An efficient computational fluid-particle dynamics method to predict deposition in a simplified approximation of the...

Area of Science:

• Computational fluid dynamics
• Respiratory system modeling
• Particle transport phenomena

Background:

• Accurate simulation of particle deposition in the human lung is computationally intensive.
• Existing models struggle with high-fidelity simulations of the complete airway tree.
• Large-scale lung simulations are needed for advanced respiratory research.

Purpose of the Study:

• To develop a computationally efficient numerical methodology for predicting particle deposition in the deep lung.
• To simulate particle deposition during a full breathing cycle using a simplified lung model.
• To reduce computational costs for large-scale lung simulations.

Main Methods:

• Developed a geometrical model of the bronchial tree (generations 10-19) and an acinar model.
• Decomposed the bronchial tree into representative subunits to reduce computational cost.
• Employed an Eulerian-Lagrangian approach for airflow and particle transport simulation.
• Utilized topological information to account for gravitational forces on particles.

Main Results:

• Achieved significant savings in computational cost by simulating representative subunits.
• Deposition estimates align well with existing literature trends.
• Found comparable deposition during exhalation and inhalation for 1-5μm particles, suggesting breath-hold benefits.
• Demonstrated significant impact of airway orientation on deposition rates, especially for particles >2μm.

Conclusions:

• The developed methodology offers a computationally efficient approach for lung particle deposition simulation.
• Airway orientation and breathing phase significantly influence particle deposition patterns.
• A simplified semi-analytical approach further reduces computational costs with minimal accuracy loss.