Ion Cyclotron Resonance Frecuency Heating of Fusion Plasmas
This research lines investigates the use of electromagnetic waves in the ion cyclotron range of frequencies for plasma heating and current drive in fusion devices. It is is one of the main heating and current drive methods in the present day machines and also foreseen for ITER.
Fast Ions in Fusion Plasmas
This research line studies the production and the behaviour of fast ions in fusion plasmas. Understanding the fast ion behaviour is of key importance for the next step fusion reactor ITER where the plasma heating will be dominated by fusion-born alpha particles. Alpha particles are fast 3.5-MeV Helium-4 ions born in the fusion reactions between deuterons and tritons which form the fusion fuel.
The research efforts in the field of fast ion physics are directed towards enhancing the modeling capabilities of such fast ions in fusion devices for improved physics understanding and performance optimization. Special emphasis is given to the modeling of fast ions heated with waves in the ion cyclotron range of frequencies and their interactions with a variety of plasma instabilities.
HPC for Multiphysics Modelling of Fusion Reactors
This research line develops new HPC tools for multiphysics modelling of fusion reactors based on the state-of-the-art parallel computational mechanics code system ALYA, with the aim of providing the fusion research community with a cutting-edge computational tool for addressing complex multiphysics problems both in existing and future fusion devices. It has potential to become a driver for future fusion reactor design given its HPC capabilities which make the analysis of different design variations feasible.
The ALYA system has been developed for more than 15 years at the Department of Computer Applications in Science and Engineering (BSC-CASE), Barcelona Supercomputing Center (BSC), Spain. It has two main features. Firstly, it is designed for running with the highest efficiency standards in large scale supercomputing facilities. Secondly, it is capable of solving different physics, each one with its own model characteristics, in a coupled way. A good efficiency for more than 100,000 cores and 4.2 billion elements meshes has been demonstrated.
Fusion Materials Modelling
This research line focuses on modelling fusion materials using different atomistic approaches: classical molecular dynamics; ab-initio Density Functional Theory (DFT); and development of machine learning interatomic potentials.
The linear scaling algorithms implemented in the BigDFT code allow for analysis of much larger physical problems than have been tackled so far. The goal of this approach is to optimize and demonstrate the performance of linear scaling BigDFT for metallic systems relevant for fusion material research.
Classical molecular dynamics relies on fast, accurate interatomic potentials to scale up system sizes and timescales compared to what is computationally feasible using ab-initio methods. Highly accurate DFT calculations on systems form training data for machine learning interatomic potentials. Finally, machine learning approaches optimize between the accuracy of ab-initio methods and the speed needed for larger system sizes modelled using classical molecular dynamics methods.