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DTSTAMP:20241120T082409Z
LOCATION:HG F 26.3
DTSTART;TZID=Europe/Stockholm:20240603T150000
DTEND;TZID=Europe/Stockholm:20240603T153000
UID:submissions.pasc-conference.org_PASC24_sess105_msa186@linklings.com
SUMMARY:Developing Turbulence Models with Mhd for Fusion Engineering
DESCRIPTION:Minisymposium\n\nKatarzyna Borowiec, Arpan Sircar, and Vittori
 o Badalassi (Oak Ridge National Laboratory)\n\nThe fusion device optimizat
 ion will require accurate reduced order models to explore design space and
  identify a conceptual design. Existing system codes used for fusion appli
 cations rely on scaling laws that do not have desired accuracy. There is a
  need for knowledge transfer from high-fidelity to low-fidelity models pro
 ducing computationally efficient and accurate reduced order representation
 . The AI/ML approaches are a perfect candidate for this task. One of the s
 pecific challenges of the blanket design in magnetic confinement fusion ar
 e MHD effects present in the coolant and/or breeder. The MHD effects have 
 significant influence on pressure drop and heat transfer both being extrem
 ely influential in system design. These effects are also difficult to mode
 l often requiring direct numerical simulations and fine mesh resolution to
  capture steep wall gradients. Fortunately, this analysis can be replaced 
 with lower-fidelity approaches such as RANS with appropriate turbulence mo
 dels that capture MHD effects on the mean flow. However, the 3D turbulence
  models for MHD flows are not available. As part of the VERTEX project, we
  have developed an AI/ML model with LES database that can capture the infl
 uence of the MHD effects on turbulent flow showcasing a successful knowled
 ge transfer from high-fidelity to low-fidelity models.\n\nDomain: Engineer
 ing, Physics, Computational Methods and Applied Mathematics\n\nSession Cha
 ir: Franklin Curtis (Oak Ridge National Laboratory)
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