Aeronautical engineering internships

Supervisor: Dr Morgan Li

Eligibility: Interests and experience in working with Arduino; familiar with Python

Project Description:

A long-running microplastic monitoring program in Australia samples deposits along the high-tide line and quantifies the number of microplastic particles in the collected material. This approach is intentionally simple and accessible, enabling participation by citizen scientists. However, in addition to the concentration of microplastics in the aquatic environment, several environmental factors, including wind and tidal conditions, affect the transport and accumulation of floating materials.

In this project, we aim to develop an Arduino-based data logger to monitor the wind speed and direction near the sampling site. The prototype will be calibrated against high-accuracy velocity measurement techniques in controlled laboratory settings. The data collected by the data logger will be used to assist the interpretation of microplastic load variations.

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

Supervisor: Dr Sydney Dolan

Eligibility:  Interests and experience in image processing and Python programming

Project Description:

This project explores how event cameras can estimate the rotation of spacecraft in space. When a spacecraft loses its rate sensors or reaction wheels, recovering its angular rate is critical for detumbling and restoring control. The same technology can be used to observe other space objects, helping estimate the rotation of satellites, debris, and tumbling targets. The project will involve implementing a recent event-camera motion-estimation method in Python and simulating star-field event streams under different spacecraft motions. Students will use contrast ptimization, warping events according to candidate motion until the stars form a sharp image, and then estimate the spacecraft’s spin using least-squares ptimization. Performance will be evaluated across a range of slew and tumble rates. The project provides practical experience in Python, computer vision, event-based sensing, ptimization, and spacecraft dynamics, with applications in fault recovery, detumbling, and space-object observation.

Requirement to be on campus: No

Supervisors: Dr Sydney Dolan

Eligibility: Interests and experience in Python. Prior experience spacecraft simulation or courses in control are beneficial but not required. WAM≥75

Project Description:

This internship project focuses on a tumbling known-target state estimation problem. A known object tumbles in space while two distant satellites observe it with wide-angle cameras; combining those views is a notional way to recover pose and spin. The orbital setting is what makes this solvable in principle: known observer orbits and relative orbital dynamics provide changing multi-view geometry and let estimators separate predictable orbital relative motion from the target's rigid-body rates. From far away that estimation is still quite hard, because wide FOVs give little angular detail and the two observer streams must be associated in time and geometry. The student will generate Basilisk scenarios with a known tumbling target and two far-range observers, produce synthetic wide-view timelines with ground-truth pose and rates, and use this suite to compare different estimation algorithms in the same setting. This project provides experience in spacecraft simulation, and advanced estimation techniques.

Requirement to be on campus: No

Supervisor: Dr Sydney Dolan

Eligibility: Interests and experience in Python, reinforcement learning, and spacecraft systems. WAM≥75.

Project Description:

This internship project focuses on reinforcement learning for satellite task planning. An Earth-observing satellite must choose among imaging, charging, wheel desaturation, and downlink under competing power, storage, and pointing limits. Classical planners precompute sequences that grow combinatorially and become brittle: they cannot react onboard to faults, changing requests, or opportunistic targets. RL is attractive in this setting because the trained policy is closed-loop and cheap to execute, so the spacecraft can adapt in real time while respecting safety constraints that other scheduling methodologies struggle to encode. Using Basilisk and BSK-RL, the student will train an agent in a resource-constrained simulation and compare it with rule-based scheduling. This project provides hands-on experience in spacecraft simulation, Python, and reinforcement learning, with applications to autonomous mission operations.

Requirement to be on campus: No

Supervisors: Prof Dries Verstraete, Ben Van Magill, Brock Cooper

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:

People living in regional and rural Australia face unique challenges due to their (isolated) geographical location and often have poorer health outcomes than people living in metropolitan areas. Rural and remote areas have double the number of preventable hospitalisations and two-and-a-half times more potentially avoidable deaths compared to metropolitan areas.

Drones could help improve health care services for remote Australians. However, current technology does not allow drones to cover the required distances while being sustainable and emissions free. A specialised medical drone is under development at the University of Sydney in partnership with ASAC Consultancy.

This research project aims to create an experimental database of motors, electronic speed controllers and propellers to enable the selection of the optimal propulsion system for this medical drone. Experimental data on powertrain components is extremely limited, and this project will provide critical data to extend the drone’s range.  

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

Supervisors: Dr. Zi Wang and Dr. Bingran Wang

Eligibility: 2nd and 3rd year AMME students

Project Description:

AI-enabled sensing networks are emerging as a powerful approach for intelligent, autonomous, and large-scale environmental monitoring. This project aims to develop a design optimisation framework for vision sensor networks to enable reliable and cost-effective monitoring of avian activities at wind farms. The research will optimise sensor placement, configuration, and network architecture to maximise detection and tracking performance while minimising deployment and operational costs. The framework will integrate computer vision models with numerical optimisation methods to account for environmental conditions, sensor performance, and spatial coverage. The resulting methodology will support the systematic design of scalable sensor networks, providing wind farm operators with improved data on bird movements and interactions with wind turbines to support wildlife protection and sustainable wind energy development.

Requirement to be on campus: No

Supervisor: Dr. Bingran Wang

Eligibility: 2nd and 3rd year AMME students

Project Description:

AI-enabled engineering design provides new opportunities to rapidly explore complex design spaces by integrating surrogate models with numerical optimisation. This project will develop an automated multidisciplinary design optimisation framework for a blended-wing-body aircraft, considering coupled aerodynamic and structural design. The student will investigate optimisation and machine-learning approaches to simultaneously determine aircraft planform and internal structural parameters while satisfying aerodynamic performance, structural stress, payload-volume, and fuel-volume requirements. The project will use existing aerodynamic surrogate models and structural datasets to enable computationally efficient design exploration. Different optimisation strategies will be investigated and compared in terms of design quality, computational efficiency, robustness, and constraint satisfaction.

Requirement to be on campus: No

Supervisor: Pawel Przytarski

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:

Micro- and mini-gas turbines are promising powerplants for UAVs and hybrid-electric propulsion, but selecting the appropriate turbomachinery architecture remains challenging during preliminary design. For a given engine duty, it is not clear whether axial, mixed-flow, or radial compressor and turbine configurations offer the best balance of aerodynamic performance and compactness.

This 8-week project will investigate whether a system-level design map, analogous to classical turbomachinery charts such as the Smith Chart and Cordier diagram, can provide a simple framework for comparing these architectures at micro-engine scale. Building on recent advances in identifying optimum compressor and turbine topologies at the component level, a low-order model will be developed to relate engine duty and non-dimensional flow and work requirements to preferred turbomachinery architectures.

A small set of representative designs will then be evaluated using 3D RANS simulations to assess the low-order predictions and identify key differences between architectures. The outcome will be a preliminary design chart and methodology to support turbomachinery architecture selection during early-stage micro-gas-turbine design.

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

Supervisor: Pawel Przytarski

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:

This project will investigate the aerodynamic behaviour of delta wings at high angles of attack and sideslip, with particular focus on the complex vortical flows generated by the highly swept leading edges. The objective is to assess the ability of RANS-based CFD methods to predict the development, interaction, and breakdown of these vortices and their influence on aerodynamic loads.

The student will review existing experimental and computational studies, identify suitable validation datasets, and reproduce selected cases using SU2. Simulations will be performed across representative combinations of angle of attack and sideslip to examine changes in vortex structure, flow separation, and aerodynamic forces and moments.

Different turbulence-modelling and numerical approaches will be considered to identify their influence on the predicted flow physics. The results will be compared with published experimental data to assess the accuracy and limitations of current CFD approaches and to identify key factors governing the prediction of highly vortical delta-wing flows.

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

Supervisor: Pawel Przytarski

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:

Rocket engines, high-power electronics and fusion reactor walls remove extreme heat loads through narrow cooling channels in which the coolant boils. Boiling carries away enormous heat via latent heat transport but predicting where it begins and how it alters wall temperature and pressure drop remains difficult, so designers work with conservative margins.

This project develops a reduced-order model of a heated cooling channel that treats the boiling coolant as a liquid–vapour mixture in thermodynamic equilibrium. The student will implement the model in Python, drawing fluid properties from open thermophysical libraries, and use it to predict the onset of boiling, vapour quality, wall temperature and pressure drop along the channel. The model will then be extended to realistic geometries such as rectangular channels of varying cross-section with heat input from one side only. These will be then benchmarked against established correlations and published experimental data.

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

Supervisor: Dr Sydney Dolan

Eligibility: Interests and experience with CAD and programming, pcb design is a plus.

Project Description:

This internship project focuses on improving the design of an educational rover used for classroom instruction. An initial rovr has been developed, but a second iteration is needed to address areas where the current design and housing are cumbersome, as well as replace the ad-hoc wiring with a more integrated PCB solution. The project will also involve testing the rover in classroom environments to get initial feedback on its usability and suitability as an educational platform. This work is being undertaken in partnership with an educational organisation in Melbourne that works with technology schools to introduce students to foundational concepts in programming, space sciences, math, and physics. The re-designed rover will ultimately provide a more robust platform to engage students with through practical experimentation.

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