moves arrival and departure routes to maximize expected capacity. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked while the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes are indeed open (on average) in the weather that materializes. In the final part of the thesis we consider more realistic models of terminal airspace routing and structure. We develop an A*-based routing algorithm that identifies 3-D routes through airspace that adhere to physical aircraft constraints during climb and descent, are conflict-free, and are likely to avoid convective weather hazards. The proposed approach is aimed at improving traffic manager decision-making in today's operational environment.by Diana Michalek Pfeil.Ph.D
Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms
Convective weather, and thunderstorm development in particular, represents a major source of disruption, delays and safety hazards in the Air Traffic Management system. Thunderstorms are challenging to forecast and evolve on relatively rapid timescales; therefore, aircraft trajectory planning tools need to consider the uncertainty in the forecasted evolution of these convective phenomena. In this work, we use data from a satellite-based product, Rapidly Developing Thunderstorms, to estimate a model of the uncertain evolution of thunderstorms. We then introduce a methodology based on numerical optimal control to generate avoidance trajectories under uncertain convective weather evolution. We design a randomized procedure to initialize the optimal control problem, explore the different resulting local optima, and identify the best trajectory. Finally, we demonstrate the proposed methodology on a realistic test scenario, employing actual forecast data and an aircraft performance model.This work is supported by the Spanish Government through Project entitled Analysis and optimisation of aircraft trajectories under the effects of meteorological uncertainty (TRA2014-58413-C2-2-R)12; this project has been funded under R&D&I actions of Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad (call 2014).Publicad
Air Traffic Management Technology Demonstration - 3 (ATD-3): Operational Concept for the Integration of ATD-3 Capabilities Version 1.0
ATD-3 has developed four capabilities to address its goal and objectives. The four ATD-3 capabilities include: Dynamic Weather Routes (DWR), Multi-Flight Common Routes (MFCR), Traffic Aware Strategic Aircrew Requests (TASAR), and Dynamic Routes for Arrivals in Weather (DRAW). This document describes the long-term, mature vision for the use and incorporation of the ATD-3 capabilities into the National Airspace System (NAS). This vision describes their complementary interaction and the benefit capture that accrues from use. Recognizing that all capabilities are unlikely to be implemented in unison, each of the capabilities is designed and able to be implemented independently. As discrete portions of the integrated capabilities are planned, additional integration efforts should be undertaken to validate the complementary interactions and benefit pool are realized from the selected subset
Robust aircraft trajectory optimization under meteorological uncertainty
Mención Internacional en el título de doctorThe Air Traffic Management (ATM) system in the busiest airspaces in the world
is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental
challenges. One major pillar of this process is the shift towards a concept
of operations centered on aircraft trajectories (called Trajectory-Based Operations or
TBO in Europe) instead of rigid airspace structures. However, its successful implementation
(and, thus, the realization of the associated improvements in ATM performance)
rests on appropriate understanding and management of uncertainty. Due to its complex
socio-technical structure, the design and operations of the ATM system are heavily impacted
by uncertainty, proceeding from multiple sources and propagating through the
interconnections between its subsystems.
One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic
nature, a number of meteorological phenomena of interest cannot be forecasted with
complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in
individual air and ground operations that propagates to all ATM processes. Therefore,
in order to achieve the goals of SESAR and similar programs, it is necessary to deal
with meteorological uncertainty at multiple scales, from the trajectory prediction and
planning processes to flow and traffic management operations.
This thesis addresses the problem of single-aircraft flight planning considering two
important sources of meteorological uncertainty: wind prediction error and convective
activity. As the actual wind field deviates from its forecast, the actual trajectory will
diverge in time from the planned trajectory, generating uncertainty in arrival times,
sector entry and exit times, and fuel burn. Convective activity also impacts trajectory
predictability, as it leads pilots to deviate from their planned route, creating challenging
situations for controllers. In this work, we aim to develop algorithms and methods
for aircraft trajectory optimization that are able to integrate information about the
uncertainty in these meteorological phenomena into the flight planning process at both
pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in
order to generate more efficient and predictable trajectories.
To that end, we frame flight planning as an optimal control problem, modeling the
motion of the aircraft with a point-mass model and the BADA performance model. Optimal
control methods represent a flexible and general approach that has a long history
of success in the aerospace field. As a numerical scheme, we use direct methods, which
can deal with nonlinear systems of moderate and high-dimensional state spaces in a
computationally manageable way. Nevertheless, while this framework is well-developed
in the context of deterministic problems, the techniques for the solution of practical optimal control problems under uncertainty are not as mature, and the methods proposed
in the literature are not applicable to the flight planning problem as it is now
understood.
The first contribution of this thesis addresses this challenge by introducing a framework
for the solution of general nonlinear optimal control problems under parametric
uncertainty. It is based on an ensemble trajectory scheme, where the trajectories of the
system under multiple scenarios are considered simultaneously within the same dynamical
system and the uncertain optimal control problem is turned into a large conventional
optimal control problem that can be then solved by standard, well-studied direct methods
in optimal control. We then employ this approach to solve the robust flight plan
optimization problem at the planning horizon. In order to model uncertainty in the
wind and estimating the probability of convective conditions, we employ Ensemble Prediction
System (EPS) forecasts, which are composed by multiple predictions instead of
a single deterministic one. The resulting method can be used to optimize flight plans for
maximum expected efficiency according to the cost structure of the airline; additionally,
predictability and exposure to convection can be incorporated as additional objectives.
The inherent tradeoffs between these objectives can be assessed with this methodology.
The second part of this thesis presents a solution for the rerouting of aircraft in
uncertain convective weather scenarios at the tactical horizon. The uncertain motion of
convective weather cells is represented with a stochastic model that has been developed
from the output of a deterministic satellite-based nowcast product, Rapidly Developing
Thunderstorms (RDT). A numerical optimal control framework, based on the pointmass
model with the addition of turn dynamics, is employed for optimizing efficiency
and predictability of the proposed trajectories in the presence of uncertainty about
the future evolution of the storm. Finally, the optimization process is initialized by a
randomized heuristic procedure that generates multiple starting points. The combined
framework is able to explore and as exploit the space of solution trajectories in order to
provide the pilot or the air traffic controller with a set of different suggested avoidance
trajectories, as well as information about their expected cost and risk.
The proposed methods are tested on example scenarios based on real data, showing
how different user priorities lead to different flight plans and what tradeoffs are then
present. These examples demonstrate that the solutions described in this thesis are
adequate for the problems that have been formulated. In this way, the flight planning
process can be enhanced to increase the efficiency and predictability of individual aircraft
trajectories, which would lead to higher predictability levels of the ATM system and thus
improvements in multiple performance indicators.El sistema de gestión del tráfico aéreo (Air Traffic Management, ATM) en los espacios
aéreos más congestionados del mundo está siendo reformado para lidiar con múltiples
desafíos socioeconómicos, medioambientales y de capacidad. Un pilar de este proceso es
el gradual reemplazo de las estructuras rígidas de navegación, basadas en aerovías y waypoints,
hacia las operaciones basadas en trayectorias. No obstante, la implementación
exitosa de este concepto y la realización de las ganancias esperadas en rendimiento ATM
requiere entender y gestionar apropiadamente la incertidumbre. Debido a su compleja
estructura socio-técnica, el diseño y operaciones del sistema ATM se encuentran marcadamente
influidos por la incertidumbre, que procede de múltiples fuentes y se propaga
por las interacciones entre subsistemas y operadores humanos.
Uno de los principales focos de incertidumbre en ATM es la meteorología. Debido a su
naturaleza no-linear y caótica, muchos fenómenos de interés no pueden ser pronosticados
con completa precisión en cualquier horizonte temporal, lo que crea disrupción en las
operaciones en aire y tierra que se propaga a otros procesos de ATM. Por lo tanto,
para lograr los objetivos de SESAR e iniciativas análogas, es imprescindible tener en
cuenta la incertidumbre en múltiples escalas espaciotemporales, desde la predicción de
trayectorias hasta la planificación de flujos y tráfico.
Esta tesis aborda el problema de la planificación de vuelo de aeronaves individuales
considerando dos fuentes importantes de incertidumbre meteorológica: el error en la
predicción del viento y la actividad convectiva. Conforme la realización del viento se
desvía de su previsión, la trayectoria real se desviará temporalmente de la planificada, lo
que implica incertidumbre en tiempos de llegada a sectores y aeropuertos y en consumo
de combustible. La actividad convectiva también tiene un impacto en la predictibilidad
de las trayectorias, puesto que obliga a los pilotos a desviarse de sus planes de vuelo
para evitarla, cambiado así la situación de tráfico. En este trabajo, buscamos desarrollar
métodos y algoritmos para la optimización de trayectorias que puedan integrar
información sobre la incertidumbre en estos fenómenos meteorológicos en el proceso de
diseño de planes de vuelo en horizontes de planificación (antes del despegue) y tácticos
(durante el vuelo), con el objetivo de generar trayectorias más eficientes y predecibles.
Con este fin, formulamos la planificación de vuelo como un problema de control
óptimo, modelando la dinámica del avión con un modelo de masa puntual y el modelo
de rendimiento BADA. El control óptimo es un marco flexible y general con un largo
historial de éxito en el campo de la ingeniería aeroespacial. Como método numérico,
empleamos métodos directos, que son capaces de manejar sistemas dinámicos de alta
dimensión con costes computacionales moderados. No obstante, si bien esta metodología es madura en contextos deterministas, la solución de problemas prácticas de control
óptimo bajo incertidumbre en la literatura no está tan desarrollada, y los métodos
propuestos en la literatura no son aplicables al problema de interés.
La primera contribución de esta tesis hace frente a este reto mediante la introducción
de un marco numérico para la resolución de problemas generales de control óptimo
no-lineal bajo incertidumbre paramétrica. El núcleo de este método es un esquema de
conjunto de trayectorias, en el que las trayectorias del sistema dinámico bajo múltiples
escenarios son consideradas de forma simultánea, y el problema de control óptimo bajo
incertidumbre es así transformado en un problema convencional que puede ser tratado
mediante métodos existentes en control óptimo. A continuación, empleamos este método
para resolver el problema de la planificación de vuelo robusta. La incertidumbre en el
viento y la probabilidad de ocurrencia de condiciones convectivas son modeladas mediante
el uso de previsiones de conjunto o ensemble, compuestas por múltiples predicciones
en lugar de una única previsión determinista. Este método puede ser empleado para
maximizar la eficiencia esperada de los planes de vuelo de acuerdo a la estructura de
costes de la aerolínea; además, la predictibilidad de la trayectoria y la exposición a la
convección pueden ser incorporadas como objetivos adicionales. El trade-off entre estos
objetivos puede ser evaluado mediante la metodología propuesta.
La segunda parte de la tesis presenta una solución para reconducir aviones en escenarios
tormentosos en un horizonte táctico. La evolución de las células convectivas es
representada con un modelo estocástico basado en las proyecciones de Rapidly Developing
Thunderstorms (RDT), un sistema determinista basado en imágenes de satélite.
Este modelo es empleado por un método de control óptimo numérico, basado en un
modelo de masa puntual en el que se modela la dinámica de viraje, con el objetivo de
maximizar la eficiencia y predictibilidad de la trayectoria en presencia de incertidumbre
sobre la evolución futura de las tormentas. Finalmente, el proceso de optimizatión es
inicializado por un método heurístico aleatorizado que genera múltiples puntos de inicio
para las iteraciones del optimizador. Esta combinación permite explorar y explotar el
espacio de trayectorias solución para proporcionar al piloto o al controlador un conjunto
de trayectorias propuestas, así como información útil sobre su coste y el riesgo asociado.
Los métodos propuestos son probados en escenarios de ejemplo basados en datos
reales, ilustrando las diferentes opciones disponibles de acuerdo a las prioridades del
planificador y demostrando que las soluciones descritas en esta tesis son adecuadas para
los problemas que se han formulado. De este modo, es posible enriquecer el proceso de
planificación de vuelo para incrementar la eficiencia y predictibilidad de las trayectorias
individuales, lo que contribuiría a mejoras en el rendimiento del sistema ATM.These works have been financially supported by Universidad Carlos III de Madrid
through a PIF scholarship; by Eurocontrol, through the HALA! Research Network grant
10-220210-C2; by the Spanish Ministry of Economy and Competitiveness (MINECO)'s
R&D program, through the OptMet project (TRA2014-58413-C2-2-R); and by the European
Commission's SESAR Horizon 2020 program, through the TBO-Met project
(grant number 699294).Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira iPresidente: Damián Rivas Rivas.- Secretario: Xavier Prats Menéndez.- Vocal: Benavar Sridha
Predictive Weather Display in ATC: Implications for Research and Training
Two systems are central to the Next Generation Air Transportation System (NextGen) air traffic management program - Traffic Management Advisor (TMA) and En Route Automation Modernization (ERAM). One purpose of both systems is to reduce air traffic control (ATC) delay. The present study reports on an exploratory integration of convective weather, a major source of delay, into the ATC systems to allow early re-route around weather in order to reduce delay. Pseudo-controllers ran a series of simulation-based scenarios with screen capture and video collection to assess delay and safety performance. Results provide evidence that delay was reduced by early rerouting in response to convective weather predictions. Implications for training and research are discussed
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