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Data and AI Powered Turbulence Modelling for Fusion Energy Cooling Flows PhD 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
Data and AI Powered Turbulence Modelling for Fusion Energy Cooling Flows PhD 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
3.2+
6.5+
92+
10 Feb 2025
3 Years
Oct
Tuition fees
International
28,600
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
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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
Data Science and Artificial IntelligenceMain Subject Area
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 fees
International
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
How to get a full scholarship
Looking for a fully-funded scholarship to see you into university? Find out how to boost your chances of getting one.
Scholarships to study abroad
Find scholarships to study abroad with our lists of international scholarships – categorized by country, by subject, and by type of student.
Scholarship Applications: Frequently Asked Questions
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Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bespoke Organometallic Compounds for Anticancer Studies PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Bio-based Sustainable Materials for Autonomous Thermal Management in Wearables PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Biomimmetic Catalytic Asymmetric Synthesis of the Bisorbicillinoids and Related Products PhD
Climate and Ecological Transitions Hub Studentship PhD
Climate and Ecological Transitions Hub Studentship PhD
Coherent State Methods in Spectral Asymptotics PhD
Coherent State Methods in Spectral Asymptotics PhD
Computational Modelling of Sustainable Alloys PhD
Designing Classroom Tasks To Improve Students’ Learning Of Mathematics PhD
Developing Novel Antibiofilm Coatings for Sustainable Biofilm-Free Surfaces PhD
Development of Hybrid Microcomb Sources with Topologically Protected Dark Resonances for Simultaneous Classical and Quantum PhD
Development of Novel Bicyclopentanes (BCP) Bioisosteres From Bench Stable [1.1.1]-Propellane Precursors PhD
Digital Twins for Biofilm Management PhD
Dispersionless Integrable Systems In 2+1 Dimensions PhD
Social Sciences and Management (6)
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
An Evaluation of Erasmus+ Sport Projects and Their Impact on EU Sport Policy Development PhD
Climate Change, Work Design and Worker Wellbeing Phd
Climate Change, Work Design and Worker Wellbeing Phd
Communication and Social Change (CSC) Hub Studentship PhD
Communication and Social Change (CSC) Hub Studentship PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Enhancing Motivation in Sport and Exercise Contexts PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD
Out of School Youth Provision: An Analysis of Funding and Operational Resilience within the Sector PhD