Design Method Using Statistical Models for Miniature Left Ventricular Assist Device Hydraulics

Smith, P. Alex; Wang, Yaxin; Bieritz, Shelby A.; Sampaio, Luiz C.; Cohn, William E.; Metcalfe, Ralph W.; Frazier, O. H.

doi:10.1007/s10439-018-02140-w

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…As can be seen from Fig. 1 b the impeller consists of 2 rotating blades and the diffuser consists of 3 stationary blades, in keeping with the previous study by Smith et al 27 . A cutaway view of the NeoVAD is shown in Fig.…”

Section: Methodssupporting

confidence: 75%

Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

Nissim

Karnik²,

Smith³

et al. 2023

Sci Rep

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The NeoVAD is a proposed paediatric axial-flow Left Ventricular Assist Device (LVAD), small enough to be implanted in infants. The design of the impeller and diffuser blades is important for hydrodynamic performance and haemocompatibility of the pump. This study aimed to optimise the blades for pump efficiency using Computational Fluid Dynamics (CFD), machine learning and global optimisation. Meshing of each design typically included 6 million hexahedral elements and a Shear Stress Transport turbulence model was used to close the Reynolds Averaged Navier–Stokes equations. CFD models of 32 base geometries, operating at 8 flow rates between 0.5 and 4 L/min, were created to match experimental studies. These were validated by comparison of the pressure-flow and efficiency-flow curves with those experimentally measured for all base prototype pumps. A surrogate model was required to allow the optimisation routine to conduct an efficient search; a multi-linear regression, Gaussian Process Regression and a Bayesian Regularised Artificial Neural Network predicted the optimisation objective at design points not explicitly simulated. A Genetic Algorithm was used to search for an optimal design. The optimised design offered a 5.51% increase in efficiency at design point (a 20.9% performance increase) as compared to the best performing pump from the 32 base designs. An optimisation method for the blade design of LVADs has been shown to work for a single objective function and future work will consider multi-objective optimisation.

show abstract

Section: Methodssupporting

confidence: 75%

Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

Nissim

Karnik²,

Smith³

et al. 2023

Sci Rep

Self Cite

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show abstract

“…The hydrodynamic efficiency of the prototype RIAFBP was estimated to be 7%‐8%, while that of the miniature RIAFBP was estimated as 6%‐7%. The maximum efficiency of the miniature axial flow blood pump developed by Smith et al is 20%, but this result was obtained using a bigger motor in the in vitro measurement system.…”

Section: Discussionmentioning

confidence: 98%

Development of rear‐impeller axial flow blood pump for realization of axial flow blood pump installed at aortic valve position

et al. 2019

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In this study, rear‐impeller axial flow blood pumps (RIAFBP) were developed to realize a trans‐valve axial ventricular assist device (VAD) which consists of the latter blood pump and a polymer monomembrane aortic valve, such as the jellyfish valve. The motor of the RIAFBP is installed in the left ventricle, and its impeller is placed at the aortic valve position. In the prototype RIAFBP, the rotation of the motor is sustained by polyethylene bushings. The RIAFBP has a length of 50 mm and diameter of 19.6 mm. The miniature RIAFBP has the same construction as that of the prototype; however, it employs a ceramic bearing and fin bearing to improve endurance and to reduce blood stagnation. The miniature RIAFBP has a length of 63 mm and diameter of 12 mm. Both RIAFBPs were examined by an in vitro experiment using a 33% glycerin solution. The prototype RIAFBP achieved a maximum pump outflow of 8.5 L/min against a pump head of 100 mm Hg at a rotational speed of 12 000 rpm. The miniature RIAFBP achieved 7 L/min against a pump head of 70 mm Hg at a rotational speed of 21 600 rpm. In conclusion, the miniature RIAFBP has enough pump performance to realize the trans‐valve axial VAD.

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“…The rotor-stator convention that is more common for axial-flow devices is avoided to reduce confusion with the magnetic levitation and motor parts that bear the same name. As can be seen from Figure 1 (b) the impeller consists of 2 rotating blades and the diffuser consists of 3 stationary blades 18 .…”

Section: Methodsmentioning

confidence: 99%

“…The blade geometry for this study is defined and parameterised in accordance with previous experiments 18 . The baseline design consists of an impeller of two blades and a diffuser of three blades.…”

Section: Computational Fluid Dynamicsmentioning

confidence: 99%

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Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades

Nissim

Karnik²,

Smith³

et al. 2023

Preprint

View full text Add to dashboard Cite

The NeoVAD is a proposed paediatric axial-flow Left Ventricular Assist Device (LVAD), small enough to be implanted in infants. The design of the impeller and diffuser blades is important for hydrodynamic performance and haemocompatibility of the pump. This study aimed to optimise the blades for pump efficiency using Computational Fluid Dynamics (CFD), machine learning and global optimisation. Meshing of each design typically included ~6 million hexahedral elements and a Shear Stress Transport (SST) turbulence model was used to close the Reynolds Averaged Navier-Stokes (RANS) equations. CFD models of 32 base geometries, operating at 8 flow rates between 0.5 and 4 l/min, were created to match experimental studies. These were validated by comparison of the pressure-flow and efficiency-flow curves with those experimentally measured for all base prototype pumps. A surrogate model was required to allow the optimisation routine to conduct an efficient search; a multi-linear regression, Gaussian Process Regression and a Bayesian Regularised Artificial Neural Network (BRANN) predicted the optimisation objective at design points not explicitly simulated. A Genetic Algorithm was used to search for an optimal design. The optimised design offered a 5.51% increase in efficiency at design point (a 20.9% performance increase) as compared to the best performing pump from the 32 base designs.An optimisation method for the blade design of LVADs has been shown to work for a single objective function and future work will consider multi-objective optimisation.

show abstract

Design Method Using Statistical Models for Miniature Left Ventricular Assist Device Hydraulics

Cited by 12 publications

References 15 publications

Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

Development of rear‐impeller axial flow blood pump for realization of axial flow blood pump installed at aortic valve position

Machine Learning based on Computational Fluid Dynamics enables geometric design optimisation of the NeoVAD blades

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