Dust Transport in MAST-U
Lead Research Organisation:
University of Liverpool
Department Name: Electrical Engineering and Electronics
Abstract
The aim of this project is to experimentally study dust transport and mobilization to: i) verify the
physical models used in DTOKs in a magnetized plasma; ii) study the mobilization and transport of
dust in MAST-U with different divertor (the tokamak's exhaust) configurations. Experiments verifying
existing ion drag models and studying mobilization from surfaces will be performed at Liverpool on
the magnetized RF plasma experiment. Advanced plasma diagnostics such as RFEA, Langmuir and
emissive probes, imaging emission spectroscopy and fast visible cameras will be used. To study dust
mobilization and transport in MAST-U a recently developed modified DSF (Divertor Science Facility)
probe head will be used to inject dust with a separate head for MAST-U and ITER relevant slots
(representing typical tile gaps). Stereoscopic imaging, using fast visible and infra-red cameras and a
machine learning dust tracking algorithm, along with other diagnostics (such as Langmuir probes)
will be used to reconstruct the dust trajectory. Plasma background simulations along with DTOKs
predictions will then be used to verify existing models and study dust transport from the
reconstructed trajectories.
physical models used in DTOKs in a magnetized plasma; ii) study the mobilization and transport of
dust in MAST-U with different divertor (the tokamak's exhaust) configurations. Experiments verifying
existing ion drag models and studying mobilization from surfaces will be performed at Liverpool on
the magnetized RF plasma experiment. Advanced plasma diagnostics such as RFEA, Langmuir and
emissive probes, imaging emission spectroscopy and fast visible cameras will be used. To study dust
mobilization and transport in MAST-U a recently developed modified DSF (Divertor Science Facility)
probe head will be used to inject dust with a separate head for MAST-U and ITER relevant slots
(representing typical tile gaps). Stereoscopic imaging, using fast visible and infra-red cameras and a
machine learning dust tracking algorithm, along with other diagnostics (such as Langmuir probes)
will be used to reconstruct the dust trajectory. Plasma background simulations along with DTOKs
predictions will then be used to verify existing models and study dust transport from the
reconstructed trajectories.
Organisations
People |
ORCID iD |
Paul Bryant (Primary Supervisor) | |
Juan Pablo Broude Garcia (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S022430/1 | 01/10/2020 | 31/03/2028 | |||
2889644 | Studentship | EP/S022430/1 | 01/10/2023 | 30/09/2027 | Juan Pablo Broude Garcia |