Inter-Laboratory Characterization of the Velocity Field in the FDA Blood Pump Model Using Particle Image Velocimetry (PIV).

PURPOSE: A credible computational fluid dynamics (CFD) model can play a meaningful role in evaluating the safety and performance of medical devices. A key step towards establishing model credibility is to first validate CFD models with benchmark experimental datasets to minimize model-form errors before applying the credibility assessment process to more complex medical devices. However, validation studies to establish benchmark datasets can be cost prohibitive and difficult to perform. The goal of this initiative sponsored by the U.S. Food and Drug Administration is to generate validation data for a simplified centrifugal pump that mimics blood flow characteristics commonly observed in ventricular assist devices. METHODS: The centrifugal blood pump model was made from clear acrylic and included an impeller, with four equally spaced, straight blades, supported by mechanical bearings. Particle Image Velocimetry (PIV) measurements were performed at several locations throughout the pump by three independent laboratories. A standard protocol was developed for the experiments to ensure that the flow conditions were comparable and to minimize systematic errors during PIV image acquisition and processing. Velocity fields were extracted at the pump entrance, blade passage area, back gap region, and at the outlet diffuser regions. A Newtonian blood analog fluid composed of sodium iodide, glycerin, and water was used as the working fluid. Velocity measurements were made for six different pump flow conditions, with the blood-equivalent flow rate ranging between 2.5 and 7 L/min for pump speeds of 2500 and 3500 rpm. RESULTS: Mean intra- and inter-laboratory variabilities in velocity were ~ 10% at the majority of the measurement locations inside the pump. However, the inter-laboratory variability increased to more than ~ 30% in the exit diffuser region. The variability between the three laboratories for the peak velocity magnitude in the diffuser region ranged from 5 to 25%. The bulk velocity field near the impeller changed proportionally with the rotational speed but was relatively unaffected by the pump flow rate. In contrast, flow in the exit diffuser region was sensitive to both the flow rate and the rotational speed. Specifically, at 3500 rpm, the exit jet tilted toward the inner wall of the diffuser at a flow rate of 2.5 L/min, but the jet tilted towards the outer wall when the flow rate was 7 L/min. CONCLUSIONS: Inter-laboratory experimental mean velocity data (and the corresponding variance) were obtained for the FDA pump model and are available for download at https://nciphub.org/wiki/FDA_CFD . Experimental datasets from the inter-laboratory characterization of benchmark flow models, including the blood pump model presented herein and our previous nozzle model, can be used for validating future CFD studies and to collaboratively develop guidelines on best practices for verification, validation, uncertainty quantification, and credibility assessment of CFD simulations in the evaluation of medical devices (e.g. ASME V&V 40 standards working group).

Fluid Dynamics in Rotary Piston Blood Pumps.

Mechanical circulatory support can maintain a sufficient blood circulation if the native heart is failing. The first implantable devices were displacement pumps with membranes. They were able to provide a sufficient blood flow, yet, were limited because of size and low durability. Rotary pumps have resolved these technical drawbacks, enabled a growing number of mechanical circulatory support therapy and a safer application. However, clinical complications like gastrointestinal bleeding, aortic insufficiency, thromboembolic complications, and impaired renal function are observed with their application. This is traced back to their working principle with attenuated or non-pulsatile flow and high shear stress. Rotary piston pumps potentially merge the benefits of available pump types and seem to avoid their complications. However, a profound assessment and their development requires the knowledge of the flow characteristics. This study aimed at their investigation. A functional model was manufactured and investigated with particle image velocimetry. Furthermore, a fluid-structure interaction computational simulation was established to extend the laboratory capabilities. The numerical results precisely converged with the laboratory measurements. Thus, the in silico model enabled the investigation of relevant areas like gap flows that were hardly feasible with laboratory means. Moreover, an economic method for the investigation of design variations was established.

FDA Benchmark Medical Device Flow Models for CFD Validation.

Computational fluid dynamics (CFD) is increasingly being used to develop blood-contacting medical devices. However, the lack of standardized methods for validating CFD simulations and blood damage predictions limits its use in the safety evaluation of devices. Through a U.S. Food and Drug Administration (FDA) initiative, two benchmark models of typical device flow geometries (nozzle and centrifugal blood pump) were tested in multiple laboratories to provide experimental velocities, pressures, and hemolysis data to support CFD validation. In addition, computational simulations were performed by more than 20 independent groups to assess current CFD techniques. The primary goal of this article is to summarize the FDA initiative and to report recent findings from the benchmark blood pump model study. Discrepancies between CFD predicted velocities and those measured using particle image velocimetry most often occurred in regions of flow separation (e.g., downstream of the nozzle throat, and in the pump exit diffuser). For the six pump test conditions, 57% of the CFD predictions of pressure head were within one standard deviation of the mean measured values. Notably, only 37% of all CFD submissions contained hemolysis predictions. This project aided in the development of an FDA Guidance Document on factors to consider when reporting computational studies in medical device regulatory submissions. There is an accompanying podcast available for this article. Please visit the journal's Web site (www.asaiojournal.com) to listen.