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 ((Formula presented.)), and the 25th and 75th percentiles of WSS. Statistical analysis showed that (Formula presented.) 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 (Formula presented.). Correlation analysis revealed that k was positively associated with topological features of the scaffold, whereas (Formula presented.) metrics were negatively correlated with these properties. ML models trained on six topological and flow inputs achieved a performance of (Formula presented.) above 0.9 for predicting k and (Formula presented.), demonstrating strong predictive capability based on the topology. Their performance decreased for (Formula presented.) and (Formula presented.), reflecting the difficulty in capturing more specific shear events. These findings highlight the potential of ML to guide scaffold design by linking topology to flow conditions critical for osteogenesis. © 2025 by the authors.