Multiphysics and multi-scale thermal-fluid models are essential for understanding the coupling of physical phenomena, enabling the optimization of welding and additive manufacturing process parameters. However, the thermal-fluid simulations are extremely time-consuming due to the need for a fine mesh (approximately 100 \(\upmu \) m) and small time steps (around 10−5 s), which restrict the modeling domain to very small dimension. This paper proposes a multiphysics, multi-scale thermal-fluid model based on the Moving Thermal-Fluid (MTF) framework combined with Octree-based Adaptative Mesh Refinement (AMR) that can solve heat and mass transfer problem in an optimal way. The MTF framework consists of solving the thermal-fluid problem only within a small, moving region containing the melt pool, while calculating a heat transfer problem in the rest of the domain. Therefore, much fewer degrees of freedom should be solved for a given mesh. The Octree-based AMR will create a fine mesh in the moving region containing the melt pool and a coarser mesh in the rest of the region. Thanks to the presence of Octree structure, the remeshing and variables transfer for the nodes and Gauss point can be performed in an analytical way with negligible CPU time. To validate the proposed approach, simulations of a laser welding benchmark and a single-track direct energy deposition process were conducted using both the Octree-based MTF framework and the MTF framework with a pre-defined mesh (numerical reference). Results show that the proposed method reduces CPU time by more than a factor of 5, while maintaining high accuracy in thermal cycles and melt pool dimensions, with deviations of less than 5%.
