Numerical investigation of three patterns of motion in an electromagnetic pulsatile VAD

Computational fluid dynamics-based study of possibility of generating pulsatile blood flow via a continuous-flow VAD

Until recent years, it was almost beyond remedy to save the life of end-stage heart failure patients without considering a heart transplant. This is while the need for healthy organs has always far exceeded donations. However, the evolution of VAD technology has certainly changed the management of these patients. Today, blood pumps are designed either pulsatile flow or continuous flow, each of which has its own concerns and limitations. For instance, pulsatile pumps are mostly voluminous and hardly can be used for children. On the other hand, the flow generated by continuous-flow pumps is in contrast with pulsatile flow of the natural heart. In this project, having used computational fluid dynamics, we studied the possibility of generating pulsatile blood flow via a continuous-flow blood pump by adjusting the rotational speed of the pump with two distinct patterns (sinusoidal and trapezoidal), both of which have been proposed and set based on physiological needs and blood flow waveform of the natural heart. An important feature of this study is setting the outlet pressure of the pump similar to the physiological conditions of a patient with heart failure, and since these axial pumps are sensitive to outlet pressures, more secure and reliable results of their performance are achieved. Our results show a slight superiority of a sinusoidal pattern compared to a trapezoidal one with the potential to achieve an adequate pulsatile flow by precisely controlling the rotational speed.

Multi-objective optimization of pulsatile ventricular assist device hemocompatibility based on neural networks and a genetic algorithm

PURPOSE

Given the benefit of pulsatile blood flow for perfusion of coronary arteries and end organs, pulsatile ventricular assist devices (VADs) are still widely used as paracorporeal mechanical circulatory support devices in clinical applications. However, poor hemocompatibility limits the service period of the VADs. Most previous improvements on VAD hemocompatibility were conducted by trial-and-error CFD analysis, which does not easily arrive at the best solution.

METHODS

In this paper, a multi-objective optimization method integrating neural networks and NSGA-II (Non-dominated Sorted Genetic Algorithm-II) based on FSI simulation was developed and applied to a pulsatile VAD to optimize its hemocompatibility. First, the VAD blood chamber was parameterized with the principal geometrical parameters. Three hemocompatibility indices including hemolysis, platelet activation, and platelet deposition were chosen as goal functions. The neural networks were built to fit the nonlinear relationship between goal functions and geometrical parameters. Next, a multi-objective optimization algorithm (NSGA-II) was used to search out the Pareto optimal solutions in the built neural networks. Finally, the best compromise solution was selected from the Pareto optimal solutions by a fuzzy membership approach and validated by FSI simulation.

RESULTS

The best compromise solution simultaneously possesses an acceptable hemolysis index, platelet activation index, and platelet deposition index, and the corresponding relative errors between the indices predicted by optimization algorithm and the one calculated by FSI simulations are all less than 5%.

CONCLUSIONS

The results suggest that the proposed multi-objective optimization method has the potential for application in optimizing pulsatile VAD hemocompatibility, and may also be applied to other blood-wetted devices.