Marthen Gabriel
University of York
Co-hort year: 2025
I completed my undergraduate degree in Nuclear Engineering at Universitas Gadjah Mada, Indonesia, followed by an M.Eng. in Nuclear Engineering at Texas A&M University, United States of America, under the LPDP scholarship. My academic background was largely in nuclear fission, but during my undergraduate studies I took an introduction to nuclear fusion reactor course that sparked my interest in fusion. This interest deepened when I participated in the ASEAN School on Plasma and Nuclear Fusion (ASPNF 2019) in Thailand, where I was exposed to the potential of fusion energy in addressing global energy challenges.
As part of the Fusion CDT (Plasma Strand) based at the University of York, my PhD in Plasma Science and Fusion Energy is funded by the LPDP and the EPSRC Scholarship under the supervision of Professor Christopher Ridgers. My research focuses on Surrogate Models for Impurity Transport in Reactor-scale Magnetically Confined Fusion (MCF) Devices. In reactors where the necessary plasma confinement is achieved through the imposition of strong magnetic fields, large heat loads can impact the walls and must be mitigated. This can be done by pumping impurity gases into a region known as the divertor, which is specially designed to mitigate these heat loads. These gases are ionised and the ions cause an increase in radiation from the plasma, cooling it. It is essential, however, that we understand the transport of these impurities. If they reach the plasma in the core of the reactor they can harm fusion performance. Impurity transport is challenging as we must typically track the motion of the particles in turbulent fields. Current models for impurity transport in MCF devices, while accurate, are computationally intensive. These models often require substantial computational resources and time, limiting their application in real-time control scenarios and comprehensive parameter space explorations. In this project. I will generate training data using the Hermes-3 code, which will then be used to train Artificial Neural Network (ANN)-based surrogate model for inclusion in large-scale modelling codes.
This research will address the critical need for efficient impurity transport modelling in reactor-scale magnetically confined fusion devices by developing computationally efficient surrogate models, enabling broader exploration of reactor operating spaces, optimization of impurity seeding strategies, and progress toward practical fusion energy.
