Diverter Decision Aiding for In-Flight Diversions

It was determined that artificial intelligence technology can provide pilots with the help they need in making the complex decisions concerning en route changes in a flight plan. A diverter system should have the capability to take all of the available information and produce a recommendation to the pilot. Phase three illustrated that using Joshua to develop rules for an expert system and a Statice database provided additional flexibility by permitting the development of dynamic weighting of diversion relevant parameters. This increases the fidelity of the AI functions cited as useful in aiding the pilot to perform situational assessment, navigation rerouting, flight planning/replanning, and maneuver execution. Additionally, a prototype pilot-vehicle interface (PVI) was designed providing for the integration of both text and graphical based information. Advanced technologies were applied to PVI design, resulting in a hierarchical menu based architecture to increase the efficiency of information transfer while reducing expected workload. Additional efficiency was gained by integrating spatial and text displays into an integrated user interface

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Assessing Wind Impact on Semi-Autonomous Drone Landings for In-Contact Power Line Inspection

In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The performance of the system is evaluated for two landing strategies, variously controllers parameters and four level of wind intensities. The results show that a two-stage landing strategies offers higher probabilities of landing success and give insight regarding the best controller parameters and the maximum wind level for which the system is robust. Lastly, an experimental demonstration of the system landing autonomously on a power line is presented

A study for active control research and validation using the Total In-Flight Simulator (TIFS) aircraft

The results of a feasibility study and preliminary design for active control research and validation using the Total In-Flight Simulator (TIFS) aircraft are documented. Active control functions which can be demonstrated on the TIFS aircraft and the cost of preparing, equipping, and operating the TIFS aircraft for active control technology development are determined. It is shown that the TIFS aircraft is as a suitable test bed for inflight research and validation of many ACT concepts

An Intelligent Autopilot System that learns piloting skills from human pilots by imitation

An Intelligent Autopilot System (IAS) that can learn piloting skills by observing and imitating expert human pilots is proposed. IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to handle flight uncertainties, and the need to construct control models manually. A robust Learning by Imitation approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured from these demonstrations. The datasets are then used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when performing piloting tasks including handling flight uncertainties such as severe weather conditions. Experiments show that IAS performs learned take-off, climb, and slow ascent tasks with high accuracy even after being presented with limited examples, as measured by Mean Absolute Error and Mean Absolute Deviation. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to pilot an aircraft starting from the stationary position on the runway, and ending with a steady cruise

Aerodynamic detailed design of an Unmanned Aerial Vehicle with VTOL capabilities

ALF/ENGAER 139424-L Vasco Luís Martins Ferreira Coelho. Examination Committee: Chairperson: BGEN/EngEl 119923-E Rui Fernando da Costa Ferreira; Supervisor: MAJ/EngAer 131603-G Joao Vítor Aguiar Vieira Caetano, Dr. Frederico José Prata Rente Reis Afonso; Member of the Committee: Prof. Dr. Afzal SulemanEsta tese está integrada num projeto de desenvolvimento de um veículo aéreo não tripulado capaz de efetuar descolagem e aterragem vertical, e tendo hidrogénio como principal fonte de energia utilizando para tal uma célula de combustível. A dissertação foca-se nas fases de desenvolvimento preliminar e detalhada no que diz respeito a estudos aerodinâmicos e desempenho em voo. A fase preliminar abrange a conceção da asa e da cauda, recorrendo ao software XFLR5, em conjunto com uma estimativa da resistência aerodinâmica total da aeronave, recorrendo a expressões semi-empíricas. Para a análise detalhada, foi utilizado o software de mecânica de fluidos computacional Fluent. A escolha do modelo de turbulência SST, em conjunto com o modelo de transição y_Re0 , é validada pelas simulação 2D do perfil SG6042, apresentando resultados consistentes com os dados experimentais. A polar aerodinâmica da asa é obtida através da simulações 3D da mesma para vários ângulos de ataque. Por forma a melhorar as propriedades aerodinâmicas da asa, foi aplicada torção à ponta da asa, movendo a região inicial da perda da ponta da asa para a raiz. O impacto do sistema de propulsão vertical na resistência aerodinâmica em voo cruzeiro é avaliado através da realização de testes em túnel de vento e simulações em Fluent. Simulações de toda a aeronave concluem que, dependendo do alinhamento dos rotores, a resistência aerodinâmica da aeronave varia entre 16.32 e 19.22 N para voo cruzeiro, resultando num tempo total de voo entre 3H05 e 3H25.This thesis is part of a project to design an unmanned aerial vehicle capable of performing vertical take-off and landing, and having hydrogen as its main energy source by using a fuel cell. The present dissertation is focused on the preliminary and detailed design phases regarding aerodynamics and flight performance studies. The preliminary phase encompasses the wing and tail design, with the aid of XFLR5, together with an estimate of the total aircraft drag by resorting to semi-empirical expressions. A longitudinal static stability analysis is conducted, and the unmanned aerial vehicle characteristics are presented after the preliminary phase of the project. For the detailed analysis, Fluent was chosen as the computational fluid dynamics software to be used. 2D simulation over the SG6042 wing airfoil validated the choice of the SST turbulence model, coupled with the y_ Re0 transition model, as the results were consistent with experimental data. The drag polar of the wing is obtained by simulating the 3D wing at various angles of attack. To enhance the wing aerodynamic properties, twist was given to the wingtip, moving the stall region from the wingtip to the root. The impact of the vertical propulsion system on the drag at cruise is assessed by performing wind tunnel tests and simulations on Fluent. Simulations of the entire aircraft conclude that, depending on the stopping position of the rotors, the drag of the aircraft varies between 16.32 and 19.22 N for cruise, which results in a total flight time between 3H05 and 3H25.N/

Aeronautical engineering: A continuing bibliography with indexes (supplement 292)

This bibliography lists 675 reports, articles, and other documents recently introduced into the NASA scientific and technical information system database. Subject coverage includes the following: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A continuing bibliography with indexes (supplement 323)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Optimisation du trafic aérien à l'arrivée dans la zone terminale et dans l'espace aérien étendu

Selon les prévisions à long terme du trafic aérien de l'Organisation de l'Aviation Civile Internationale (OACI) en 2018, le trafic mondial de passagers devrait augmenter de 4,2% par an de 2018 à 2038. Bien que l'épidémie de COVID-19 ait eu un impact énorme sur le transport aérien, il se rétablit progressivement. Dès lors, l'efficacité et la sécurité resteront les principales problématiques du trafic aérien, notamment au niveau de la piste qui est le principal goulot d'étranglement du système. Dans le domaine de la gestion du trafic aérien, la zone de manœuvre terminale (TMA) est l'une des zones les plus complexes à gérer. En conséquence, le développement d'outils d'aide à la décision pour gérer l'arrivée des avions est primordial. Dans cette thèse, nous proposons deux approaches d'optimisation qui visent à fournir des solutions de contrôle pour la gestion des arrivées dans la TMA et dans un horizon étendu intégrant la phase en route. Premièrement, nous abordons le problème d'ordonnancement des avions sous incertitude dans la TMA. La quantification et la propagation de l'incertitude le long des routes sont réalisées grâce à un modèle de trajectoire qui représente les informations temporelles sous forme de variables aléatoires. La détection et la résolution des conflits sont effectuées à des points de cheminement d'un réseau prédéfini sur la base des informations temporelles prédites à partir de ce modèle. En minimisant l'espérance du nombre de conflits, les vols peuvent être bien séparés. Outre le modèle proposé, deux autres modèles de la litérrature - un modèle déterministe et un modèle intégrant des marges de séparation - sont présentés comme références. Un recuit simulé (SA) combiné à une fenêtre glissante temporelle est proposé pour résoudre une étude de cas de l'aéroport de Paris Charles de Gaulle (CDG). De plus, un cadre de simulation basé sur l'approche Monte-Carlo est implémenté pour perturber aléatoirement les horaires optimisés des trois modèles afin d'évaluer leurs performances. Les résultats statistiques montrent que le modèle proposé présente des avantages absolus dans l'absorption des conflits en cas d'incertitude. Dans une deuxième partie, nous abordons un problème dynamique basé sur le concept de Gestion des Arrivées Étendue (E-AMAN). L'horizon E-AMAN est étendu jusqu'à 500 NM de l'aéroport de destination permettant ainsi une planification anticipée. Le caractère dynamique est traitée par la mise à jour périodique des informations de trajectoires réelles sur la base de l'approche par horizon glissant. Pour chaque horizon temporel, un sous-problème est établi avec pour objectif une somme pondérée de métriques de sécurité du segment en route et de la TMA. Une approche d'attribution dynamique des poids est proposée pour souligner le fait qu'à mesure qu'un aéronef se rapproche de la TMA, le poids de ses métriques associées à la TMA devrait augmenter. Une étude de cas est réalisée à partir des données réelles de l'aéroport de Paris CDG. Les résultats finaux montrent que grâce à cet ajustement anticipé, les heures d'arrivée des avions sont proches des heures prévues tout en assurant la sécurité et en réduisant les attentes. Dans la troisième partie de cette thèse, on propose un algorithme qui accélère le processus d'optimisation. Au lieu d'évaluer les performances de tous les aéronefs, les performances d'un seul aéronef sont concentrées dans la fonction objectif. Grâce à ce changement, le processus d'optimisation bénéficie d'une évaluation d'objectif rapide et d'une vitesse de convergence élevée. Afin de vérifier l'algorithme proposé, les résultats sont analysés en termes de temps d'exécution et de qualité des résultats par rapport à l'algorithme utilisé à l'origine.According to the long term air traffic forecasts done by International Civil Aviation Organization (ICAO) in 2018, global passenger traffic is expected to grow by 4.2% annually from 2018 to 2038 using the traffic data of 2018 as a baseline. Even though the outbreak of COVID-19 has caused a huge impact on the air transportation, it is gradually restoring. Considering the potential demand in future, air traffic efficiency and safety will remain critical issues to be considered. In the airspace system, the runway is the main bottleneck in the aviation chain. Moreover, in the domain of air traffic management, the Terminal Maneuvering Area (TMA) is one of the most complex areas with all arrivals converging to land. This motivates the development of suitable decision support tools for providing proper advisories for arrival management. In this thesis, we propose two optimization approaches that aim to provide suitable control solutions for arrival management in the TMA and in the extended horizon that includes the TMA and the enroute phase. In the first part of this thesis, we address the aircraft scheduling problem under uncertainty in the TMA. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and the model that incorporates separation buffers - are presented as benchmarks. Simulated annealing (SA) combined with the time decomposition sliding window approach is used for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented to randomly perturb the optimized schedules of the three models so as to evaluate their performances. Statistical results show that the proposed model has absolute advantages in conflict absorption when uncertainty arises. In the second part of this thesis, we address a dynamic/on-line problem based on the concept of Extended Arrival MANagement (E-AMAN). The E-AMAN horizon is extended up to 500NM from the destination airport so as to enhance the cooperation and situational awareness of the upstream sector control and the TMA control. The dynamic feature is addressed by periodically updating the real aircraft trajectory information based on the rolling horizon approach. For each time horizon, a sub-problem is established taking the weighted sum of safety metrics in the enroute segment and in the TMA as objective. A dynamic weights assignment approach is proposed to emphasize the fact that as an aircraft gets closer to the TMA, the weight for its metrics associated with the TMA should increase. A case study is carried out using the real arrival traffic data of the Paris CDG airport. Final results show that through early adjustment, the arrival time of the aircraft can meet the required schedule for entering the TMA, thus ensuring overall safety and reducing holding time. In the third part of this thesis, an algorithm that expedites the optimization process is proposed. Instead of evaluating the performance of all aircraft, single aircraft performance is focused and a corresponding objective function is created. Through this change, the optimization process benefits from fast evaluation of objective and high convergence speed. In order to verify the proposed algorithm, results are analyzed in terms of execution time and quality of result compared to the originally used algorithm