About the project
This project pioneers deep learning for turbulence modeling, focusing on wall-bounded flows. By combining convolutional neural networks (CNNs), generative adversarial networks (GANs), and physics-informed methods, it aims to develop dynamic predictive models that overcome current limitations. The research supports scalable, accurate simulations of multi-scale phenomena, advancing computational design across energy, transport, and biomedical applications.
This project explores the cutting edge of artificial intelligence for turbulence modelling in turbulent flows, particularly in wall-turbulent flows. Using advanced deep neural networks, the research aims to create hybrid predictive models that combine theoretical insights with data-driven approaches that reliably simulate turbulent flows. While convolutional neural networks (CNNs) and generative adversarial networks (GANs) have shown promise in capturing complex flow structures (e.g., reverse energy cascade), they also come with challenges, including the need for extensive, high-resolution training data and limitations in generalizing to new conditions. The goal is to develop predictive hybrid models
for the computational design.
This project will push the boundaries of current methods, addressing specific limitations: CNNs’ difficulty with high-frequency flow features and GANs’ computational demands. The research will pioneer the use of physics-informed loss functions and reinforced learning in a hybrid framework, enhancing model accuracy and applicability. The ultimate goal is to contribute models that can guide the design and development of fluid systems by predicting multi-physics, multi-scale phenomena, critical to advancing technologies in any field such as sustainable energy, transport or medical.
Key project tasks include:
- creating specialized training datasets
- optimizing network architectures for efficiency
- exploiting the inclusion of physical constraints
- integrating into CFS solvers
- validating models in realistic settings
This work will also focus on scaling solutions to Exascale computing, bringing new possibilities to turbulence simulations.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
