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

Farcaș, Ionuț-Gabriel; Siena, A. Di; Jenko, F.

doi:10.1088/1741-4326/abecc8

Cited by 6 publications

(4 citation statements)

References 29 publications

(67 reference statements)

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“…Moreover, ascertaining the impact of variations in parameters that characterize the confining magnetic field is crucial as well (e.g., for design optimization). We note that the current sparse grid framework has been employed for UQ and SA in linear simulations in fusion research (linear in the five phase-space variables that characterize the underlying gyrokinetic model) in previous efforts 13 , 32 . Linear simulations are often used to gain valuable insights regarding some general trends or parameter dependencies.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, it intrinsically provided an accurate interpolation-based surrogate model of the parameters-to-output of interest mapping that was nine orders of magnitude cheaper than the high-fidelity model. Note that in the context of the simpler linear gyrokinetic simulations concerning turbulence suppression by energetic particles, recent research efforts have shown that the sensitivity-driven approach can be effectively used as a surrogate model for optimization 32 or as a low-fidelity model in a multi-fidelity study 36 .…”

Section: Discussionmentioning

confidence: 99%

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A general framework for quantifying uncertainty at scale

2022

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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. Here, we demonstrate the efficiency of this adaptive 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 this refinement method intrinsically provides an accurate surrogate model that is nine orders of magnitude cheaper than the high-fidelity model.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A general framework for quantifying uncertainty at scale

2022

Self Cite

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“…This reduced model can be further used, for example, for optimization purposes or as a low-fidelity model in a subsequent multi-fidelity UQ study. Note note that in the context of the simpler linear gyrokinetic simulations concerning turbulence suppression by energetic particles, we have recently showed that the sensitivity-driven approach can be effectively used as surrogate model for optimization [12] or as a low-fidelity model in a multi-fidelity study [22].…”

Section: Discussionmentioning

confidence: 99%

A general framework for quantifying uncertainty at scale and its application to fusion research

Farcaș¹,

Merlo²,

Jenko³

2022

Preprint

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In many fields of science, remarkably 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. As it turns out, 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 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 reduced model that is nine orders of magnitude cheaper than the high-fidelity model. Our approach enables studies previously considered infeasible.

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“…On the other hand, fast ions can affect plasma turbulence in turn [13][14][15]. In some experimental and numerical studies [16][17][18][19][20], the suppression of plasma turbulence has also been observed, which is linked to the presence of fast ions. Generally, the effects of fast ions can be classified as electrostatic effects and electromagnetic effects.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of ion-temperature-gradient mode by trapped fast ions

Wang

Cai

Gao

et al. 2022

Plasma Sci. Technol.

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Understanding and modelling fast-ion stabilization of ion-temperature-gradient (ITG) driven microturbulence have profound implications for designing and optimizing future fu- sion reactors. In this work, an analytic model is presented which describes the effect of fast ions on ITG mode. This model is derived from bounce-average gyro-kinetic equation for trapped fast ions and ballooning transformation for ITG mode. Besides dilution, strong wave-fast ion resonant interaction is involved in this model. Based on numerical calculations, the effects of the main physical parameters are studied. The increasing density of fast ions will strengthen the effects of fast ions. The effect of wave-particle resonance depends strong- ly on the temperature of fast ions. Furthermore, both increasing density gradient and the ratio of the temperature and density gradients can strengthen the stabilization of fast ions on ITG mode. Finally, the influence of resonance broadening of wave-particle interaction is discussed.

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

Cited by 6 publications

References 29 publications

A general framework for quantifying uncertainty at scale

A general framework for quantifying uncertainty at scale

A general framework for quantifying uncertainty at scale and its application to fusion research

Stabilization of ion-temperature-gradient mode by trapped fast ions

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