About the Project
Supervisory Team: Temistocle Grenga and Prof. Bharath Ganapathisubramani
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.
Entry requirements
A UK 2:1 honours degree, or its international equivalent.
Knowledge of the following areas is desirable:
- fluid dynamics
- turbulence
- data analysis
- python
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Engineering & the Environment (7175)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, please doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, T.Grenga@soton.ac.uk.
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.
