Multiphysics modeling adds to understanding fine-particle transport in human airways
Published on: October 7, 2019
Respiratory diseases represent a leading cause of global mortality —along with a hefty financial burden on health care systems. Around 250 million people are afflicted, and their treatment costs over $10,000 (US) each year. But it is much harder to quantify the lowered quality of life these patients experience.
With this in mind, you can easily understand why health care providers and regulatory agencies across the globe are working to improve treatment quality as well as to lower related costs.
Product development engineering hurdles center on limited knowledge of air-flow patterns and fine-particle transport within diseased human airways, resulting in very poor drug delivery. The best inhalers deliver approximately 30% of the total inhaled drug into the intended target sites; much of the drug is wasted and side effects are prevalent. The scenario is complicated by inter-subject anatomical and physiological variation as well as difficulty in sampling from human airways.
Status of computer simulations
Experimental studies in this field are fraught with difficulties and extensive costs, making in-silico optimization an attractive option, especially recently. Researchers can better predict different conditions (age, disease state, inter-subject variation, etc.) via modeling, varying input parameters like air flow rate, airway geometry, mucus-layer thickness and adhesion. Medical device researchers can maximize drug delivery by optimizing formulation, changing the particle size and shape along with actuation conditions.
Since the drug deposition is largely governed by fluid forces, computational
fluid dynamics (CFD) has been used extensively for such simulations. Good
resolution of fluid-flow patterns has frequently provided excellent quantitative
matching with experiments, both absolute values and parametric trendlines.
However, using point-particles in CFD simulation suffers from two
limitations that restrict its utility:
(a) Spherical shape Assumption of sphericity can be restrictive and inaccurate when simulating long fibers for occupational diseases or when studying high-aspect-ratio carrier particles in drug formulations. (b) Trap boundary condition A particle touching the airway wall is entrapped on the wall and does not re-enter the air stream. In reality, this may not be accurate. Furthermore, you cannot capture local variations in mucus thickness and stickiness.
These limitations can be effectively overcome by discrete element method (DEM) simulation, which models a particle phase much more accurately than CFD.
DEM-CFD integration for predicting particle deposition
In coupled simulations, fluid flow and particle motion are modeled through an integration between Rocky DEM and ANSYS Fluent. The flowchart of this coupling scheme is given in Figure 1.
Using Rocky DEM, which is fully integrated with ANSYS tools and embedded in ANSYS Workbench, there are a number of advantages:
(a) DEM-CFD Coupling Full integration with Fluent allows for seamless DEM-CFD coupling. For the current problem — in which particle motion is affected by fluid forces but fluid flow is not, in turn, affected by particles — one-way DEM-CFD coupling is a reasonable approach. This is easily achieved using the Rocky UI, with just a click of a button.
After a mesh-independence study of reconstructed lung geometry (Figure 2), the converged steady-state airflow pattern in Fluent can be imported to Rocky.
(b) Workbench Optimization By running Rocky in Workbench, one can parametrize variables, like particle size and air flow rate, and generate a response surface to see deposition-pattern variation. Large study designs can be easily executed, producing enhanced process understanding that leads to identification of an optimal condition, with just a fraction of the effort.
(c) Multiple Meshes Rocky also supports multiple mesh types: tetrahedral, hexahedral and polyhedral. Polyhedral meshes were used for this work, since they offer enhanced computation efficiency with higher quality and increased cell connectivity, thus improving numerical solver stability.
(d) Custom Particle Shapes One can model a custom particle shape, and Rocky can compute the associated fluid forces and provide adhesion details to capture particle-mucus interactions.
(e) Faster Solution In case a two-way simulation runs, Rocky does not compete with Fluent for computational resources or memory. Rocky can run in GPUs while Fluent uses CPUs.
Obtaining adhesive-force parameters requires calibration of material behavior, such as matching regional particle deposition with experiments performed on 3-D printed airway casts. Since reduced adhesive-force stiffness proportionally decreases adhesion and particle-deposition fraction, we evaluated different values in this work to find a good match with experiments at a steady 15 liters-per-minute (LPM) inhalation (Figure 4).
Using simulation results, the integrated solution generated an animation that clearly shows the particle movement and deposition in a healthy airway. Also, post-processing features in Rocky have enabled us to mark deposited particles at each lobar bronchi.
Using Rocky DEM, multiphysics DEM-CFD calculation integrated with easy-to-use statistical analysis tools provides accurate and fast answers to drug particle transport. For example, a user can back-track the injection position of the regionally deposited particle — which is helpful in optimizing actuator and mouth-piece design. Researchers can test variation of the steady-flow rate along with patient-specific breathing patterns through two-way integrated simulations. Finally, applying similar simulation on patient-specific geometries can lead to an enhanced understanding of the individual’s diseases, resulting in more-efficient clinical plans.
Applications Engineer, Rocky DEM Business Unit at ESSS
Ahmad holds a Master of Science degree in Chemical Engineering from Oklahoma State University with experience in analyzing multiphase and turbulent flows in the field of biomechanics. His prior roles included developing and optimizing chemical processes through data analysis tools in the oil and gas industries. Given his diverse background from petroleum to pharmaceuticals, Ahmad recognizes and understands the challenges ahead of simulating multiphysics systems across many industries.