Securing Our Future

A Towing Tank facilitates testing of small-scale physical models of ships and submarines to determine hydrodynamic performance and is widely used in Naval Architecture and Ocean Engineering. This project has generated a solution to transform the University of Adelaide's 30m Hydraulics Laboratory Flume into a Towing Tank. This towing tank has been designed for educational and industry purposes and boasts the ability for quick prototyping and unique local applications. The naval skills acquired through practical learning using a towing tank would help meet the increasing demand for naval skills in South Australia, with the  7,000-job shipbuilding programs for AUKUS submarines and Hunter Class Frigates. Additionally, this towing tank can support reducing global carbon emissions by providing the means to improve ship efficiency. Our design incorporates learnings from international partners at the University College London and international leaders in Hydrodynamic testing from across the United States, United Kingdom and Australia.

Project by:

• Lucy Collier   
• Gleb Lebedev
• Emily Lewis 
• Mia Mathew-Klose 
• Cara McGlaughlin 
• Samuel Moseley  

In the digital age, protecting our information from cyber threats is crucial. My project aims to monitor the flow of data between mobile devices to predict what kind of apps are being used, which can help identify unusual or potentially dangerous activity.

To achieve this, I collect and analyse network traffic data that is sent form a group of mobile devices housed in a Testbed. Using Python, I generate detailed statistics about this traffic and train a machine learning model to recognise patterns. This model can then predict the types of applications being used based solely on the flow of data.

The results are displayed on a dashboard, which provides an up-to-date view of network activity. The main goal is to give cybersecurity experts a powerful tool to spot potential threats quickly, helping to keep our digital environments safe and secure.

Project by:

• Ivan Tranquilan 

In many aerospace applications, aircraft components are subject to extremely high sound pressure levels generated by engines or rockets. In these environments, it is vital that structural integrity is maintained and tested prior to use. Currently within the National Space Qualification Network, there are no testing facilities that simulate the intense acoustic environment to which avionics are subject. High pressure sound sources must be used to achieve sounds equivalent to those experienced in aerospace applications. Commercial products exist, however they are very expensive so the aim is to design and manufacture an alternative capable of similar performance using two innovative configurations. Both designs 'chop' high pressure airflow by variably aligning slots and therefore modulate the airflow to produce a very high sound pressure level in the form of white noise.

Project by:

• Joshua Antony
• Joseph Brown
• Uri Hauben 
• Mitchell Keegan
• Mitchell O'Hara 

With environmental sustainability becoming one of the aerospace industry's central focuses, many new technologies have been proposed over the past decades that promise to optimise fuel consumption and reduce flight emissions. This project aims to explore one such technology, namely by investigating the implementation of morphing twist wings in unmanned aerial vehicles and their ability to optimise their twist characteristics for different flight stages. This year's team have focused on optimising the trade-off between twist compliance and aerodynamic efficiency, by developing a novel wing structure capable of providing high flexibility within the plane of the wing while maintaining high rigidity in the out-of-plane direction. To validate the new design, physical testing was conducted in a wind tunnel, whereby links between wing twist angle, lift, drag, and pitching moment confirmed that morphing wing twist can serve as an effective control method by which to adjust aerodynamics and stability for different conditions.

Project by:

• Quoc Minh Vu 
• Mihai Curteanu 

Since the dawn of aviation, engineers have always aimed to fly at astonishing speeds and heights, but currently, only a few aircraft can reach hypersonic speeds faster than Mach 5. Our project aims to improve the design of a manned hypersonic aircraft to achieve these extreme speeds more effectively. During the period of the project, the shape of the aircraft, including the wings, tail and body will be focused on. This is to ensure that it will perform well at high speeds.  

To achieve this, an advanced fluid flow simulation software- Ansys will be used as this software will help us analyze and test the aircraft's aerodynamics virtually. By reviewing existing technology and continuously refining the design, the goal is to improve the aircraft's performance and make the hypersonic flight more feasible and efficient.

Project by:

• Bou Zen Khoo 
• Ka Yiu Tse 
• Wei Chuen Ng 

Ever wanted to play a game of catch but have no friends? Or play over an unreasonable distance? Well now you can with the BlueBird-58-MK3!

The aim of this project is to design and build a toy aircraft capable of automated flight and perched landings. With this, the aircraft can be thrown and automatically flown between players. Autonomous flight even allows the toy to return to the original launcher. Aggressive landing techniques, like that of perching manoeuvres done by birds, allows for safe hand capture. Our project has successfully used engineering methods to produce a flying-wing micro-aerial vehicle (MAV) suitable for the set mission profiles. The combination of careful component selection to accommodate strict weight and cost restrictions enabled us to produce a MAV that is commercially viable and can be flown unlicensed. Crucially, this technology has greater applications within other industries such as defence, agriculture and mining.

Project by:

• Paul Wentrock
• Daniel Trimboli
• Alexander Magarey
• Aidan Rowett

The project "Simulating Phone Data for Education and Training" addresses the need for practical training tools in forensic science, particularly for law enforcement and academic settings. The aim is to develop a comprehensive training dataset that replicates real-world mobile forensic investigations. The methodology involves creating detailed scenarios that mirror complex forensic analyses, including diverse devices, operating systems, and user behaviors. Data is extracted from mobile devices, edited, and anonymized to ensure privacy while retaining educational value.

The project uses advanced forensic tools to construct realistic training environments that enhance participants' skills in handling and analyzing digital evidence within legal and ethical boundaries. Key outcomes include improved decision-making skills and technical competencies among forensic professionals. The simulations have also highlighted the significance of ethical considerations in forensic investigations. Future expansions will introduce more complex scenarios to continually challenge and prepare learners for real-world digital investigations.

Project by:

• Muhammad Fitri Kamarudin 
• Muhammad Afiq Mohamed Nasir 

Acoustic and vibration control plays a crucial role in signature management of naval vessels to ensure they maintain optimal stealth. As the Royal Australian Navy expands its fleet, the need for specialised signatures engineers with foundational knowledge of acoustics and vibration increases. Raising interest in the field of acoustics and vibration is therefore crucial and this project's rig will strive to capture the interest of students of all ages. The demonstration rig illustrates the effect of using soft mounting between a vibration source and the rest of a structure; in this case a metal plate representing a hull of a ship sitting in a volume of water. The soft mounting decreases the noise levels experienced by the hull as well as in the water, showing that they are an effective way of controlling vibrations and signatures. Users will be encouraged to feel and hear the difference in noise levels.

Project by:

• Jordan Pett 

As manufacturing demands increase, the amount of available resources is finite and dwindling, so alternative manufacturing processes are required to minimize wasting resources. Laser Powder Bed Fusion (LPBF) is one such method which can create stronger materials with the addition of heat treatment. The goals of the project were to understand the effect of heat treatment on the various mechanical properties vital to manufacturing, including strength and durability. By printing and testing various samples, this data can be compared to previous data and provide valuable information moving forward. Through testing, heat treatment has shown to improve the strength of the materials while altering the ductility, making the Stainless Steel less likely to stretch and the Inconel more likely to stretch before failure. Further tests are still being conducted to identify changes in the structural integrity of the 3D-printed samples.

Project by:

• Yianni Florinis 
• Michael Floridis 
• Liam Sanderson 

Inadequate flow control in industrial processes can lead to inefficiencies, such as increased energy consumption and reduced product quality. This project aims to tackle these challenges by developing practical experiments and scripts to ensure optimal flow control in complex systems.
Different flow conditions were tested using real-world scenarios, and precise instructions were developed to manage them effectively. Analyzing these results provided insights into the most effective methods for maintaining steady and efficient flow.

The findings offer valuable guidance for industries that rely on precise liquid management, such as chemical processing or wastewater treatment. By implementing these strategies, operations can be made more efficient, costs reduced, and product quality improved.

Project by:

• Hung Dung Ngo 
• Roshdi Arouri 

Imagine knowing exactly when your back pain is about to strike before it even happens. This groundbreaking research project aims to do just that. By analysing data from wearable devices like fitness trackers and sleep monitors, we're on a mission to predict when low back pain will flare up. Forget relying on guesswork or waiting until the pain hits; our study could unlock the secret to staying one step ahead of those dreaded episodes. If successful, this research could lead to personalised tips and tricks to prevent flare-ups before they ruin your day. Say goodbye to surprise back pain and hello to a smarter, pain-free future.

Project by:

• Joan Shu Ting Lim 

The development of zero-emissions aircraft is essential for achieving cleaner, quieter, and more sustainable air travel. One promising example is the Blue Spirit Aero (BSA) Dragonfly, a hydrogen-electric aircraft with a distributed propulsion system consisting of 12 identical propulsion units integrated along the wings. This project aims to optimise the aerodynamic characteristics of the Dragonfly by using computational fluid dynamics (CFD) to refine the shape of the fairings (the casings around each propulsion unit) and the positions of the slotted flaps on its wings. By conducting CFD analyses under various conditions, we were able to validate BSA's design decisions and identify configurations that enhance the wing's aerodynamic efficiency. These improvements are expected to reduce the Dragonfly's power requirements, resulting in fuel savings and increased flight range.

Project by:

• Duddlie Yu 
• Henry Rice 
• Jacob Allen 
• Jamie Angelakis 

With the growth in CO2 emissions caused by global aviation demand, solutions are needed more than ever for greater efficiencies in flight. Distributed electric propulsion (DEP) has the potential to improve key performance metrics in fixed wing aircraft by harnessing multiple propulsors located across the wing. This project involved an in-depth literature review to assist in the design and build of a DEP unmanned aircraft vehicle (UAV). Testing of the UAV can then be performed to discern the advantage of the DEP system. The final design involved six electric motors fixed to 3D printed wings with carbon fibre structural reinforcements. The design aims to take advantage of the propeller wash interacting with the wing surface to produce more lift force when compared to a conventional wing. Future flight tests of the final design will help analyse the benefits of the DEP system.

Project by:

• Edward Duggan 
• Dillon Duncan 
• Lance Mihic 
• Josh Bough 

The world is set to use more than 180 Zetabytes of data in 2025, enough to store 3 million years worth of YouTube videos. With all this data, engineers are trying to build out networks of autonomous vehicles on our roads, constellations of satellites in space, and coordinated swarms of drone aircraft in our airports and battlefields. These systems demand high speed connections that work rain, hail and shine over distances as far as Mars. Clearly plugging in an Ethernet cable is not an option, so this project is investigating laser communications. Lasers operate with more energy than the radio waves still widely used today, allowing them to transmit information 10-100 times as fast. By rapidly redirecting a signal beam, this prototype can talk with several targets positioned over extreme distances, even if spread across a wide area. This design offers another tool for building tomorrow's world.

Project by:

• Samuel Nitschke 
• Jeremiah Agag 
• Dulkith Jayasekera 

Identifying weak spots or potential damage in aircraft components before they escalate into serious problems is crucial for maintaining safety and reliability. Our project aims to improve   methods for measuring and visualising stress hot spots on aircraft components. Traditional testing methods can be limited and time-consuming, especially for complex parts with unusual shapes.

To overcome these challenges, we use Thermoelastic Stress Analysis (TSA) with advanced thermal cameras that capture heat patterns on structural components. By strategically placing multiple TSA cameras around the test structure, we can create detailed 3D maps showing where stress is highest. These maps help us identify areas that might need reinforcement or repair.

Our project has developed a methodology for testing that can be used in future stress testing of structural components. By combining multiple TSA cameras with 3D modelling, we've created an approach that paves the way for faster and more reliable aircraft inspections in the future.

Project by:

• Amelia Wood 
• Luke Ellbourne 

The aim of the project is to integrate multiple wireless communications bearers onto UAV's for the purpose of enhancing network resilience through dynamic multibearer networking. To achieve this goal, a lab based experimental testbed using a variety of wireless bearers including tactical mesh and point to point radios was developed to experimentally evaluate the performance of a software solution to the multibearer networking problem developed by DSTG. The testbed was designed to meet specific criteria defined by stakeholders, including dimensional, performance and thermal constraints. This test bed was used to simulate a mission environment, enabling a low risk analysis of the capabilities of various software solutions.

Project by:

• Denis Vasilyev 

The rapid proliferation of radio frequency (RF) devices has led to a cluttered electromagnetic (EM) spectrum, complicating the design of reliable RF systems. Understanding how new RF technologies perform in this crowded environment is crucial. To address this, our project aims to simulate complex EM environments using affordable, commercially available software-defined radios (SDRs) in a hardware-in-the-loop (HWIL) setup. This approach allows for realistic, inexpensive simulations without over-the-air radiation. In partnership with DSTG, we developed a physical configuration to generate complex EM environments and explored the systems' capabilities to simulate different scenarios. The resulting data was processed into range-Doppler maps for evaluation. Our results demonstrate a novel simulation method for generating realistic complex EM environments. This approach can be used to evaluate the capabilities of various RF devices, benefiting fields such as telecommunications and defense.

Project by:

• Allan Macdonald 
• Denver Quin 
• Jake Delyster 
• Jesse Fabian 

Aircraft with distributed electric propulsion (DEP) with vertical takeoff and landing (VTOL) provide countless possibilities for testing and achieving feats conventional fixed wing aircraft are not capable of achieving. However, several gaps are presented such as the lack of commercial VTOL aircraft and therefore an absence in available information. Gaps similar to this establish the primary motivations behind the project. This project aims to construct multiple prototype aircraft using innovative design and construction methods to investigate the effects of distributed electric propulsion, while being capable of being used as an experimental test bed, serving as the foundation for future research projects to continue testing, verifying and incorporating these unexplored technologies.

Project by:

• Austin J. Gravestocks 
• Zehao Liu 
• Kristjan J. Liivamagi 
• Dylan Burnett 
• Ruard Koekemoer 
• Sui Hin Hui 

A human-powered submarine is exactly what the name suggests. Instead of using an engine, a pilot pedals to propel the submarine through a pool in the European International Submarine Race (eISR). Engineering students from universities across the world come together in teams to design, test, and race human-powered submarines in the eISR. We want to be the first team from Australia to compete in the eISR in 2026 and our project aims to develop the power generation and propulsion systems for this race. We are adopting a sustainable approach to designing a safe, efficient, and fast submarine by testing different propeller designs and power generation configurations. The outcome of this project is to create a propulsion and power generation system for a submarine that can manoeuvre smoothly through the water with speed and efficiency.

Project by:

• Amna Sajjad  
• Harrison Philip Catley 
• Kazimierz Mikolaj Bejnarowicz 
• Pericles Skordos 
• Quinn Stewart Carmody 
• Pratishtha Vashishta 

Mobile phone records are well-documented on a global scale for criminal investigations and are ever-increasing in demand; holding vital location data and communication patterns. However, concerns on inaccurate interpretation due to the complexity and variation of records has drawn scrutiny in the courts. It is imperative for law enforcement agents and subject expert matter witnesses to have appropriate levels of understanding telecommunications engineering, dependent on their tertiary and technological background. This project in collaboration with Digital Forensic Sciences Australia focuses on applying telecommunications engineering concepts to mobile network record synthesis. The records endeavours to facilitate education within law enforcement. Graphical simulations of viable connections for mobile users and impacting surrounds are also demonstrated for analysis purposes.

Project by:

• Aleesa Harmse

Surf Life Saving SA (SLSSA) aims to advance its capabilities in the 21st century by automating data analysis for high-risk drowning factors, particularly focusing on weather conditions, beach attendance, and drowning incidents. SLSSA requires a web app that consolidates information from various related subsystems into a presentable format for decision-making by operators. In our project we built a web app that took in weather factors from the Bureau of Meteorology website, important dates like public holidays from data.gov.au and stored them in an SQL database, analysed the ongoing data with K means and provided risk ratings to the individual beach zones. We created a web app that does the above all visualised in the dashboard, with recommendations based on the risk ratings.

Project by:

• Celine Tan 
• Loay Abdelfattah 

My project aims to help computers "see" objects in images and videos, much like how humans can recognize things around them. The goal is to make this process faster and more accurate, which could help in many areas, like self-driving cars, security cameras, or helping robots understand their surroundings.

To do this, we use special programs called "models," such as YOLOv8 to YOLOv10, which are good at recognizing objects. We also improved another model, DiffusionDet, so it can work faster when figuring out what's in an image. By training these models with lots of pictures, we teach the computer to get better at recognizing things on its own.

In the end, our project helped create a faster and smarter system for detecting objects, which could make technology in different areas work more efficiently!

Project by:

• Jiyun Hao 

As cyber threats have seen a steep rise, it has become paramount to strengthen cybersecurity assessment techniques. As software systems become more complex, so do the risks they face. This study uses large language models (LLMs), such as GPT, Llama, and Gemini, to enhance vulnerability assessment. By leveraging the National Vulnerability Database (NVD) as a knowledge base, these models can access detailed information on known vulnerabilities to identify and answer questions in detail about cybersecurity vulnerabilities to its users. The goal is to explore how well these models, combined with different data embedding techniques, can answer questions about vulnerabilities. After development, the study will evaluate the models' performance using real-world systems or through user testing, determining their ability to improve vulnerability detection and help mitigate risks effectively.

Project by:

• Shivansh Chopra 

It is tedious to manually annotate when vulnerabilities are introduced into codebases, but this information is essential to understanding the root cause and history of vulnerabilities. To alleviate this manual burden, different automatic approaches have been proposed to trace the vulnerability-introducing commits, among which, SZZ algorithm is the most used one in the literature. However, this algorithm is far from perfect as it is known to generate many false positives. Such wrong predictions not only increase the inspection effort for developers, but they can also negatively affect any prediction models leveraging its output.

This project aims to investigate the impact of data automatically generated by SZZ on the performance of just-in-time software vulnerability prediction. In the first step, we will apply the state-of-the-art V-SZZ algorithm to generate vulnerability-introducing commits from the respective vulnerability-fixing commits in different software projects. After that, we will curate the developer-annotated vulnerability-introducing commits from GitHub as ground truth for the investigations. We will develop just-in-time or commit-level software vulnerability prediction models leveraging various Machine Learning and Deep Learning models. These models will be trained in two scenarios. The first will use the ground truth data, while the second will use the data auto-labeled by SZZ. We will compare the performance of the two scenarios using different evaluation measures.

Project by:

• Guanqiao Huang 

When a person uses their phone, they generate network events (texting, calling, internet use). These network events are recorded by the mobile service provider (Telstra) and includes:

• The time of the event.
• The location of the connected cell tower (base station).
• The type of event (SMS, Voice, Data).
• Information about both sender and recipient.

These records can be used to assist law enforcement in their investigations, as they provide information on the approximate location of a suspect's phone at a given time, as well as who they are communicating with. However, mobile network records can be complex and difficult to understand which can lead to investigators making incorrect interpretations. Additionally, there is an absence of resources available for understanding how to interpret mobile network records. Due to the lack of available resources, we are developing an application that can generate synthetic mobile network data that accurately depict an input scenario.

Project by:

• Adam English 
• James Hutchins