Coupling CFD and Machine Learning to Assess Flow Properties in Porous Scaffolds for Tissue Engineering.

ABSTRACT: Computational fluid dynamics and machine learning (ML) models are employed to investigate the relationships between scaffold topology and key flow parameters, including permeability (k), average wall shear stress (WSSa), and the 25th and 75th percentiles of WSS. Statistical analysis showed that WSSa values are consistent with those found in common scaffold architectures, while percentile-based WSS properties provided insight into shear environments relevant for bone and cartilage differentiation. No significant effect of pore shape was observed on k and WSSa. Correlation analysis revealed that k was positively associated with topological features of the scaffold, whereas WSS metrics were negatively correlated with these properties. ML models trained on six topological

SUBMITTER: Rodriguez-Guerra J 

PROVIDER: S-EPMC12566269 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature