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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 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.
Programme 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 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.
Admission Requirements
3.2+
6.5+
92+
10 Feb 2025
3 Years
Oct
International
28,600
Scholarships
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Data and AI Powered Turbulence Modelling for Fusion Energy Cooling Flows PhD
Loughborough University, Loughborough, United Kingdom
36 monthsProgramme duration
28,600 Tuition Fee/year
10 Feb, 2025Application Deadline
Data Science and Artificial IntelligenceMain Subject Area
Programme 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 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.
Programme 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 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.
Admission Requirements
International
Scholarships
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Enhancing Human-Robot Interaction: Leveraging Artificial Intelligence for Emotional Intelligence in Robotics PhD
Unveiling Molecular Mechanisms of the Beneficial Impact of Exercise on Cancer Metabolism PhD
Using the Metabolome to Study Aging and Geroprotectors in 3D Cell Models PhD
Visual Data Mining on Proteins and Biomedicine Crystallisation PhD
Waste-derived Bioplastics for a Circular and Sustainable Food Packaging Economy PhD
Life Sciences and Medicine (6)
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD
Biomarkers of Aging PhD
Biomarkers of Aging PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Developing Prescription Guidelines for Passive Heat Therapy PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Does Socialised Real-Life Body Exposure Promote Body Image Positivity PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Energy Dissipation In Human Soft Tissue During Impacts PhD
Natural Sciences (6)
Accelerated Screening and Synthesis of New Chemotherapeutic Agents for Pancreatic Cancer PhD
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