Pharmaceutical aerosol transport in airways: a combined machine learning (ML) and discrete element model (DEM) approach

The continuum and discrete phase interaction could significantly affect the inhaled particle's transport behaviour. The accurate analysis of continuum and discrete phase interaction needs computational fluid dynamics (CFD) and Discrete Element Method (DEM) simulation, which is computationally expensive. Therefore, this study aims to develop a novel machine learning (ML) prediction model from CFD-DEM data to predict pharmaceutical aerosol transport in airways accurately. This study uses the CFD model for the continuum and DEM for the discrete phases. A soft sphere approach was used to calculate the overlap of the colliding particles. Proper validation was performed to ensure the accuracy of the present model. The CFD-DEM model analysed the particle transport in an idealised and realistic airway model, and different methods were used to analyse the transport behaviour. As the flow rate increased from 15 to 60 lpm, the deposition efficiency (DE) significantly improved due to particle interaction, rising from 20 % to 42 % without interaction and from 50 % to a remarkable 76 % with interaction, demonstrating the critical role of particle interaction in enhancing DE across varying flow rates. During the particle-particle interaction, a stagnation point and a high-pressure zone were observed at the airway model's carinal angle. Finally, a ML prediction model is developed from CFD-DEM data, which accurately predicts the pharmaceutical aerosol deposition in airways. The ML prediction model predicts the deposition pattern for different flow rates and particle sizes without CFD-DEM simulations. The present findings and more case-specific investigation would advance the knowledge of aerosol transport in airways and benefit more efficient targeted drug delivery devices.