IFMIF-DONES · Fusion Research · Accelerator Engineering

Mechanical & Systems Engineer

Vacuum Systems · Accelerator Technology · Fusion Engineering · Safety · Experimental R&D

I work at the interface between physical systems, safety engineering and experimental research. My current focus is accelerator vacuum systems for fusion research, combining modelling, instrumentation, controls and experimental validation.

Explore my work View CV ORCID
VacuumSystems & dynamics
AcceleratorsInjector & beamline interfaces
SafetyAnalysis & mitigation
ControlsEPICS · Phoebus · DAQ
SimulationMolflow+ · ANSYS · Python

Current focus

My current work is focused on the engineering and safety of accelerator vacuum systems within the IFMIF-DONES project, including experimental validation of vacuum-loss accident scenarios, mitigation systems, instrumentation, control and system integration. My research is supported by the MuVacAS experimental facility, developed to reproduce and study representative vacuum accident scenarios under controlled experimental conditions.

I am also involved in an international engineering assignment in Japan, supporting accelerator injector knowledge transfer and safety-related activities.

Experimental R&D

MuVacAS

Experimental platform for vacuum-loss accident scenarios and mitigation strategies relevant to IFMIF-DONES.

 Fusion infrastructure

IFMIF-DONES

Accelerator vacuum and safety engineering for a major fusion research infrastructure.

 International engineering

LIPAc / Injector

Knowledge transfer, injector technology and accelerator safety activities in Japan.

 Digital engineering

Controls & Digital Engineering

EPICS, Phoebus, data acquisition, digital twins and advanced control research.

Engineering approach

Requirements → Design → Simulation → Manufacturing → Instrumentation → Integration → Testing → Validation

I enjoy working across the complete engineering cycle, connecting detailed technical development with system-level problems.

Research & publications

My research connects experimental vacuum engineering with modelling and advanced control. A recent publication explores autonomous pressure control in MuVacAS using deep reinforcement learning and deep-learning surrogate models.

Read the publication · View all publications · View talks & presentations

Connect

ORCID · LinkedIn · GitHub

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