ABSTRACT Numerical modeling of material extrusion additive manufacturing processes is a well-established method for analyzing and understanding various phenomena occurring during printing. Several studies have demonstrated how different printing parameters and rheological properties influence strand morphology, surface roughness, and mesostructures. In large-area additive manufacturing, the deformation of deposited layers can become significant if sufficient yield stress is not achieved. To address this, we have developed a computational fluid dynamics (CFD) model that incorporates time-dependent yield stress buildup to stabilize the printed layers [1]. Specifically, a scalar-based approach is employed to increase the yield stress of previously deposited layers when printing new layers with lower yield stress. The results show a significant reduction in deformation, along with improvements in surface roughness. Beyond simulating strand morphologies and macroscopic features, CFD can also be leveraged to investigate microscopic characteristics, specifically fiber orientation. It was previously assumed that fibers align exclusively along the printing direction; however, this alignment depends on multiple factors, including nozzle design and process parameters. To study this behavior, a new solver was developed in OpenFOAM, implementing the Advani-Tucker tensor approach, where a tensor represents the evolving fiber orientation [2, 3]. The model captures key effects such as fiber-fiber interactions and two-way coupling, in which the flow field influences fiber orientation, and the resulting orientation, in turn, affects viscosity anisotropically. The fiber orientation model is implemented using an overset mesh methodology, where the nozzle is represented as an overset mesh and the background mesh defines the deposition domain. The simulations of fiber orientation during 3D printing allow their prediction and control, providing valuable insights into the development of microstructural features and their influence on the performance of printed parts. REFERENCES [1] Mollah, Md Tusher et al., Additive Manufacturing 71 (2023): 103605. [2] Šeta, Berin et al., Composites Part B 266 (2023): 110957. [3] Šeta, Berin et al., Additive Manufacturing 92 (2024): 104396.