The development of an intra-aortic axial pump to be placed in the descending aorta by a minimally invasive insertion method is detailed. The device is driven by an extra-aortic motor and works in series with the natural heart to offload the left ventricular and augment blood flow to the abdominal organs. An extended computational model of the human circulatory system (HCS), CAM, was initially used to explore the interaction between the HCS and the VAD, and ultimately to define parameters for a prototype. Generalised models for the impeller and motor were developed to bridge the simplified estimations and time consuming finite-element analysis (FEA). Three prototype impellers were designed an analytical model based on turbomachine theory. Despite the efficiency being lower then expected, the impellers are capable of providing sufficient pressure head in both the impeller testing system and the mock circulatory loop. The mock loop provides detailed HCS response profiles to the pump’s axial flow, thereby enabling refinement of the pump design. The analytical motor model provides a starting point for permanent-magnet motor design. The results show good agreement with the FEA and experimental valuations. An ultra-light ironless motor was chosen to drive the pump in the mock loop tests. Two control algorithms to suite psychological needs have been developed for the prototype VAD. The algorithms, which use pressure readings upstream and downstream of the VAD to determine the pump status, were implemented in the controller and have been tested in a mock loop. The results give an insight into controller for an intra-aortic blood pump working in series with the heart. The minimally-invasive axial VAD concept was evaluated, and mock loop experiments demonstrated the device feasibility during afterload reduction and cardiac output augmentation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:604689 |
| Date | January 2010 |
| Creators | Hsu, P.-L. |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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