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Updated: Jul 20, 2025

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Published on: May 2, 2025
Computational Fluid Dynamics Turbulence Model and Experimental Study for a Fontan Cavopulmonary Assist Device
Shreyas Sarfare1, Md Shujan Ali1, Alan Palazzolo1
1Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843.
Abstract:
Head-flow HQ curves for a Fontan cavopulmonary assist device (CPAD) were measured using a blood surrogate in a mock circulatory loop and simulated with various computational fluid dynamics (CFD) models. The tests benchmarked the CFD tools for further enhancement of the CPAD design. Recommended Reynolds-Averaged Navier-Stokes (RANS) CFD approaches for the development of conventional ventricular assist devices (VAD) were found to have shortcomings when applied to the Fontan CPAD, which is designed to neutralize off-condition obstruction risks that could contribute to a major adverse event. The no-obstruction condition is achieved with a von Karman pump, utilizing large clearances and small blade heights, which challenge conventional VAD RANS-based CFD hemodynamic simulations. High-fidelity large eddy simulation (LES) is always recommended; however, this may be cost-inhibitive for optimization studies in commercial settings, thus the reliance on RANS models. This study compares head and power predictions of various RANS turbulence models, employing experimental measurements and LES results as a basis for comparison. The models include standard k-ϵ, re-normalization group k-ϵ, realizable k-ϵ, shear stress transport (SST) k-ω, SST with transitional turbulence, and Generalized k-ω. For the pressure head predictions, it was observed that the standard k-ϵ model provided far better agreement with experiment. For the rotor torque, k-ϵ predictions were 30% lower than LES, while the SST and LES torque values were near identical. For the Fontan CPAD, the findings support using LES for the final design simulations, k-ϵ model for head and general flow simulation, and SST for power, shear stress, hemolysis, and thrombogenicity predictions.
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