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Computational Fluid Dynamics and Machine Learning Algorithms Analysis of Striking Particle Velocity Magnitude, Particle Diameter, and Impact Time Inside an Acinar Region of Human Lung

Computational fluid dynamics (CFD) simulations with machine learning Algorithms (MLA) is becoming increasingly popular as the combination reduces the computational time of the CFD simulations required for classifying, predicting, or optimising the impact of geometrical and physical variables of a specific study. The main target of drug delivery studies is predicting the optimum particle diameter for targeting particular locations in the lung to achieve a desired therapeutic effect. In addition, the main goal of molecular dynamics studies is investigating particle-lung interaction through given particle properties. Therefore, this study combines the two by numerically determining the optimum particle diameter required to obtain an ideal striking velocity magnitude and impact time inside an acinar part of the lung. At first, the striking velocity magnitudes and time for impact (two independent properties) of three different particle diameters () are computed using CFD simulations. Then, machine learning classifiers determine the particle diameter corresponding to these two independent properties. In this study, two cases are compared: A healthy acinus where a surfactant layer covers the inner surface of the alveoli providing low air-liquid surface tension (LST) values), and a diseased acinus where only a water layer covers the surface causing high surface tension (HST) values ). Results showed that all three particle diameters attain larger velocities and smaller impact times in a diseased acinus than in a healthy one. In both cases, the particles acquire the smallest velocities and longest impact times, and the particles possess the largest velocities and shortest impact times.