ANSYS‐MATLAB co‐simulation of mucus flow distribution and clearance effectiveness of a new simulated cough device

Abstract

Coughing is an irritable reaction that protects the respiratory system from infection and improves mucus clearance. However, for the patients who cannot cough autonomously, an assisted cough device is essential for mucus clearance. Considering the low efficiency of current assisted cough devices, a new simulated cough device based on the pneumatic system is proposed in this paper. Given the uncertainty of airflow rates necessary to clear mucus from airways, the computational fluid dynamics Eulerian wall film model and cough efficiency (CE) were used in this study to simulate the cough process and evaluate cough effectiveness. The Ansys-Matlab co-simulation model was set up and verified through experimental studies using Newtonian fluids. Next, model simulations were performed using non-Newtonian fluids, and peak cough flow (PCF) and PCF duration time were analyzed to determine their influence on mucus clearance. CE growth rate (λ) was calculated to reflect the CE variation trend. From the numerical simulation results, we find that CE rises as PCF increases while the growth rate trends to slow as PCF increases; when PCF changes from 60 to 360 L/min, CE changes from 3.2% to 51.5% which is approximately 16 times the initial value. Meanwhile, keeping a long PCF duration time could greatly improve CE under the same cough expired volume and PCF. The results indicated that increasing the PCF and PCF duration time can improve the efficiency of mucus clearance. This paper provides a new approach and a research direction for control strategy in simulated cough devices for airway mucus clearance.

Coupled discrete phase model and Eulerian wall film model for numerical simulation of respiratory droplet generation during coughing

“…The generated droplets were then tracked by using the DPM. The coughing airflow was assumed to be an isothermal and incompressible ideal gas with a density and viscosity of 1.185 kg/m 3 and 1.81 × 10 −5 Pa, respectively 38 . The governing equations for the airflow were the equation of continuity and the Navier–Stokes equations.…”

Dynamics of cough and particulate behaviour in the human airway

“…The presence of mucus is represented in the present biomechanical model through a wall restitution coefficient. Specifically, the rebound velocity relative to the wall is determined by the impingement velocity and a restitution coefficient thus: u p;rebound ¼ e t u p;impinged (15) u p;rebound ¼ e n u p;impinged (16) where e t and e n denote the restitution coefficients in the wall-normal and tangential directions respectively. In the present study, these restitution coefficients were set to 0.1 to allow for a small amount of stickiness at the surface of the airway [40].…”

“…These efforts have led to the development of computational models in order to characterize the interaction between respiratory airflow and the surrounding soft tissue surfaces of the airway [11,12], producing numerical simulations of these flows [1,13]. Some studies have focused on the role of the mucus layer in airway clearance [14][15][16][17][18]. Most of these studies assumed rigid walls and used approximated airway geometry despite the compliant nature of the human airways [18].…”

A Novel Method to Evaluate Patient-Ventilator Synchrony during Mechanical Ventilation

“…For example, during the treatment of COVID-19, PVA occurs in most patients, which increases the need for sedation and muscle relaxants [16,17]. In addition, the respiratory gland is stimulated, which increases the secretion of sputum and causes sputum stasis, resulting in difficulty in suction [18][19][20]. erefore, the research and clinical evaluation of patient-ventilator synchrony are vital for the process of mechanical ventilation.…”