This project develops AI-driven simulation models to predict and analyse the degradation of polymer-based acoustic materials used in underwater environments by integrating data from environmental telemetry such as water pressure, salinity, and temperature, as well as structural and mechanical stress parameters of the materials. For example, the AI models will be capable of identifying degradation trends over time in different polymer compositions and microstructures, assessing how environmental conditions accelerate material wear, and providing actionable insights for improving long-term acoustic performance and durability. These models will simulate the performance decay of sound-absorbing materials exposed to harsh underwater conditions, such as those found in deep-sea operations or coastal infrastructures, and make predictive recommendations for material selection, coating design, and deployment lifespan. The AI system will also support digital twin modelling of underwater environments, enabling real-time or scenario-based forecasting of material failure points and aiding in the design of more sustainable and resilient marine acoustic systems.
School
Mechanical and Manufacturing Engineering
Research Area
Materials science | Artificial intelligence | Marine engineering | Computational simulation
Suitable for recognition of Work Integrated Learning (industrial training)?
Yes
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
This project will be carried out within a collaborative research group that includes PhD students, Master’s students, honours thesis students, and an industry partner from the defence sector. You will work alongside researchers with expertise in materials science, mechanical engineering, and computational modelling, gaining exposure to both academic and industry-focused research environments. The partnership with the defence sector provides opportunities to contribute to real-world applications and ensures your work is informed by practical challenges and performance requirements. You will gain hands-on experience with simulation tools, data analysis, and material characterisation, with the potential for your research to support the development of advanced technologies across a range of defence-related applications.
- A machine learning-based model capable of simulating degradation behaviour of selected acoustic polymers
- Evaluation of the impact of material structure and composition on degradation and acoustic performance
- A set of recommendations for enhancing material durability for long-term underwater use
- Collaborative work with the industry partner on AI design and development related to the product
- Experience in preparing detailed lab reports and technical documentation aligned with industry standards
Kabir, I.I., Fu, Y., De Souza, N., Baena, J.C., Yuen, A.C.Y., Yang, W., Mata, J., Peng, Z. and Yeoh, G.H., 2020. PDMS/MWCNT nanocomposite films for underwater sound absorption applications. Journal of Materials Science, 55(12), pp.5048-5063.