REGRESSION OF THE NAVIER-STOKES EQUATION SOLUTIONS FOR PULMONARY AIRWAY FLOW USING NEURAL NETWORKS

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Respiratory System Modeling

Background:

• Particle deposition in airways is crucial for understanding lung diseases like asthma and COPD.
• Patient-specific geometry significantly impacts deposition patterns but is computationally challenging to model.
• Current Computational Fluid Dynamics (CFD) models face hurdles with complex human lung geometries.

Purpose of the Study:

• To develop an approximation method for simulating airflow and particle deposition in patient-specific airway geometries.
• To overcome the computational complexity of full CFD simulations in human lungs.
• To enable more accurate, patient-specific studies of lung diseases.

Main Methods:

• Developed a neural network approximation to the Navier-Stokes equations.
• Modeled airflow and tobacco particle deposition in a Physiologically Realistic Bifurcation (PRB) model.
• Trained a neural network to regress mean velocity and mass flow components using ANSYS Fluent simulations.

Main Results:

• The neural network approximation effectively models airflow and particle deposition under specified assumptions.
• The method demonstrated good performance for a single-generation airway branch.
• Serial application to a two-generation airway geometry yielded reasonable approximations.

Conclusions:

• Neural network-based approximations offer a viable and efficient alternative to full CFD for patient-specific airway modeling.
• This approach can facilitate personalized studies of particle deposition and lung disease progression.
• Further application to more complex airway geometries is warranted.