PhD Studentship: Machine Learning models for subgrid scales in turbulent reacting flows

Project title: Machine Learning models for subgrid scales in turbulent reacting flows  

Supervisory Team: Temistocle Grenga, Ed Richardson

Project description

Supervised deep convolutional neural networks (CNNs) and generative adversarial networks (GANs) have gained significant attention for large eddy simulation (LES) subgrid-scale (SGS) modeling in turbulent reacting flows due to their ability to reconstruct statistically meaningful flow fields.

Despite their popularity, both approaches still present major challenges such as large amounts of high-resolution data (from direct numerical simulations or experiments) during the training and a lack of generalization capability. In addition, each method presents specific limitations. For example, CNNs lack the ability to accurately reconstruct high-frequency features for out-of-sample flows and need fully supervised training. On the other hand, GANs allow for semi-supervised and fully unsupervised training, but they are computationally more expensive, and there is still not a comprehensive understanding of the discriminator's contributions. Similarly, the role of the physics-informed loss function to improve the predictive capability is largely unknown.

The project's final outcome will be the realization of reliable predictive models able to guide the design of future thermochemical energy conversion processes for hydrogen-based and sustainable fuels (carbon-neutral fuels). Such models will be able to overcome the limitation of current turbulence-combustion models in predicting multi-regime combustion and multi-scale phenomena (e.g. intrinsic instabilities, backscattering).

The main tasks of the project are:

• Collection and production of training databases (DNS) for different flame regimes and fuels
• Optimization of network structure with respect to the computational efficiency and generalization capability
• Enhancement of physical-informed features of the network to improve generalization capability
• Investigation of the optimal training procedure
• Integration into computational fluid dynamics code
• A-posteriori validation of the models (LES)
• Enhancement of the scalability performance to Exa-scale computing

If you wish to discuss any details of the project informally, please contact Dr. Temistocle Grenga, Aerodynamics and Flight Mechanics Research Group, Email: t.grenga@soton.ac.uk, Tel: +44 (0) 2380 59 7918.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date:

Applications should be received no later than 01 August 2023 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £18,622 tax-free per annum for up to 3.5 years.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Temistocle Grenga

Applications should include:

• Research Proposal
• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@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.

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