arXiv subjects

Alessandro Di Siena

Publications and source records attributed to Alessandro Di Siena.

6 recordsLinked to original sources

arXiv2026

How Fusion-Born Alpha Particles Suppress Microturbulence in Burning Plasmas

A central unresolved question in fusion energy research is whether energetic alpha particles, the primary products of deuterium-tritium fusion reactions, enhance or degrade plasma confinement. In burning plasmas, the operating regime of future devices such as ITER and SPARC, alpha particles become the dominant heating source, yet their impact on confinement has remained uncertain. Here, we present self-consistent simulations of burning plasmas that simultaneously evolve microturbulence, alpha-particle heating, and macroscopic plasma profiles to steady state, and find that alpha particles can substantially improve confinement. Fusion-born alpha particles weakly destabilize toroidal Alfven eigenmodes (TAEs), which nonlinearly enhance zonal flows that shear apart and suppress ion-scale turbulence. The resulting reduction in turbulent heat transport drives stronger core profile peaking, increasing alpha heating by up to 25% and establishing a self-reinforcing feedback loop. This mechanism has no direct analogue in present-day experiments, where external heating dominates, and reveals an intrinsic pathway toward improved confinement in burning plasmas.

physics.plasm-ph↗

arXiv2023

Reassessing the impact of megaelectronvolt ions in fusion plasmas via gyrokinetic simulations

Gyrokinetic simulations conducted by Mazzi et al. reveal the suppression of turbulence in fusion plasmas through the destabilization of Toroidal Alfvén Eigenmodes (TAEs) by megaelectronvolt ions. Our analysis demonstrates that the authors' numerical findings are strongly influenced by the selected simulation settings, calling into question their claim that the resulting heat conductivity aligns with the TRANSP power balance. Specifically, we assert that the numerical results presented by Mazzi et al. are a direct consequence of the inadequacy of resolution employed in their numerical simulations. Notably, there are three primary factors contributing to this issue: (i) the employed radial box size is insufficient, leading to an undesirable impact of the boundary conditions on the simulations; (ii) the adoption of a higher minimum toroidal mode number fails to accurately resolve the entire range of TAEs, resulting in an underestimation of TAE drive and introducing spurious effects on wave-particle resonances; (iii) an insufficient resolution in the magnetic moment direction exacerbates these challenges. Upon addressing the aforementioned numerical issues, a significant increase in heat conductivity for each plasma species was observed by more than tenfold, diverging from the expected values derived from the TRANSP power balance calculations. Consequently, our results raise serious doubts on the authors' assertion of enhanced performance in the presence of strongly unstable TAEs, emphasizing the need for a reevaluation of their claims.

physics.plasm-ph↗

arXiv2021

Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with respect to various physical parameters characterizing the background plasma and the magnetic equilibrium. Therefore, uncertainty quantification is essential for achieving predictive numerical simulations of plasma turbulence. However, the high computational costs of the required gyrokinetic simulations and the large number of parameters render standard Monte Carlo techniques intractable. To address this problem, we propose a multi-fidelity Monte Carlo approach in which we employ data-driven low-fidelity models that exploit the structure of the underlying problem such as low intrinsic dimension and anisotropic coupling of the stochastic inputs. The low-fidelity models are efficiently constructed via sensitivity-driven dimension-adaptive sparse grid interpolation using both the full set of uncertain inputs and subsets comprising only selected, important parameters. We illustrate the power of this method by applying it to two plasma turbulence problems with up to $14$ stochastic parameters, demonstrating that it is up to four orders of magnitude more efficient than standard Monte Carlo methods measured in single-core performance, which translates into a runtime reduction from around eight days to one hour on 240 cores on parallel machines.

physics.comp-ph↗

arXiv2021

Turbulence suppression by energetic particles: A sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization

A newly developed sensitivity-driven approach is employed to study the role of energetic particles in suppressing turbulence-inducing micro-instabilities for a set of realistic JET-like cases with NBI deuterium and ICRH $^3$He fast ions. First, the efficiency of the sensitivity-driven approach is showcased for scans in a $21$-dimensional parameter space, for which only $250$ simulations are necessary. The same scan performed with traditional Cartesian grids with only two points in each of the $21$ dimensions would require $2^{21} = 2,097,152$ simulations. Then, a $14$-dimensional parameter subspace is considered, using the sensitivity-driven approach to find an approximation of the parameter-to-growth rate map averaged over nine bi-normal wave-numbers, indicating pathways towards turbulence suppression. The respective turbulent fluxes, obtained via nonlinear simulations for the optimized set of parameters, are reduced by more than two order of magnitude compared to the reference results.

physics.plasm-ph↗

arXiv2020

Turbulence suppression by energetic particle effects in modern optimized stellarators

Turbulent transport is known to limit the plasma confinement of present-day optimized stellarators. To address this issue, a novel method to strongly suppress turbulence in such devices is proposed, namely the resonant wave-particle interaction of supra-thermal particles - e.g., from ion-cyclotron-resonance-frequency (ICRF) heating - with turbulence-driving microinstabilities like ion-temperature-gradient (ITG) modes. The effectiveness of this mechanism is demonstrated via analytic theory and large-scale gyrokinetic simulations, revealing an overall turbulence reduction by up to $80\%$. These results hold the promise of new and still unexplored stellarator scenarios with enhanced confinement and improved performance, essential for achieving burning plasmas in future devices.

physics.plasm-ph↗

arXiv2019

Implementation of energy transfer technique in ORB5 to study collisionless wave-particle interactions in phase-space

A new diagnostic has been developed to investigate the wave-particle interaction in the phase-space in gyrokinetic particle-in-cell codes. Based on the projection of energy transfer terms onto the velocity space, the technique has been implemented and tested in the global code ORB5 and it gives an opportunity to localise velocity domains of maximum wave-plasma energy exchange for separate species. Moreover, contribution of different species and resonances can be estimated as well, by integrating the energy transfer terms in corresponding velocity domains. This Mode-Plasma-Resonance (MPR) diagnostic has been applied to study the dynamics of the Energetic-particle-induced Geodesic Acoustic Modes (EGAMs) in an ASDEX Upgrade shot, by analysing the influence of different species on the mode time evolution. Since the equations on which the diagnostic is based, are valid in both linear and nonlinear cases, this approach can be applied to study nonlinear plasma effects. As a possible future application, the technique can be used, for instance, to investigate the nonlinear EGAM frequency chirping, or the plasma heating due to the damping of the EGAMs.

physics.plasm-ph↗