Validation of CFD predictions of flow in a 3D alveolated bend with experimental data

Abstract

Verifying numerical predictions with experimental data is an important aspect of any modeling studies. In the case of the lung, the absence of direct in vivo flow measurements makes such verification almost impossible. We performed computational fluid dynamics (CFD) simulations in a 3D scaled-up model of an alveolated bend with rigid walls that incorporated essential geometrical characteristics of human alveolar structures and compared numerical predictions with experimental flow measurements made in the same model by particle image velocimetry (PIV). Flow in both models was representative of acinar flow during normal breathing (0.82 ml/s). The experimental model was built in silicone and silicone oil was used as the carrier fluid. Flow measurements were obtained by an ensemble averaging procedure. CFD simulation was performed with STAR-CCM+ (CD-Adapco) using a polyhedral unstructured mesh. Velocity profiles in the central duct were parabolic and no bulk convection existed between the central duct and the alveoli. Velocities inside the alveoli were ∼2 orders of magnitude smaller than the mean velocity in the central duct. CFD data agreed well with those obtained by PIV. In the central duct, data agreed within 1%. The maximum simulated velocity along the centerline of the model was 0.5% larger than measured experimentally. In the alveolar cavities, data agreed within 15% on average. This suggests that CFD techniques can satisfactorily predict acinar-type flow. Such a validation ensure a great degree of confidence in the accuracy of predictions made in more complex models of the alveolar region of the lung using similar CFD techniques.

Introduction

Many respiratory diseases are directly linked to air pollution and the deposition of particulate matter in the alveolar zone of the lungs (Dockery et al., 1993; Pope et al., 2002; Pope, 1995; Schwartz and Dockery, 1992; Wilson, 1996). The specific pattern of particulate deposition in the airspaces is a key factor in determining the local doses and the subsequent redistribution and clearance of deposited particles. Deposition patterns are mainly affected by particle size and flow characteristics within the respiratory tract. To better understand the transport of aerosols in the complex flow field of the respiratory tract, both numerical modeling studies and in vitro experiments have been performed. Computational fluid dynamics (CFD) modeling offers the flexibility of easily modifying parameters such as lung geometry, flow rates, particle sizes, and ventilation distribution. However, potential numerical errors and artifacts can lead to non-physiological CFD results. Experimental studies offer the advantage of physical realism; once the numerical model is experimentally validated, it is often more efficient to perform the parametric investigation numerically.

Many CFD studies have used complex realistic models of the lung (Harrington et al., 2006; Nowak et al., 2003; van Ertbruggen et al., 2005). These models provided detailed information on the flow field and deposition patterns within lung structures. However, the absence of direct in vivo measurements in the lung made it almost impossible to validate numerical predictions from these models. In vitro studies of the flow in lung structures have been performed by particle image velocimetry (PIV), a non-intrusive technique that allows the measurement of instantaneous velocity fields. Ramuzat (2003) studied both steady and oscillating flow in a three-dimensional (3D) model of three successive generations of conducting airways. Karl et al. (2004) performed flow visualizations studies in a model of a straight alveolated duct that they used to verify CFD predictions; however, no quantitative comparison was provided. Tippe and Tsuda (1999) used the PIV technique to study mixing processes in a model of a single expanding and contracting alveolus.

The aim of the present study, which modeled flow within a 3D alveolated bend, was three-fold: (1) develop a physical model that incorporates essential geometrical characteristics of alveolar structures in which experimental flow measurements could be obtained, (2) perform CFD simulations of the flow in a model that was the exact replica of the physical model, and (3) use the experimental data to verify the numerical predictions to validate the CFD approach. Such validation would ensure a great degree of confidence in the accuracy of CFD flow predictions made with more complex models of the acinar region of the lung for which experimental data may not be available.