Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling

A. Ho, JET Contributors, Tommy Ahlgren

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over similar to 10 confinement times, with a simulation boundary condition at rho(tor) = 0.85. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for n(e), T-e, T-i, and Omega(tor) within 2 sigma, but the simulation underpredicts the mid-radius Ti and overpredicts the core n(e) and T-e profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.

Original languageEnglish
Article number056007
JournalNuclear Fusion
Volume59
Issue number5
Number of pages18
ISSN0029-5515
DOIs
Publication statusPublished - May 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • gaussian processes
  • integrated modelling
  • model validation
  • tokamak
  • turbulence
  • uncertainty quantification
  • 114 Physical sciences

Cite this

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title = "Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling",
abstract = "This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over similar to 10 confinement times, with a simulation boundary condition at rho(tor) = 0.85. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for n(e), T-e, T-i, and Omega(tor) within 2 sigma, but the simulation underpredicts the mid-radius Ti and overpredicts the core n(e) and T-e profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.",
keywords = "gaussian processes, integrated modelling, model validation, tokamak, turbulence, uncertainty quantification, 114 Physical sciences",
author = "A. Ho and {JET Contributors} and Tommy Ahlgren",
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Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling. / Ho, A.; JET Contributors ; Ahlgren, Tommy.

In: Nuclear Fusion, Vol. 59, No. 5, 056007, 05.2019.

Research output: Contribution to journalArticleScientificpeer-review

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AB - This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over similar to 10 confinement times, with a simulation boundary condition at rho(tor) = 0.85. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for n(e), T-e, T-i, and Omega(tor) within 2 sigma, but the simulation underpredicts the mid-radius Ti and overpredicts the core n(e) and T-e profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.

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