https://www.um.edu.mt/library/oar/handle/123456789/81367 2026-04-27T07:33:33Z 2026-04-27T07:33:33Z https://www.um.edu.mt/library/oar/handle/123456789/118587 2024-02-15T06:38:29Z 2021-01-01T00:00:00Z

Title: Design of a flywheel for a kinetic energy recovery and storage system from a landing aircraft Abstract: With the ever-increasing environmental pressure, the aviation industry is in constant pursuit to reduce fuel consumption, both in flight as well as on the ground. This thesis addresses engineless aircraft ground movement by developing a concept for energy recovery and storage from a landing aircraft. Following an economic assessment to assess the concept, the thesis develops a design process to establish the shape, material, geometry and operational requirements of the flywheel. Following analytical calculations of the stresses induced in the flywheel, a finite element analysis of the flywheel under operational loads is executed and results analysed with reference to the suitability of the design method followed. Results illustrate that stresses induced by operational rotational speeds are within material stress constraints determined by the material properties. Description: M.Sc.(Melit.)

2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/118584 2024-02-15T06:35:52Z 2021-01-01T00:00:00Z

Title: Aerodynamic stall recovery using artificial intelligence techniques Abstract: According to International Civil Aviation Organization (ICAO) in 2019 just before Covid-19 started, 4.5 billion people worldwide travelled by plane. That is nearly half the population of the world. Although, accidents happen, statistically, it is the safest mode to travel from one place to another as of now. This is because of extensive training given to pilots, the on-board automation and protection systems of modern commercial aircraft. Despite the on-board automation and protection systems, aerodynamic stall is still a possible occurrence and pilots undergo stall detection and recovery training to deal with such scenarios. Nevertheless, accidents have occurred due to pilot error during stall recovery. This work uses combination of Reinforcement Learning (RL) algorithm, Behavioral Cloning (BC) and Deep Learning (DeL) based regression model to train multiple Machine Learning (ML) models to automatically recover an aircraft from a wings level (1G) stall, stall during a turn and stabilize it. The RL environment consists of X-Plane flight simulator, NASA’s XPlaneConnect plugin to interface X-Plane with python programming language. The design of whole setup and implementation of the algorithms is discussed, together with the training and testing of the ML models in this dissertation. The stall recovery process was divided into two parts. The first part was about reducing the Angle of Attack (AoA) below the critical AoA. Once, the current AoA is less than critical AoA then the side stick controls are handed over to the second ML model. The first agent is based on Deep Deterministic Policy Gradient (DDPG) algorithm, which has been pre-trained using BC technique. The data which is used to pre-train the actor network of DDPG is recorded with the help of expert pilots. Pre-training the actor network helps the RL agent to converge at a faster rate and learn a policy which is similar to that of an expert pilot. The pre-training of the actor network significantly reduced the time taken to train the first agent to recover from a stall as the actor network performed like an expert pilot. Once, the agent learns a policy which is similar to expert’s policy. A random noise is added to the output of the network to help the agent to explore the RL environment. This exploration is needed to help the agent to find better policies to recover from an aerodynamic stall. The second agent is a DeL regression model, which is trained on a different expert recorded dataset. The DL regression model is responsible to stabilize the aircraft and reach a safe airspeed once the RL model hands over the side stick control to the regression model. The results obtained in this work are satisfactory as the ML models have been able to recover from stall events at various altitudes between 3,000 feet and 30,000 feet. This dissertation present and discuss the training and test results in further detail and will examine the sensitivity of the algorithms to various other factors. Description: M.Sc. (Melit.)

2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/81708 2021-10-04T09:43:25Z 2021-01-01T00:00:00Z

Title: Multi-sensor obstacle detection and tracking for aircraft ground operations Abstract: Aviation accident reports demonstrate that accidents and incidents during aircraft ground operations have remained unresolved. The majority of these accidents arise from pilot error. Air Traffic Control limit this problem to a certain extent by providing sequencing to ground traffic. However, a proper solution for obstacle avoidance is still inexistent. This thesis addresses the problem of obstacle avoidance by proposing an obstacle detection and tracking technology that can be used to determine the distance of separation between aircraft and obstacles. Unlike previous work, the proposed solution fuses data acquired from two colour cameras and a LIDAR sensor. Image data acquired from the two cameras is fused using stereo vision techniques. These techniques compare pixels between the left and right images to recover depth information. This information is then used to map each image pixel to its corresponding 3D world coordinate. Another set of 3D points is acquired from the LIDAR sensor. The two sets of 3D points, referred to as point clouds, are processed and analysed to detect obstacles. Detected obstacles are passed on to a tracking algorithm that consists of a Particle Filter and an Occupancy Grid. The Particle Filter tracks the positions of detected obstacles whilst the Occupancy Grid tracks their shapes. The tracked information may then be used to determine the distance of separation between the aircraft and obstacles. The proposed technology was evaluated in different scenarios through a series of experiments. The first batch of experiments was carried out in a synthetic environment. Meanwhile, the second batch of experiments was carried out in a real environment. The accuracy and performance of the proposed sensor fusion algorithm were identified. The results show that it successfully detects obstacles and that it manages to improve confidence in the area they occupy. Description: Ph.D.(Melit.)

2021-01-01T00:00:00Z