arXiv subjects

Pablo Rodriguez-Fernandez

Publications and source records attributed to Pablo Rodriguez-Fernandez.

5 recordsLinked to original sources

arXiv2026

Core-edge integrated modeling of ARC: on the effect of impurity transport and detachment conditions

Integrated modeling of ARC H-modes has been conducted to assess the feasibility of high-performance scenarios compatible with divertor detachment. The analysis incorporates self-consistent evolution of impurity radiation and density profiles, demonstrating that fusion power levels approaching a GW can be achieved while maintaining divertor temperatures below 2 eV with Ar seeding. Sensitivity studies reveal a strong dependence of fusion power on the separatrix density, with performance spanning 750-1000 MW, and a weaker dependence on enrichment factor and pedestal density. Alternative seeding strategies using Neon have also been explored. Plasmas with Argon seeding consistently access H-mode, providing the highest fusion power and detached divertor operation, whereas Neon seeding leads to lower performance (600-850 MW) and less robust H-mode access, due to excessive core impurity accumulation. A small W impurity peaking has been found, with decreasing values at higher Zeff. Further analyses incorporate reduced momentum transport modeling, and sensitivity studies of neoclassical impurity transport, confirming the robustness of the results. Overall, these findings support the viability of high-performing H-mode operation in ARC, ensuring divertor protection, enabled through Argon and Neon impurity seeding.

physics.plasm-ph↗

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↗

arXiv2025

Impurity peaking of SPARC H-modes: a sensitivity study on physics and engineering assumptions

In this paper, an overview of the impurity transport for three H-mode plasmas in the upcoming SPARC tokamak has been provided. The simulations have been performed within the ASTRA+STRAHL framework, using FACIT and TGLF-SAT2 to predict, respectively, neoclassical and turbulent core transport, while a neural network trained on EPED simulations has been employed to calculate the pedestal height and width self-consistently. A benchmark with previous simulations at constant impurity fraction has been provided for three H-modes, spanning different plasma current and magnetic field values. For a scenario, additional simulations have been performed to account for uncertainties in the modeling assumptions. The predictions are nearly insensitive to changes in the top of pedestal W concentrations. Varying the Ar pedestal concentration has shown a small effect on the impurity peaking and nearly constant fusion gain values, due to multiple effects on pedestal pressure, main ion dilution and density peaking. The inclusion of rotation in ASTRA simulations has shown minimal impact on confinement and impurity transport predictions. An exploratory study has been provided with a first set of simulations treating D and T separately, experiencing a maximum fusion power at 55-45% DT fuel composition, and an asymmetric distribution with respect to the D concentration. All the results, including sensitivity scans of toroidal velocity and ion temperature and density gradients, highlighted that turbulent impurity transport prevails on the neoclassical component, aligning with previous ITER predictions, and suggesting that next generation devices like SPARC, operating at low collisionality, will experience low W accumulation.

physics.plasm-ph↗

arXiv2025

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models

This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as a data labeller, to construct efficient datasets for data-driven surrogate model generation. Building off of a previous proof-of-principle successfully demonstrating training set reduction on static pre-labelled datasets, using the ADEPT framework, this strategy was applied again to the plasma turbulent transport problem within tokamak fusion plasmas, specifically the QuaLiKiz quasilinear electrostatic gyrokinetic turbulent transport code. While QuaLiKiz provides relatively fast evaluations, this study specifically targeted small datasets to serve as a proxy for more expensive codes, such as CGYRO or GENE. The newly implemented algorithm uses the SNGP architecture for the classification component of the problem and the BNN-NCP architecture for the regression component, training models for all turbulent modes (ITG, TEM, ETG) and all transport fluxes ($Q_e$, $Q_i$, $\Gamma_e$, $\Gamma_i$, and $\Pi_i$) described by the general QuaLiKiz output. With 45 active learning iterations, moving from a small initial training set of $10^{2}$ to a final set of $10^{4}$, the resulting models reached a $F_1$ classification performance of ~0.8 and a $R^2$ regression performance of ~0.75 on an independent test set across all outputs. This extrapolates to reaching the same performance and efficiency as the previous ADEPT pipeline, although on a problem with 1 extra input dimension. While the improvement rate achieved in this implementation diminishes faster than expected, the overall technique is formulated with components that can be upgraded and generalized to many surrogate modeling applications beyond plasma turbulent transport predictions.

physics.plasm-ph↗

arXiv2025

Integrated Modeling of SPARC H-mode Scenarios: Exploration of the Impact of Modeling Assumptions on Predicted Performance

In this paper an extensive database of SPARC H-modes confinement predictions has been provided, to assess its variability with respect to few input assumptions. The simulations have been performed within the ASTRA framework, using the quasi-linear model TGLF SAT2, including electromagnetic effects, for the core transport, and a neural network trained on EPED simulations to predict the pedestal height and width self-consistently. The database has been developed starting from two SPARC H-mode discharges (12.2 T, i.e. Primary Reference Discharge or PRD, and 8 T, i.e. reduced field) and permuting 4 input parameters (W concentration, DT mixture concentration, temperature ratio at top of pedestal and deviation of pedestal pressure from the EPED prediction), to perform a sensitivity study. For the PRD a scan of auxiliary input power (ion cyclotron heating) has been performed up to 25MW, to keep highly radiative plasmas above the LH power threshold as predicted by Martin and Schmidtmayr power scalings. A scan of pedestal density has then been performed for both PRD and 8T databases. ptop/pEPED and Ti/Te at top of pedestal showed the biggest impact on the fusion gain. Significant variation is observed across the database, highlighting the importance of sensitivity studies. Below a certain W concentration, the 12T database shows that Q > 5 is consistently achieved for full-field H-modes with 11 MW of auxiliary power, and values of Q > 2 are assured when increasing the input power to keep the plasma in H-mode. The 8T database demonstrates that SPARC can access a Q > 1 operational window with low W concentration, making it a potentially interesting scenario for obtaining breakeven conditions.

physics.plasm-ph↗