Modeling of a rotary blood pump.

The accurate representation of rotary blood pumps in a numerical environment is important for meaningful investigation of pump-cardiovascular system interactions. Although numerous models for ventricular assist devices (VADs) have been developed, modeling methods for rotary total artificial hearts (rTAHs) are still required. Therefore, an rTAH prototype was characterized in a steady flow, hydraulic test bench over a wide operational range for pump and hydraulic parameters. In order to develop a generic modeling method, a data-driven modeling approach was chosen. k-Nearest-neighbors, artificial neural networks, and support vector machines (SVMs) were the machine learning approaches evaluated. The best performing parameters for each algorithm were determined via optimization. The resulting multiple-input-multiple-output models were subsequently assessed under identical conditions, and a SVM with a radial basis function kernel was identified as the best performing. The achieved root mean squared errors were 0.03 L/min, 0.06 L/min, and 0.18 W for left and right flow and motor power consumption, respectively. In comparison with existing models for VADs, the flow errors are more than 70% lower. Further advantages of the SVM model are the robustness to measurement noise and the capability to operate outside of the trained parameter range. This proposed modeling method will accelerate further device refinements by providing a more appropriate numerical environment in which to evaluate the pump-cardiovascular system interaction.