Control of trapped electron mode turbulence with an electron cyclotron resonant source
The performance of a tokamak plasma largely depends on to the level of turbulent transport. Trapped electron modes are one of the main instabilities responsible for turbulence in tokamaks. On the other hand, electron cyclotron resonance heating is a generic heating system for tokamaks. Both physical processes rely on resonant interactions with electrons. Non-linear interaction between the resonant processes is theoretically possible. This thesis aims to evaluate the possibility of exploiting this non-linear interaction to stabilize the trapped electron modes instability within tokamak plasmas, using a heating source present on many tokamaks, including ITER. This control technique could improve the performance of certain tokamaks without any extra cost.
The thesis will be based on a theoretical understanding of the two processes studied, will require the use of the gyrokinetic code GYSELA to model the non-linear interactions between resonant processes, and will include an experimental aspect to validate the identified turbulence control mechanism.
Magnetic fusion turbulence: where do reduced models fail, how to enrich them?
One of the key challenges facing the field of fusion plasma modeling is the nonlinear nature of the plasma response. This means that factors such as temperature and density gradients, flows, and velocity gradients all have an impact on the transport of heat, particles, and momentum in complex ways. Modeling such a system requires a range of approaches, from the highly detailed flux-driven gyrokinetics method to simpler quasilinear models within an integrated framework. These have proven effective in interpreting experimental data and predicting plasma behaviour. However, there are two significant challenges to this approach. Firstly, modeling the peripheral region of the plasma edge, at the transition between open and closed field lines, is challenging due to the confluence of significantly different underlying physics. Recent research indicates that current quasilinear transport models may have significant shortcomings in this region. Secondly, modeling the 'near marginality' regime is challenging due to the fact that it involves a state of dynamic equilibrium where the system's behaviour is self-regulated by slow, large-scale modes. Computing this state is challenging and requires a flux-driven gyrokinetic approach to move away from the typical assumption of time scale separation between turbulence and transport. Recent work from within our team indicates that current quasilinear transport models may also be facing significant shortcomings in this regime. It is crucial to understand this regime in depth as it is relevant for future machine operation. We are now in a position to address these two issues, as we have access to cutting-edge in-house tools relevant to both ends of the spectrum.
We plan to compare transport predictions in the edge and near marginality regimes from the advanced flux-driven gyrokinetic code GYSELA with those from the integrated framework using the reduced quasilinear QuaLiKiz model. The research will contribute to the development of robust reduced models for transport, crucial for the interpretation of current experimental data and for future burning plasma operation.
Fracture dynamics in crystalline layer transfer technology
Smart Cut™ is a technology discovered at CEA and now industrially used for the manufacture of advanced substrates for electronics. However, the physical phenomena involved are still the focus of numerous studies at CEA. In Smart Cut™, a thin material layer is transferred from one wafer to another using a key fracture annealing step upon which a macroscopic fracture initiate & propagates at several km/s [i].
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Improving technology requires a solid understanding of the physical phenomena involved in the fracture step. The aim of this PhD project is thus to address the mechanisms involved in fracture initiation, propagation and post-fracture vibrations
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On the CEA-Grenoble site, with industrial interest, the student will use and further develop existing experimental setups to investigate the fracture behavior in brittle materials, including optical laser reflections [iv], time-resolved synchrotron diffracting imaging [iii], and ultra-fast direct imaging [ii].
In addition, python-based data analysis algorithms will be developed to extract quantitative information from the different datasets. This will enable the student to determine involved mechanisms and evaluate the influence of the wafer processing parameters on the fracture behavior, and thus propose improvement methods.
References :
[i] https://pubs.aip.org/aip/apl/article/107/9/092102/594044
[ii] https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.15.024068
[ii] https://journals.iucr.org/j/issues/2022/04/00/vb5040/index.html
[iv] https://pubs.aip.org/aip/jap/article/129/18/185103/158396
Building a new effective nuclear interaction model and propagating statistical errors
At the very heart of any « many-body » method used to describe the fundamental properties of an atomic nucleus, we find the effective nucleon-nucleon interaction. Such an interaction should be capable of taking into account the nuclear medium effects. In order to obtain it, one has to use a specific fitting protocol that takes into account a variety of nuclear observables such as radii, masses, the centroids of the giant resonances or the properties of the nuclear equation of state around the saturation density.
A well-known model of the strong interaction is the Gogny model. It is a linear combination of coupling constants and operators, plus a radial form factor of the Gaussian type [1]. The coupling constants are determined via a fitting protocol that typically uses the properties of spherical nuclei such as 40-48Ca, 56Ni, 120Sn and 208Pb.
The primary goal of this thesis is to develop a consistent fitting protocol for a generic Gogny interaction in order to access some basic statistical information, such as the covariance matrix and the uncertainties on the coupling constants, in order to be able to perform a full statistical error propagation on some selected nuclear observables calculated with such an interaction [2].
After having analysed the relations between the model parameters and identified their relative importance on how well observables are reproduced, the PhD candidate will explore the possibility of modifying some terms of the interaction itself such as the inclusion of a real three-body term or beyond mean-field effects.
The PhD candidate will work within a nuclear physics group at CEA/IRESNE Cadarache. The work will be done in close collaboration with CEA/DIF. Employment perspectives are in academic research and nuclear R&D labs.
[1] D. Davesne et al. "Infinite matter properties and zero-range limit of non-relativistic finite-range interactions." Annals of Physics 375 (2016): 288-312.
[2] T. Haverinen and M. Kortelainen. "Uncertainty propagation within the UNEDF models." Journal of Physics G: Nuclear and Particle Physics 44.4 (2017): 044008.
Mapping the tower of nuclear Effective Field Theory
The ability of nuclear models to accurately predict the rich phenomenology emerging in nuclei (whether for fundamental purposes or nuclear data applications) is conditioned by the possibility to construct a systematically improvable theoretical framework, i.e. with controlled approximations and estimation of associated uncertainties and biases. This is the goal of so called ab initio methods, which rely on two steps:
1 - The construction of an inter-nucleon interaction in adequation with the underlying theory (quantum chromodynamics) and adjusted in small systems, following effective field theory paradigm.
2 - The resolution of nuclear many-body problem to a given accuracy (for structure or reactions observables). This provides predictions in all nuclei of interest and includes the uncertainty propagation stemming from the interaction model up to nuclear data predictions.
This PhD thesis mostly deals with Step 1. The goal of the thesis is to construct a family of ab initio interactions by developing a new adjustment procedure of the low energy constants (including the evaluation of covariances for sensitivity analysis). The adjustment will include structure data but also reaction observables in light systems. This will open the door to a new evaluation of p+n->d+gamma cross sections (which have large uncertainties despite their importance for neutronics applications) in the context of state-of-the-art effective fields theories.
The thesis will be done in collaboration between CEA/IRESNE (Cadarache) and IJCLab (Orsay), the PhD student will spend 18 months in each laboratories. Professional perspectives are academic research and R&D labs in nuclear physics.
Modelling spin shuttling in Si and Ge spin qubits
Silicon and Germanium spin qubits have made outstanding progress in the past few years. In these devices, the elementary information is stored as a coherent superposition of the spin states of an electron or hole confined in a quantum dot embedded in a Si/SiO2 or SiGe heterostructure. These spins can be manipulated electrically and are entangled through exchange interactions, allowing for a variety of one- and two-qubit gates required for quantum computing and simulation. Grenoble is promoting original spin qubit platforms based on Si and Ge, and holds various records in spin lifetimes and spin-photon interactions. At CEA/IRIG, we support the progress of these quantum technologies with state-of-the-art modelling. We are, in particular, developing the TB_Sim code, able to describe very realistic qubit structures down to the atomic scale if needed.
Spin shuttling has emerged recently as a resource for spin manipulation and transport. A carrier and its spin can indeed be moved (shuttled) coherently between quantum dots, allowing for the transport of quantum information on long ranges and for the coupling between distant spins. The shuttling dynamics is however complex owing to the spin-orbit interactions that couple the motion of the carrier to its spin. This calls for a comprehensive understanding of these interactions and of their effects on the evolution and coherence of the spin. The aim of this PhD is to model shuttling between Si/Ge spin qubits using a combination of analytical and numerical (TB_Sim) techniques. The project will address spin manipulation, transport and entanglement in arrays of spin qubits, as well as the response to noise and disorder (decoherence). The PhD candidate will have the opportunity to interact with a lively community of experimentalists working on spin qubits at CEA and CNRS.
Near-threshold phenomena in nuclear structure and reactions
It is proposed to study the salient effects of coupling between discrete and continuous states near various particle emission thresholds using the shell model in the complex energy plane. This model provides the unitary formulation of a standard shell model within the framework of the open quantum system for the description of well bound, weakly bound and unbound nuclear states.
Recent studies have demonstrated the importance of the residual correlation energy of coupling to the states of the continuum for the understanding of eigenstates, their energy and decay modes, in the vicinity of the reaction channels. This residual energy has not yet been studied in detail. The studies of this thesis will deepen our understanding of the structural effects induced by coupling to the continuum and will provide support for experimental studies at GANIL and elsewhere.
Conceptual lessons of indefinite causality
Recent developments have recognized that quantum causal structures introduce a new non-classical resource known as causal indefiniteness, opening up novel perspectives in quantum information. Despite theoretical advancements and several experimental realizations, the conceptual implications of indefinite causality remain poorly understood. Concurrently, quantum causality has emerged as a crucial foundation for elucidating the discrepancies between operational approaches and spacetime physics. It has already facilitated a novel or enhanced understanding of fundamental concepts such as events (Vilasini and Renner, Phys. Rev. Lett. 133, 080201), facts (Brukner, Nature Phys. 16, 1172–1174, 2020), inputs/outputs (Chiribella and Liu, Comm. Phys. 5, 190, 2022), systems (Grinbaum, Stud. Hist. Phil. Mod. Phys. 58, 22-30, 2017), and computation (Araujo et al., Phys. Rev. A 96, 052315, 2017).
In this PhD project, the candidate will develop a systematic understanding of the conceptual lessons of indefinite causality within the classical, quantum, and generalized probabilistic theory (GPT) frameworks. They will examine the foundational significance of bipartite and multipartite settings, including their spatiotemporal and computational capacities. To make significant progress in quantum foundations, the candidate will seek to extract insights from indefinite causality to deepen our understanding of standard quantum theory, quantum information, and quantum interpretations.
Specific research questions include:
• Establishing conceptual grounds for the identification of systems and events across time, particularly in relation to indefinite causal orders and to "Wigner's friend" scenarios.
• Placing this emerging foundational discussion within a broader philosophical and metaphysical framework.
• Addressing the notion of the agent/observer as a theoretical rather than a metatheoretical entity.
Publications are expected in physics journals (PRL, PRA, NJP, Quantum) and/or philosophy of physics journals (Philosophy of physics, BJPS, Found. Phys., SHPMP). Collaborations are expected with groups in France, Austria, Belgium, and Canada.
Can we predict the weather or the climate?
According to everyone's experience, predicting the weather reliably for more than a few days seems an impossible task for our best weather agencies. Yet, we all know of examples of “weather sayings” that allow wise old persons to predict tomorrow’s weather without solving the equations of motion, and sometimes better than the official forecast. On a longer scale, climate model have been able to predict the variation of mean Earth temperature due to CO2 emission over a period of 50 year rather accurately.
In the late 50’ and 60’s, Lewis Fry Richardson, then Edward Lorenz set up the basis on the resolution of this puzzle, using observations, phenomenological arguments and low order models.
Present progress in mathematics, physics of turbulence, and observational data now allow to go beyond intuition, and test the validity of the butterfly effect in the atmosphere and climate. For this, we will use new theoretical and mathematical tools and new numerical simulations based on projection of equations of motion onto an exponential grid allowing to achieve realistic/geophysical values of parameters, at a moderate computational and storage cost.
The goal of this PhD is to implement the new tools on real observations of weather maps, to try and detect the butterfly effect on real data. On a longer time scale,, the goal will be to investigate the “statistical universality” hypothesis, to understand if and how the butterfly effect leads to universal statistics that can be used for climate predictions, and whether we can hope to build new “weather sayings” using machine learning, allowing to predict climate or weather without solving the equations.