Post-Doctoral position: Development of surrogate models for microtearing turbulence in tokamaks
DIFFER
Eindhoven, The Netherlands
4 dagen geleden

DIFFER (Dutch Institute for Fundamental Energy Research) is one of the Netherlands Organisation for Scientific Research (NWO) institutes and focuses on a multidisciplinary approach of energy research combining physics, chemistry, engineering, and material science.

The institute is based on two main strands, solar fuels for the conversion and storage of renewable energy and fusion energy as a clean and unlimited source of energy.

DIFFER is developing and supporting a national network on fundamental energy research and is closely collaborating with academic institutions, research institutes and industry.

Post-Doctoral position : Development of surrogate models for microtearing turbulence in tokamaks

A quasilinear MT turbulence model will be developed, with a two-components approach : tractable and accurate linear dispersion relation solutions, and the development of a saturation rule that approximates the impact of nonlinear physics in setting the turbulent fluxes.

Where necessary, multiple models will be developed for application to different physical branches of MT instabilities, covering different drive mechanisms and collision dependencies.

Starting from high-fidelity linear gyrokinetic simulations, a quasilinear model will be constructed. The saturation rule will be developed based on databases of existing nonlinear runs, as well as analytical considerations regarding the parallel magnetic potential fluctuations, velocity distributions, and cross phases that combine to set the electron magnetic flutter heat flux.

For fast tokamak simulation, a neural network regression of dispersion relation solutions will be carried out. The training set for the regression is envisaged to be built on reduced-

order MT models, supplemented by high-fidelity linear simulations. Increased weighting of the high-fidelity calculations in the regression is expected to lead to bias correction and to circumvent the loss of accuracy inherent to the reduced-order modeling.

Combining these two components is expected to lead to a novel and innovative MT transport model.

The project will be carried out in the Integrated Modeling and Transport (IMT) Group in the DIFFER fusion theme. Group leader and project supervisor is Dr.

Jonathan Citrin. Direct collaboration with the Institute for Fusion Studies at the University of Texas at Austin is envisaged within the framework of this project.

Scientific aim :

Fusion reactor performance strongly depends on the proximity of the plasma conditions to a high-performance state, maintaining distance from physics and engineering operational constraints.

Developing fast and accurate turbulence modeling within predictive tokamak simulations is key for this task, aiding with experimental interpretation, scenario optimization, and control-

oriented applications. In particular, real-time applications require reduction of computing cost by many orders of magnitude relative to high-

fidelity nonlinear gyrokinetic simulations.

Recent developments in reduced-order tokamak turbulence modeling, as well as applications of neural-network regression for surrogate modeling, are paving a pathway towards delivering such simulation capability.

However, the existing models are not accurately capturing an important class of instability, microtearing (MT) modes, which can limit electron temperature profiles, particularly in the H-mode pedestal.

Reduced-order models of MT turbulence must thus be developed to achieve efficient and accurate turbulence modeling throughout the plasma, which is the focus of this project.

The latter is embedded within a EUROfusion Theory, Simulation, Validation, and Verification (TSVV) effort on building validated predictive capability of the L-

H transition and pedestal physics.

Responsibilities and tasks :

  • Development of nonlinear saturation rules for quasilinear microtearing turbulence prediction, based on analytical and numerical considerations
  • Neural-network regression of databases of existing reduced-order models and linear high-fidelity gyrokinetic simulations
  • Qualifications :

  • We seek enthusiastic and highly talented candidates who are willing to work in an international and interdisciplinary team of theoretical and computational physicists.
  • You have (or are about to have) a Ph.D. in plasma physics or computational sciences.
  • You have good programming and analytical skills, and experience in tokamak microturbulence calculations. Direct experience with MT instability calculations is an advantage.
  • Good verbal and written communication skills (in English) are mandatory.
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