Surrogate‐Assisted Multi‐Objective Optimization of an Axial‐Flow Ventricular Assist Device Rotor Using CFD , Hybrid Surrogates, Sobol Analysis, and MOPSO

ABSTRACT Background This study presents a surrogate‐assisted workflow for multi‐objective optimization of an axial‐flow ventricular assist device (VAD) rotor using 4809 steady‐state CFD simulations spanning six geometric variables. Methods Repeated learning‐curve analysis and blade‐count‐stratified Gaussian‐process regression (GPR) assessed data efficiency and complemented tree‐based surrogate models. Final selection retained GPR for pressure rise, torque, hemolysis index, platelet activation, and exposure time, and HistGradientBoosting for hydraulic efficiency and the low‐shear fraction S R 2 values of 0.968–1.000. Because hydraulic efficiency is derived from pressure rise and torque at the fixed operating condition, the primary MOPSO formulation used six objectives: maximizing pressure rise and S  Results The balanced compromise used a 53.58° inlet blade angle, 20.93° outlet blade angle, three blades, a 0.222 mm clearance gap, 0.796 mm blade thickness, and a 20.00 mm rotor length. For the balanced compromise, mean surrogate‐to‐CFD relative error was 4.0%, with all individual errors below 10%. Relative to baseline, direct CFD confirmed reductions of 17.2% in torque, 55.6% in hemolysis index, 46.9% in platelet activation, and 27.0% in exposure time, together with a 4.5% increase in S  Conclusions The workflow supports design‐space screening, trade‐off identification, and targeted CFD confirmation of candidate geometries.