Presentation
Developing Turbulence Models with Mhd for Fusion Engineering
Presenter
DescriptionThe fusion device optimization will require accurate reduced order models to explore design space and identify a conceptual design. Existing system codes used for fusion applications rely on scaling laws that do not have desired accuracy. There is a need for knowledge transfer from high-fidelity to low-fidelity models producing computationally efficient and accurate reduced order representation. The AI/ML approaches are a perfect candidate for this task. One of the specific challenges of the blanket design in magnetic confinement fusion are MHD effects present in the coolant and/or breeder. The MHD effects have significant influence on pressure drop and heat transfer both being extremely influential in system design. These effects are also difficult to model 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 models 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 influence of the MHD effects on turbulent flow showcasing a successful knowledge transfer from high-fidelity to low-fidelity models.
TimeMonday, June 315:00 - 15:30 CEST
LocationHG F 26.3
SessionMS2G - HPC Code Development for Multi-Scale Multiphysics Simulations for Fusion Energy Design
Session Chair
Event Type
Minisymposium
Engineering
Physics
Computational Methods and Applied Mathematics