Gyrokinetic study of turbulent transport bifurcations in tokamak plasmas: impact of plasma-neutral interactions
Turbulence and associated transport degrade the confinement of tokamak plasmas, reducing the expected performance in terms of energy gain. Experimentally, several regimes of improved confinement have been observed, notably those where turbulent transport is strongly reduced at the plasma edge. These external transport barriers lead to strong density and/or temperature gradients that maximize the energy content of the confined plasma. These spontaneous bifurcations result from the self-organization of turbulence under the forcing of different sources, of particles and heat. Their mechanism is poorly understood, not least because of the topological complexity of this outer region and the wealth of probable processes at play. These regimes represent a major opportunity for achieving the best performance in ITER plasmas. It is therefore crucial to gain a better understanding of them, so as to be able to predict their transition thresholds and, if possible, control them.
The proposed PhD thesis falls within this framework. It is based on state-of-the-art numerical modeling of fusion plasmas, the five dimensional (in phase space) gyrokinetic approach. Recent developments have made it possible to treat self-consistently both transport of matter and heat in this peripheral region. What remains to be done is to implement a source of neutral particles which, through ionization, will constitute the plasma's density forcing. We already know, thanks in particular to reduced models, that this dynamic source plays a crucial role in self-organization processes. The aim of this thesis work is to couple a reduced fluid model of neutrals to kinetically described electrons and ions, and to study their impact on turbulent transport and self-organization using high-performance computing (HPC) simulations with the GYSELA code.
Modeling and ALARA optimization of maintenance operations in fusion nuclear power plants with Artificial Intelligence and Virtual Reality techniques
In view to the development of future fusion reactors, the maintenance operations in these nuclear facilities will be a diffculty, as part of them will have to be carried out hands-on. Safety rules govern interventions in a radioactive environment. They take into account the level of effective dose received by the operator, a factor that characterizes the risk to which the operator is exposed (dose depending on ambient dose rate and time).
In the aim of optimizing this dose in line with the ALARA principle and the safety constraints associated with these installations, the prior simulation of operations in Virtual Reality is an asset in terms of design optimization and worker training. Calculating dose during these simulations would be an important contribution to discriminating between different options. The simulation methods currently used to calculate dose rates are in some cases imprecise and in others very costly in terms of simulation time.
The aim of this work is to propose a new method for dynamic dose rate estimation in reduced time (or even real time) as a function of the movements of both the activation sources of a fusion installation, the maintenance operator and the shield protecting the latter. These dynamic configurations are representative of real intervention conditions. This method will implement Artificial Intelligence techniques coupled with Neutronics methods, and should be able to be integrated into a Virtual Reality tool based on existing development environments such as Unity3D.
Wall conditioning of a long pulse, tungsten tokamak: from WEST to ITER
Research on controlled thermonuclear fusion as a new source of energy is carried out in devices called tokamaks, where matter is brought to high temperature (plasma state) and confined by magnetic fields. Interactions between the plasma and the walls of the vacuum chamber of tokamaks releases impurities, which can affect plasma performance. Different conditioning methods are used to control the surface state of the vacuum chamber, and thus impurity fluxes. These mainly use low-temperature plasmas (glow or radio-frequency discharges) in hydrogen or helium, but also deposition of thin layers of boron, because of its ability to trap by chemical affinity impurities like oxygen. With the advent of metallic plasma facing components and the extension of plasma duration in superconducting tokamaks, like ITER and WEST, operated in the Institute of Research on Magnetic Fusion (CEA Cadarache, France), innovative wall conditioning techniques to maintain optimal surface state and performances are under development. The aim of this thesis is to characterize and evaluate in WEST the relevance for ITER of different methods of boron injection, both a priori and in real time. The work will consist on the one hand to participate in experiments on WEST and to analyze experimental data (location and lifetime of boron deposits, effect on plasma performance). In order to understand the transport of boron, the candidate will work with plasma boundary numerical models (SOLEDGE, EIRENE, DIS). This work, combining experiments and simulations, will consolidate the understanding of the physics of wall conditioning in a metallic environment and predicting consequences for ITER and future fusion devices.
Anomaly Detection Machine learning Methods for Experimental Plasma Fusion Data Quality - Application to WEST Data
Fusion plasmas in tokamaks have complex non-linear dynamics. In the WEST Tokamak, of the same family as the ITER project, a large amount of heterogeneous experimental fusion data is collected. Ensuring the integrity and quality of this data in real time is essential for the stable and safe operation of the Tokamak. Continuous monitoring and validation are essential, as any disturbance or anomaly can significantly affect our ability to ensure plasma stability, control performance and even lifetime. The detection of unusual patterns or events within the collected data can provide valuable insights and help identify potentially abnormal behavior in plasma operations.
This Ph.D. research aims to study and develop anomaly detection system for WEST -- prefiguring what could be installed on ITER -- by integrate machine learning algorithms, statistical methodologies and signal processing techniques to validate various diagnostic signals in Tokamak operations, including density, interferometry, radiative power and magnetic data.
The expected outcomes are:
– The development of dedicated machine-learning algorithms capable of detecting anomalies in selected time series data from WEST Tokamak.
– The fine-tuning of an operational autonomous system able to ensure data quality in Tokamak reactors, integrated into the WEST AI platform.
– The constitution of a comprehensive database.
– The validation of a data quality framework built for the specific needs of plasma fusion research.
Turbulence in the edge plasma of tokamaks in regimes of stiff reactive coupling with neutrals
The strategy to manage the extreme heat fluxes to the wall of magnetic fusion reactors relies on the dissipation of the plasma’s energy through interaction with neutral gas present in the edge of the plasma mainly due to the recombination of the plasma in contact with solid materials. The physics at play consists in a balance between plasma transport, dominated by turbulence, and atomic and molecular reactions. The modelling of this extremely non-linear phenomenology is mandatory for the design and operational space definition of future devices like ITER. It requires the use of numerical codes treating self-consistently the related mechanisms, which has not been done to date. IRFM and AMU have co-developed such numerical tool, the SOLEDGE3X-EIRENE code package, which offers the capability to model self-consistently turbulent transport and neutral particles dynamics in 3D realistic geometry. First studies demonstrated that the inclusion of plasma-neutrals interactions in simulations significantly change the self-organization of turbulence and the resulting transport. They also highlighted several specific challenges related to the appearance of long time scales in the system. This PhD project aims at pursuing this work to extend it to regimes of tight coupling between the plasma and neutrals, which are the regimes of interest for future reactors. The work will rely on numerical simulations to be run on world-class High Performance Computers. Their outputs will be analyzed in order to understand the underlying phenomenology and compare it with experimental trends. Depending on the taste and capabilities of the successful candidate, it could also include a numerical (improvement of the code) or an experimental (dedicated experiments on the WEST tokamak or European partner devices) arm.