Inter-Laboratory Characterization of the Velocity Field in the FDA Blood Pump Model Using Particle Image Velocimetry (PIV)
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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.
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.
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.
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).
KeywordsParticle image velocimetry PIV Benchmark model CFD validation VVUQ Computational fluid dynamics FDA
Our thanks to Dr. Tina Morrison at the FDA/Center for Devices and Radiological Health for review of the manuscript and helpful comments. Matthew Giarra (Rochester Institute of Technology) was the principal design engineer for the blood pump model, under the direction of Dr. Steven W. Day and in collaboration with Dr. Richard A. Malinauskas.
Conflict of Interest
All authors declare that they have no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 4.CDRH. CDRH regulatory science report, http://www.fda.gov/downloads/MedicalDevices/ScienceandResearch/UCM521503.pdf, 2018.
- 7.Fabritious, B. Application of Genetic Algorithms to Problems in Computational Fluid Dynamics. Exeter: University of Exeter, 2014.Google Scholar
- 8.Giarra, M. N. Shear Stress Distribution and Hemolysis Measurements in a Centrifugal Blood Pump. Rochester: Mechanical Engineering, Rochester Institute of Technology, 2009.Google Scholar
- 10.Hariharan, P., M. Giarra, V. Reddy, S. W. Day, K. B. Manning, S. Deutsch, S. F. Stewart, M. R. Myers, M. R. Berman, G. W. Burgreen, E. G. Paterson, and R. A. Malinauskas. Multilaboratory particle image velocimetry analysis of the FDA benchmark nozzle model to support validation of computational fluid dynamics simulations. J. Biomech. Eng. 133:041002, 2011.CrossRefGoogle Scholar
- 12.Smithee, I., and S. P. Gent. Computational fluid dynamics modeling of blood as a heterogeneous fluid. In: Design of Medical Devices Conference, 2018.Google Scholar
- 16.McCroskey, W. J. A Critical Assessment of Wind Tunnel Results for the NACA 0012 Airfoil. Moffett Field: N.A.a.S. Administration, 1987.Google Scholar
- 24.Steinman, D. A., Y. Hoi, P. Fahy, L. Morris, M. T. Walsh, N. Aristokleous, A. S. Anayiotos, Y. Papaharilaou, A. Arzani, S. C. Shadden, P. Berg, G. Janiga, J. Bols, P. Segers, N. W. Bressloff, M. Cibis, F. H. Gijsen, S. Cito, J. Pallares, L. D. Browne, J. A. Costelloe, A. G. Lynch, J. Degroote, J. Vierendeels, W. Fu, A. Qiao, S. Hodis, D. F. Kallmes, H. Kalsi, Q. Long, V. O. Kheyfets, E. A. Finol, K. Kono, A. M. Malek, A. Lauric, P. G. Menon, K. Pekkan, M. Esmaily Moghadam, A. L. Marsden, M. Oshima, K. Katagiri, V. Peiffer, Y. Mohamied, S. J. Sherwin, J. Schaller, L. Goubergrits, G. Usera, M. Mendina, K. Valen-Sendstad, D. F. Habets, J. Xiang, H. Meng, Y. Yu, G. E. Karniadakis, N. Shaffer, and F. Loth. Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge. J. Biomech. Eng. 135(2013):021016, 2012.Google Scholar
- 25.Stewart, S. F. C., P. Hariharan, E. G. Paterson, G. W. Burgreen, V. Reddy, S. W. Day, M. Giarra, K. B. Manning, S. Deutsch, M. R. Berman, M. R. Myers, and R. A. Malinauskas. Results of FDA’s first interlaboratory computational study of a nozzle with a sudden contraction and conical diffuser. Cardiovasc. Eng. Technol. 4:374–391, 2013.CrossRefGoogle Scholar
- 26.Stewart, S. F. C., E. G. Paterson, G. W. Burgreen, P. Hariharan, M. Giarra, V. Reddy, S. W. Day, K. B. Manning, S. Deutsch, M. R. Berman, M. R. Myers, and R. A. Malinauskas. Assessment of CFD performance in simulations of an idealized medical device: results of FDA’s First Computational Interlaboratory Study. Cardiovasc. Eng. Technol. 3:139–160, 2012.CrossRefGoogle Scholar
- 28.American Society of Mechanical Engineers. Draft Standard (Under Development)—Verification and Validation in Computational Modeling of Medical Devices. New York: ASME.Google Scholar
- 30.U.S. FDA. Guidance for Industry and Food and Drug Administration Staff—Reporting of Computational Modeling Studies in Medical Device Submissions, 2016.Google Scholar