PhD Studentship: Multi-Fidelity Systems-Level Simulations for Nuclear Thermal Hydraulics

This project is co-funded by Rolls-Royce Submarines. Due to the need for security clearance, this project is open to British nationals only.  The project is 3.5 years long and tuition fees will be paid . The successful candidate will receive a tax free stipend of at least £19,237 per annum.

This PhD will develop a multi-fidelity model of a thermal hydraulics problem, combining 2D/3D CFD (only where needed), and 1D models for sub-systems. Bypassing some of the challenges in defining appropriate boundary conditions associated with a traditional boundary-based coupling, we will investigate a novel hybrid 1D/3D approach. The entire domain will be discretised with both CFD and systems code meshes, with one driving the other via source terms. The systems code will drive the CFD via sources where the CFD mesh is (deliberately) coarse to enforce consistency between the two overlapping domains. However, in regions of the loop pipework where the system code would be expected to perform poorly (regions where thermal stratification or strong 3D effects might be expected) the CFD mesh can be refined, with the resolved CFD then driving the systems code via sources in the systems code governing equations to enforce consistency with the resolved CFD (averaged to the systems code resolution). The identification of regions where the CFD grid is resolved can initially be done manually. With machine learning, the potential to automate the identification of problematic regions (for system-level code) can be considered. A prototype of this volumetric coupling has been developed within the team. The PhD student will take this idea from a prototype to a mature and thoroughly tested method.

We are also interested in expanding our multi-fidelity capability into parametric space. In this case, machine learning is employed to map the solution from low- fidelity level (1D) to a higher-fidelity (2D/3D CFD). If time allows and depending on the interests and skills of the PhD applicant, a portion of the project could be devoted towards further development of this parametric mapping.

This project is co-funded by Rolls-Royce Submarines. The PhD student will work closely with Rolls-Royce staff to maximise the industrial relevance of the work, including via a short-term secondment in Derby.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Due to the need for security clearance, this project is open to British nationals only. 

• Strong programming skills in any language (ideally C++ and/or Python).
• Excellence in Fluid Mechanics, Heat Transfer and Thermodynamics.
• Ideally, some experience in machine learning.
• Strong written and verbal communication skills.

Please contact Dr Alex Skillen before you apply: alex.skillen@manchester.ac.uk

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