Quantification on the Effect of Uncertainty On Impurity Migration In PISCES-A Simulated With GITR

ORAL

Abstract

The extreme heat, charged particle, and neutron flux / fluence to plasma facing materials in magnetically confined fusion devices has motivated research to understand, predict, and mitigate the associated detrimental effects. Of relevance to the ITER divertor is the helium interaction with the tungsten divertor, the resulting erosion and migration of impurities.

The linear plasma device PISCES A [1] has performed dedicated experiments for ~10^22 m-2s-1 flux, 250 eV He exposed tungsten targets to assess the net and gross erosion of tungsten and volumetric transport.

We present results of the erosion / migration / re-deposition of W during the experiment from the GITR (Global Impurity Transport) code coupled to materials response models. Specifically, the modeled and experimental W I emission spectroscopy data for the 429.4 nm wavelength and net erosion through target and collector mass difference measurements are compared. Bayesian inference is applied to the background plasma profile measurements (Te,ne) and data fitting in order to produce an uncertain input model for GITR. This allows for a bounding in the predictions of the experimental observables based on the uncertainty in the input data and models.

[1] R.P. Doerner Nucl. Fusion 52 (2012)

Presenters

  • Tim Younkin

    University of Tennessee, Oak Ridge National Lab, Oak Ridge National Lab, University of Tennessee, University of Tennessee-Knoxville

Authors

  • Tim Younkin

    University of Tennessee, Oak Ridge National Lab, Oak Ridge National Lab, University of Tennessee, University of Tennessee-Knoxville

  • Khachick Sargsyan

    Sandia National Laboratories

  • Tiernan Casey

    Univ of California - Berkeley

  • Habib Nasri Najm

    Sandia Natl Labs

  • Russ Doerner

    Univ of California - San Diego, Univ of California, San Diego

  • Daisuke Nishijima

    Univ of California - San Diego

  • D. L. Green

    Oak Ridge National Lab, Oak Ridge National Laboratory, ORNL

  • John Canik

    Oak Ridge National Lab, ORNL

  • Ane Lasa

    University of Tennessee, Oak Ridge National Lab

  • Philip C Roth

    Oak Ridge National Lab

  • D. Curreli

    Univ of Illinois - Urbana, University of Illinois at Urbana-Champaign, University of Illinois - UC

  • Jon T Drobny

    Univ of Illinois - Urbana

  • Parker Forehand

    University of Tennessee

  • Brian D. Wirth

    University of Tennessee, University of Tennessee, Knoxville, University Of Tennessee, Oak Ridge National Laboratory, Univ of Tennessee, Knoxville, University of Tennessee, Oak Ridge National Lab, University of Tennessee-Knoxville, University of Tennessee, Knoxville, Oak Ridge National Laboratory, University of Tennessee Knoxville, University of Tennessee - Knoxville, Oak Ridge National Laboratory