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Data and AI Powered Turbulence Modelling for Fusion Energy Cooling Flows PhD
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Data and AI Powered Turbulence Modelling for Fusion Energy Cooling Flows PhD
Loughborough University, Loughborough, United Kingdom
36 monthsProgram duration
28,600 Tuition Fee/year
10 Feb, 2025Application Deadline
YesScholarships
Program overview
Main Subject
Data Science and Artificial Intelligence
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Magnetohydrodynamics (MHD) is critical in many natural and engineering problems. With tackling climate change and NetZero targets among the driving factors of our economies, fusion...
Read more energy is a potential key technology.
The realisation of future fusion energy relies on effective fusion power plant design and fundamentally sound understanding of metallurgical processes, of which efficient cooling involving MHD is a key step. However, the computational demands of full-scale 3D MHD simulations are significant, particularly due to the need for fine meshing in the inner boundary layers, such as those on Hartmann and side walls, where the behaviour of electric currents must be precisely captured.
This research project aims to develop and test augmented closure models for Reynolds-Averaged Navier-Stokes and Maxwell Equations using previously obtained high fidelity simulation data and AI algorithms. In particular, the tensor and vector based neural network methods, which incorporate a physics-based layer, will be investigated.
By leveraging these methods, the research seeks to reduce the computational load associated with these meshing requirements, enabling faster and more accessible turbulence models which can predict complex flow behaviours under Lorenz and buoyancy forces accurately. Read less
Program overview
Main Subject
Data Science and Artificial Intelligence
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Magnetohydrodynamics (MHD) is critical in many natural and engineering problems. With tackling climate change and NetZero targets among the driving factors of our economies, fusion...
Read more energy is a potential key technology.
The realisation of future fusion energy relies on effective fusion power plant design and fundamentally sound understanding of metallurgical processes, of which efficient cooling involving MHD is a key step. However, the computational demands of full-scale 3D MHD simulations are significant, particularly due to the need for fine meshing in the inner boundary layers, such as those on Hartmann and side walls, where the behaviour of electric currents must be precisely captured.
This research project aims to develop and test augmented closure models for Reynolds-Averaged Navier-Stokes and Maxwell Equations using previously obtained high fidelity simulation data and AI algorithms. In particular, the tensor and vector based neural network methods, which incorporate a physics-based layer, will be investigated.
By leveraging these methods, the research seeks to reduce the computational load associated with these meshing requirements, enabling faster and more accessible turbulence models which can predict complex flow behaviours under Lorenz and buoyancy forces accurately. Read less
Admission Requirements
Tuition Fee and Scholarships
Domestic Students
International Students
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