arXiv subjects

Gabriele Merlo

Publications and source records attributed to Gabriele Merlo.

12 recordsLinked to original sources

arXiv2026

iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow

We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local nonlinear gyrokinetic model, which allows us to compute gradients of any simulation output with respect to any input via automatic differentiation. We show that even if the stochastic nature of turbulence prevents the exact evaluation of gradients of nonlinear quantities of interest, they can still be successfully used to perform outer-loop tasks, such as profile predictions. This work enables the integration of gyrokinetics into automated parameter optimization, uncertainty quantification, sensitivity analysis, and AI workflows.

physics.plasm-ph↗

arXiv2025

Machine Learning for Electron-Scale Turbulence Modeling in W7-X

Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization. This work investigates machine-learning-driven reduced models for Electron Temperature Gradient (ETG) turbulence in the Wendelstein 7-X (W7-X) stellarator. We develop physics-guided scaling laws to predict the ETG heat flux at seven radial locations as functions of three key plasma parameters: the normalized electron temperature gradient ($\omega_{T_e}$), the ratio of normalized electron temperature and density gradients ($\eta_e$), and the electron-to-ion temperature ratio ($\tau$). The model coefficients are determined through regression combined with an active learning strategy. The procedure initializes the scaling laws using low-cardinality sparse-grid training data and iteratively enriches the training set by selecting maximally informative samples from an existing simulation database. The predictive performance of the models is assessed using out-of-sample datasets comprising more than $393$ points per radial location. Using the coefficients identified at the seven training radial locations, we further derive regression-based parameterizations for the scaling-law coefficients as functions of radial position. The resulting models are then evaluated at three additional radial locations not used during training, including both interpolation and moderate extrapolation cases. Overall, our reduced models demonstrate good predictive performance and achieve accuracy comparable to the original reference simulations, including in interpolation and moderate extrapolation regimes. An important finding is that a single radius-independent model cannot adequately describe ETG transport across the W7-X core, suggesting the presence of geometry-dependent physics not captured by the present formulation.

physics.plasm-ph↗

arXiv2024

Advanced surrogate model for electron-scale turbulence in tokamak pedestals

We derive an advanced surrogate model for predicting turbulent transport at the edge of tokamaks driven by electron temperature gradient (ETG) modes.Our derivation is based on a recently developed sensitivity-driven sparse grid interpolation approach for uncertainty quantification and sensitivity analysis at scale, which informs the set of parameters that define the surrogate model as a scaling law.Our model reveals that ETG-driven electron heat flux is influenced by the safety factor $q$, electron beta $\beta_e$, and normalized electron Debye length $\lambda_D$, in addition to well-established parameters such as the electron temperature and density gradients. To assess the trustworthiness of our model's predictions beyond training, we compute prediction intervals using bootstrapping. The surrogate model's predictive power is tested across a wide range of parameter values, including within-distribution testing parameters (to verify our model) as well as out-of-bounds and out-of-distribution testing (to validate the proposed model). Overall, validation efforts show that our model competes well with, or can even outperform, existing scaling laws in predicting ETG-driven transport.

physics.plasm-ph↗

arXiv2023

Transport Barriers in Magnetized Plasmas -- General Theory with Dynamical Constraints

A fundamental dynamical constraint -- that fluctuation induced charge-weighted particle flux must vanish -- can prevent instabilities from accessing the free energy in the strong gradients characteristic of Transport Barriers (TBs). Density gradients, when larger than a certain threshold, lead to a violation of the constraint and emerge as a stabilizing force. This mechanism, then, broadens the class of configurations (in magnetized plasmas) where these high confinement states can be formed and sustained. The need for velocity shear, the conventional agent for TB formation, is obviated. The most important ramifications of the constraint is to permit a charting out of the domains conducive to TB formation and hence to optimally confined fusion worthy states; the detailed investigation is conducted through new analytic methods and extensive gyrokinetic simulations.

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 Alfv\'en 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↗

arXiv2022

Zonal Flow Excitation in Electron-Scale Tokamak Turbulence

The derivation of an intermediate-scale gyrokinetic-electron theory in nonuniform tokamak plasmas [Chen H. et al 2021 Nucl. Fusion 61 066017] has shown that a Navier-Stokes type nonlinearity couples electron-temperature-gradient (ETG) modes and zonal flow (ZF) modes with wavelengths much shorter than the ion gyroradius but much longer than the electron gyroradius. This intermediate-scale ETG-ZF coupling is typically stronger than the Hasegawa-Mima type nonlinearity characteristic of the fluid approximation and is predicted to lead to relevant zonal flow generation and ETG mode regulation. Electron-scale, continuum, gyrokinetic simulation results are presented here which include both single-mode ETG and full-spectrum ETG turbulence. The zonal flow generation due to single ETG modes is investigated and the single-mode intermediate-scale results are found to be in agreement with theory. The full-spectrum results are then presented and explained qualitatively in terms of the single-mode results. It is found that the ETG-driven zonal flows regulate intermediate-scale electron heat flux transport to levels in the predicted range.

physics.plasm-ph↗

arXiv2022

A general framework for quantifying uncertainty at scale

In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful supercomputers, and therefore only a limited number of cases can be investigated explicitly. This prevents straightforward approaches to important tasks like uncertainty quantification and sensitivity analysis. This challenge can be overcome via our recently developed sensitivity-driven dimension adaptive sparse grid interpolation strategy. The method exploits, via adaptivity, the structure of the underlying model (such as lower intrinsic dimensionality and anisotropic coupling of the uncertain inputs) to enable efficient and accurate uncertainty quantification and sensitivity analysis at scale. We demonstrate the efficiency of our approach in the context of fusion research, in a realistic, computationally expensive scenario of turbulent transport in a magnetic confinement tokamak device with eight uncertain parameters, reducing the effort by at least two orders of magnitude. In addition, we show that our method intrinsically provides an accurate surrogate model that is nine orders of magnitude cheaper than the high-fidelity model.

physics.comp-ph↗

arXiv2022

Reduced models for ETG transport in the pedestal

This paper reports on the development of reduced models for electron temperature gradient (ETG) driven transport in the pedestal. Model development is enabled by a set of 61 nonlinear gyrokinetic simulations with input parameters taken from the pedestals in a broad range of experimental scenarios. The simulation data has been consolidated in a new database for gyrokinetic simulation data, the Multiscale Gyrokinetic Database (MGKDB), facilitating the analysis. The modeling approach may be considered a generalization of the standard quasilinear mixing length procedure. The parameter eta, the ratio of the density to temperature gradient scale length, emerges as the key parameter for formulating an effective saturation rule. With a single order-unity fitting coefficient, the model achieves an RMS error of 15%. A similar model for ETG particle flux is also described. We also present simple algebraic expressions for the transport informed by an algorithm for symbolic regression.

physics.plasm-ph↗

arXiv2020

Nonlinear symmetry breaking in electron temperature gradient driven turbulence

Nonlinear symmetry breaking may occur in systems with two or more states whose linear dynamics displays certain symmetries, one of which is preferred nonlinearly. We have identified a regime of electron temperature gradient (ETG) instabilities in a tokamak plasma with circular concentric flux surfaces that has its largest growth rate at a finite ballooning angle, establishing a symmetry that is nonlinearly broken to favor one sign for the ballooning angle. This is the first example of nonlinear symmetry breaking in simulations of a drift instability in the absence of externally imposed flow shear or asymmetry in the plasma column.

physics.plasm-ph↗

arXiv2020

How eigenmode self-interaction affects zonal flows and convergence of tokamak core turbulence with toroidal system size

Self-interaction is the process by which a microturbulence eigenmode that is extended along the direction parallel to the magnetic field interacts with itself non-linearly. This effect is particularly significant in gyrokinetic simulations accounting for kinetic passing electron dynamics. Self-interaction is known to generate stationary $E\times B$ zonal flow shear layers at radial locations near low order mode rational surfaces [Weikl et. al., Phys. Plasmas 25, 072305 (2018)]. We find however that it also plays a significant role in generating fluctuating zonal flows, which is critical to regulating transport throughout the radial extent. Unlike the usual picture of zonal flow drive where microturbulence eigenmodes coherently amplify the flow, the self-interaction drive of zonal flows from these eigenmodes are uncorrelated with each other. It is shown that the associated shearing rate of the fluctuating zonal flows therefore reduces as more toroidal modes are resolved in the simulation. In flux-tube simulations accounting for the full toroidal domain, such an increase in the density of toroidal modes corresponds to an increase in the system size, leading to a finite system size effect that is distinct from the well-known profile shearing effect.

physics.plasm-ph↗

arXiv2018

FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning

Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/software stacks. FanStore distributes datasets to the local storage of compute nodes, and maintains a global namespace. With the techniques of system call interception, distributed metadata management, and generic data compression, FanStore provides a POSIX-compliant interface with native hardware throughput in an efficient and scalable manner. Users do not have to make intrusive code changes to use FanStore and take advantage of the optimized I/O. Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.

cs.DC↗

arXiv2018

Bringing global gyrokinetic turbulence simulations to the transport timescale using a multiscale approach

The vast separation dividing the characteristic times of energy confinement and turbulence in the core of toroidal plasmas makes first-principles prediction on long timescales extremely challenging. Here we report the demonstration of a multiple-timescale method that enables coupling global gyrokinetic simulations with a transport solver to calculate the evolution of the self-consistent temperature profile. This method, which exhibits resiliency to the intrinsic fluctuations arising in turbulence simulations, holds potential for integrating nonlocal gyrokinetic turbulence simulations into predictive, whole-device models.

physics.plasm-ph↗