Türkisch-Deutsche Universität

Hydrodynamic design of blood-contacting medical devices has long relied on meshed CFD, where mesh structure and compute cost slow optimization. A mesh-free physics-informed neural network writes the axisymmetric mass-conservation equation into its loss function and is trained on experimental PIV/LDV data over the FDA reference nozzle, spanning laminar, transitional and fully turbulent regimes (Re = 500–6500), reconstructing flow fields and turbulent kinetic energy at high resolution and yielding an empirical correlation for peak TKE as a rapid hydrodynamic indicator.

Hydrodynamic design of blood-contacting medical devices has long relied on meshed CFD, where mesh structure and compute cost slow optimization. A mesh-free physics-informed neural network writes the axisymmetric mass-conservation equation into its loss function and is trained on experimental PIV/LDV data over the FDA reference nozzle, spanning laminar, transitional and fully turbulent regimes (Re = 500–6500), reconstructing flow fields and turbulent kinetic energy at high resolution and yielding an empirical correlation for peak TKE as a rapid hydrodynamic indicator.