The use of computational fluid dynamics in the development of ventricular assist devices.

ABSTRACT

Progress in the field of prosthetic cardiovascular devices has significantly contributed to the rapid advancements in cardiac therapy during the last four decades. The concept of mechanical circulatory assistance was established with the first successful clinical use of heart-lung machines for cardiopulmonary bypass. Since then a variety of devices have been developed to replace or assist diseased components of the cardiovascular system. Ventricular assist devices (VADs) are basically mechanical pumps designed to augment or replace the function of one or more chambers of the failing heart. Computational Fluid Dynamics (CFD) is an attractive tool in the development process of VADs, allowing numerous different designs to be characterized for their functional performance virtually, for a wide range of operating conditions, without the physical device being fabricated. However, VADs operate in a flow regime which is traditionally difficult to simulate; the transitional region at the boundary of laminar and turbulent flow. Hence different methods have been used and the best approach is debatable. In addition to these fundamental fluid dynamic issues, blood consists of biological cells. Device-induced biological complications are a serious consequence of VAD use. The complications include blood damage (haemolysis, blood cell activation), thrombosis and emboli. Patients are required to take anticoagulation medication constantly which may cause bleeding. Despite many efforts blood damage models have still not been implemented satisfactorily into numerical analysis of VADs, which severely undermines the full potential of CFD. This paper reviews the current state of the art CFD for analysis of blood pumps, including a practical critical review of the studies to date, which should help device designers choose the most appropriate methods; a summary of blood damage models and the difficulties in implementing them into CFD; and current gaps in knowledge and areas for future work.