EPJ E Colloquium - Human lungs fluid mechanics: an overview of current modelling techniques

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Published on 13 May 2026

Human breathing is governed by fluid mechanics across several regimes that span over a wide range of length and time scales: from turbulent airflow in the upper airways to slow interfacial motion in the smallest bronchioles and alveoli. At the same time, air motion is coupled to deformable tissue, mucus transport, surfactant dynamics, gas exchange, and, in disease, airway narrowing or liquid plugging. This makes the lung a demanding test case for modern multiphysics modelling.

In a new Colloquium, published in EPJ E, Francesco Romanò (Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille, France) provides an overview of current modelling approaches for human lung fluid mechanics. The colloquium first outlines the physical challenges that limit predictive simulations, including complex and patient-specific airway geometry, transitions between flow regimes, multiphase air-liquid interactions, nonlinear tissue mechanics, scarce validation data, and high computational cost. It then surveys the main classes of models used in the field: first-principle computational fluid dynamics, fluid-structure interaction models, reduced-order airway networks, poroelastic descriptions of lung tissue, thin-film models for mucus and surfactant-laden interfaces, and data-driven or machine-learning surrogates.

The article also discusses how these models can be coupled across scales, for example by linking detailed three-dimensional simulations of the proximal airways with simplified representations of the distal lung. Such frameworks are central to emerging digital lung twin concepts, with potential applications in inhaled drug delivery, pollutant exposure assessment, mechanical ventilation, and respiratory disease modelling. Future progress will require open benchmark cases, uncertainty-aware validation, and clinically interpretable indicators that connect model predictions with measurable patient outcomes.