Aeronautical engineering

Supervisors: Dr. Zihao Wang, A/Prof Nicholas Lawson and Dr. K C Wong

Eligibility: Familiarity with Python and MATLAB. WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description: AMME is seeking an enthusiastic student to develop new capabilities for the recently commissioned Flight Simulator Facility (FSF). The project aims to establish a software framework that allows users to validate their model-based autopilot design in the FSF using a digitally planned mission.

The project requires the student to design an autopilot system for a nominal aircraft (e.g. Pilatus PC-9) using existing aircraft model and frequency domain methods. The autopilot design can then be implemented in the FSF through the in-house developed autopilot plug-in for X-Plane 11.

The student is then expected to validate the autopilot’s performance by flying the aircraft using the autopilot in the SFS on a mission planned by ANASYS STK Mission Planning Software. The autopilot performance is then compared to the simulated results in ANSYS SCADE for validation. The student will have access to the FSF and ANSYS Software Suite to demonstrate the outcomes.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors: A/Prof. Nicholas Lawson and Prof. Anita Ho-Baillie

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description: Renewable energy through photovoltaic (PV) solar panels is now making up a significant amount of the world’s renewable energy ‘net zero’ strategy. PV solar panel performance deteriorates at elevated temperature of the panel either on an individual panel basis, or collectively in a solar farm environment.

In this project, the student or students will construct a computational fluid mechanic (CFD) model of a generic solar panel system, to allow a prediction of the flow field and heat transfer characteristics, given various variables, such as boundary layer profile and wind direction. Validation of this model will use published literature and Civil Eng wind tunnel data. Further extensions of the basic model into a solar farm array are also possible through several student projects.

In all cases, the students will work with Assoc. Prof. Nicholas Lawson in AMME and Prof. Anita Ho-Ballie in the School of Physics, a leading expert in PV.

Requirement to be on campus: Yes *dependent on government’s health advice..
 

Supervisors: Dr. Zihao Wang, Dr. KC Wong

Eligibility: WAM > 75; MATLAB; Python

Project Description: Drones are becoming increasingly accessible to the public. However, they are banned in many places around the world due to safety and privacy considerations.

This project explores the possibility of using autonomous drones with computer vision navigation to deter illegally operated drones from sensitive airspace. One example is to deploy a large canvas directly in front of a photography drone to stop it from taking videos and pictures.

The student working on the project is expected to investigate suitable motion planning algorithms to guide the chaser drone towards an evading drone.

The goal of the project is to achieve a proof-of-concept flight demonstration of the pursuit and evasion indoors. A ground-based camera network system was developed in a related project to detect and localise small flying objects. The student will have access to the camera system to conduct the final demonstration.

Requirement to be on campus: Yes *dependent on government’s health advice..

Supervisor: Dr. Xiaofeng Wu

Eligibility: must be enrolled in Space Engineering stream. WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description: The university is developing novel technologies for in-orbit servicing, assembly and manufacturing (ISAM). ISAM is an emerging business in space industry that has attracted significant investment from both government and private sector internationally.

This project will develop autonomous capabilities for robotic close proximity operations. While in autonomous operation mode, this project will enhance the safety and reliability of robotics operations in orbit with on-board autonomy. It considers sensor failures by a fault-tolerant fusion of state-based and AI-based attitude determination algorithm. It also provides a safe reinforcement learning-based strategy for reactionless robotic operations.

The successful candidate is responsible for the dynamics and control of the simulator. The candidate will be collaborating with the chief investigators, postdocs and PhD students. We will work together to develop the technologies and spacecraft subsystems that are much needed by the Australian space industry to build the Australia in the ISAM business.

Requirement to be on campus: Yes *dependent on government’s health advice.