Computational Fluid Dynamics Modeling of the Human Pulmonary Arteries with Experimental Validation

Abstract

Pulmonary hypertension (PH) is a chronic progressive disease characterized by elevated pulmonary arterial pressure, caused by an increase in pulmonary arterial impedance. Computational fluid dynamics (CFD) can be used to identify metrics representative of the stage of PH disease. However, experimental validation of CFD models is often not pursued due to the geometric complexity of the model or uncertainties in the reproduction of the required flow conditions. The goal of this work is to validate experimentally a CFD model of a pulmonary artery phantom using a particle image velocimetry (PIV) technique. Rapid prototyping was used for the construction of the patient-specific pulmonary geometry, derived from chest computed tomography angiography images. CFD simulations were performed with the pulmonary model with a Reynolds number matching those of the experiments. Flow rates, the velocity field, and shear stress distributions obtained with the CFD simulations were compared to their counterparts from the PIV flow visualization experiments. Computationally predicted flow rates were within 1% of the experimental measurements for three of the four branches of the CFD model. The mean velocities in four transversal planes of study were within 5.9 to 13.1% of the experimental mean velocities. Shear stresses were qualitatively similar between the two methods with some discrepancies in the regions of high velocity gradients. The fluid flow differences between the CFD model and the PIV phantom are attributed to experimental inaccuracies and the relative compliance of the phantom. This comparative analysis yielded valuable information on the accuracy of CFD predicted hemodynamics in pulmonary circulation models.

Markov chain Monte Carlowith Gaussian processes for fast parameter estimation and uncertainty quantification in a1Dfluid‐dynamics model of the pulmonary circulation

“…We use a 1D fluid‐dynamics model developed by the authors of 6,66 . Compared to 3D fluid‐dynamics models, 67,68 1D models can predict nearly identical pressure and flow waves throughout the pulmonary network at a fraction of the computational cost, making them ideal as a real‐time clinical tool. Therefore, the 1D models are often used as computationally tractable approximations to 3D models 38 .…”

Sensitivity analysis and uncertainty quantification of 1‐D models of pulmonary hemodynamics in mice under control and hypertensive conditions

“…We refer to this model as the "single bifurcation" (SB) model. The advantage of the SB model is that it can predict perfusion to the left and the right lobes of the lung, enabling us to compare results with previous studies 13,57 . Lastly, we compare results from the SV and SB networks with a more realistic network containing 21 vessels (right panel of Figure 3).…”

“…wall stiffness and network morphology) and function (e.g. blood pressure and flow propagation), which is vital for developing better clinical tools for disease detection and monitoring 13,41 . To test the fidelity of our model we use sensitivity analysis, uncertainty quantification, and parameter inference comparing model predictions to MPA pressure data measured in adult mice under control and hypertensive (induced by hypoxia) conditions.…”

A computationally tractable scheme for simulation of the human pulmonary system

“…Other validations have been performed using full 3D simulation [32], with strong concordance between the 3D and 1D models [33,34]. Effective validation has also been performed in capillary networks [35], though this compared steady-state distributions only.…”