Speaker
Description
Thermal Surrogate Modeling for the Monitoring of Actively Cooled First Wall Components in ITER
Nathaniel Saura1, Daniel Iglesias1
1ITER Organization, 13067 Saint Paul Lez Durance Cedex France
In the context of ITER operations, numerical simulation plays a critical role in predicting the thermal behavior of plasma-facing components subject to intense radiative and nuclear loads. It generally requires heavy simulations and prevents from extensive scenario exploration. Developing surrogate models is an alternative to such approach and allow for fast solutions under different conditions. To this aim, we developed an end-to-end framework that starts from the CAD geometry of the Diagnostic First Wall (DFW) body and computes its thermal evolution given a set of input parameters, including both surface heat flux from photon radiation and volumetric nuclear heating due to neutron streaming within the DFW body. This framework embeds SALOME to automate the meshing of 2D planes of the DFW, sets the boundary elements and run a verified Finite Element solver named MKNIX* to compute the nodal temperature evolution on each 2D mesh.
In parallel, we adapted the porous media approach [1] to account for active cooling in the DFW body without explicitly meshing the complex pipe network. Instead, we compute an effective porosity based on the spatial density and the radius of the cooling channels and model the heat transfer coefficient of the coolant using its thermophysical properties along with flow parameters such as mean velocity and inlet initial temperature (considered constant at this stage). This leads to a localized thermal resistance, named porosity resistance, that extracts energy from the body. The method showed good agreement with full CFD simulations of a generic DFW. Moreover, it does not rely on manually tuned parameters, making it adaptable for other similar problems.
Future works will focus reconstructing the full 3D temperature field from a set of 2D-slice solutions. We plan to explore convolutional neural network-based interpolators for their flexibility, the possibility to penalize their training with physics laws and their fast inference. These conditions are pivotal to support thermal monitoring.
References
[1] McDermott, M et al., Nucl. Eng. and Design, 421, 113084 (2024),
*https://daniel-iglesias.github.io/mknix/
