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Jenko, Frank

Publications and source records attributed to Jenko, Frank.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

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 β e and normalized electron Debye length λ 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.

fusion plasma↗
DOE OSTI2023

On the effect of negative triangularity on ion temperature gradient turbulence in tokamaks

Considering the same magnetic equilibrium and plasma conditions as in Duff et al., we perform linear and nonlinear simulations of electrostatic ion temperature gradient turbulence investigating the role of triangularity δ. Differently from what was previously reported, we find that triangularity increases the transport level regardless of its sign, but more strongly when δ is positive. Here, for the case analyzed, we identify the shear of triangularity as the critical parameter determining the transport level, indicating that even in the local limit negative triangularity can reduce the transport efficiently, suggesting that confinement improvement can also be expected for larger devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computationally expensive. This work proposes a context-aware multi-fidelity Monte Carlo method that optimally balances the costs of training low-fidelity models with the costs of Monte Carlo sampling. It generalizes the previously developed context-aware bi-fidelity Monte Carlo method to hierarchies of multiple models and to more general types of low-fidelity models. When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets. This is in stark contrast to traditional surrogate modeling and model reduction techniques that construct low-fidelity models with the primary goal of approximating well the high-fidelity model outputs and typically ignore the context in which the learned models will be used in upstream tasks. Further, the proposed context-aware multi-fidelity Monte Carlo method applies to hierarchies of a wide range of types of low-fidelity models such as sparse-grid and deep-network models. Numerical experiments with the gyrokinetic simulation code Gene show speedups of up to two orders of magnitude compared to standard estimators when quantifying uncertainties in small-scale fluctuations in confined plasma in fusion reactors. This corresponds to a runtime reduction from 72 days to four hours on one node of the Lonestar6 supercomputer at the Texas Advanced Computing Center.

42 ENGINEERING↗
DOE OSTI2023

Zonal flow excitation in electron-scale tokamak turbulence

Abstract The derivation of an intermediate-scale gyrokinetic-electron theory in nonuniform tokamak plasmas (Chen 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 ZF 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 ZF 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 ZFs regulate intermediate-scale electron heat flux transport to levels in the predicted range.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Spatial core-edge coupling of the particle-in-cell gyrokinetic codes GEM and XGC

Two existing particle-in-cell gyrokinetic codes, GEM for the core region and XGC for the edge region, have been successfully coupled with a spatial coupling scheme at the interface in a toroidal geometry. Additionally, a mapping technique is developed for transferring data between GEM's structured and XGC's unstructured meshes. Two examples of coupled simulations are presented to demonstrate the coupling scheme. The optimization of GEM for graphics processing unit is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗