ADVANCED CAD-TO-SIMULATION INTERFACE FOR UNSTRUCTURED MESH SIMULATIONS IN VERSATILE FUSION DEVICES

Abstract

The exponential escalation in global energy demand, catalyzed by the rapid industrialization of artificial intelligence, has precipitated an urgent requirement for commercial-scale fusion energy. As magnetic confinement devices transition from experimental facilities to pilot plants, a critical disparity has emerged between idealized physics modeling and the intricate engineering reality of tokamak assemblies. Traditional simplified models frequently fail to capture the complex interactions between plasma behavior and "as-built" reactor components—such as discrete tiles, cooling channels, and diagnostic ports—leading to uncertainties in heat load predictions and diagnostic validity.

This dissertation addresses this challenge by developing a modular and unified CAD-to-simulation geometric analysis pipeline. Unlike legacy workflows that rely on geometry simplification or intermediate file conversions, this framework functions as a stand-alone library that integrates high-fidelity computer-aided design (CAD) data directly into unstructured mesh simulations. The pipeline introduces optimized algorithms for rapid particle collision detection and realistic physics mapping, allowing simulations to account for engineering installation tolerances and misalignments.

The efficacy of this framework is validated through comprehensive application to KSTAR and ITER geometries. Key results include the precise characterization of non-axisymmetric physics phenomena, specifically neutral beam injection (NBI) induced fast ion losses and the modeling of amplified resonant magnetic perturbations (RMP) on realistic plasma-facing components . The analysis successfully identified localized heat peaks and tile-gap heat flux splitting patterns often missed by 2D assumptions. Furthermore, the framework facilitated the optimization of Lyman-alpha diagnostic line-of-sights (LOSs) through synthetic diagnostic generation.

By successfully mapping multi-physics results onto authentic engineering structures, this work establishes a standardized, error-resistant foundation for digital twin development. It bridges the gap between scientific analysis and engineering design, enabling proactive risk assessment, uncertainty quantification, and the future integration of spatial intelligence into fusion power plant operation.