Modelling and analysis of breathing-induced lung motion in swimming performance

Abstract

Sporting was solely considered to be based on talent and physical capabilities in the past. Adaption of computer-aided engineering, particularly computational fluid dynamics, in sports-engineering has made significant advancements by developing concentrated techniques that could help individuals to reach their peak performance. Studies show that physical performance could be mainly limited by three main components, namely, muscular, cardiovascular and pulmonary systems. Due to inevitable limitations that exist in the pulmonary system, researchers are more concerned in training respiratory muscles. Inspiratory-muscle training is a well-known technique in many sports, particularly in high-intensity breathing-related sports such as rowing, cycling and swimming that require an individual’s aerobic capability and respiratory system with a high-minute ventilation to sustain exhaustive breathing scenarios. This article describes inspiratory-muscle training dynamics followed by a methodology for three-dimensional lung model to understand the interdependence of several control parameters for comparative performance, thereby generating an inverse control model for potential performance improvement criteria. The three-dimensional lung model is developed based on Visible Human Project® database, which provided detailed anatomical dimensions. The shape functions are constructed using cubic splines to fit tomographic slices. Parametric studies using variables such as pressure exerted by chest cavity on lungs, lung compliance, fluid-flow/volume, average elasticity for inspiratory-muscle training and breathing-time constants are conducted. Our hypothesis is based upon synchronising the position of lung centroids with working stroke execution, which can result in enhanced swimming performance. Simulation results showed that lung centroid could vary from 109.95E−5 to 109.99E−5 m, proving that there is a possibility to change the swimmer’s floatation angle by 0.036% during each swimming cycle. The lung centroid could be varied optimally by controlled breathing patterns. A typical swimmer with 25°–35° floatation angles can increase or decrease the floatation angles during recovery and gliding phases, respectively, by synchronising breathing patterns with the stroke during different phases of swimming cycle. Previous studies showed that the distance between lung centroid and body gravity can change up to 1.5 mm. Results obtained from our model equations showed that the change in displacement of lung centroid is 1.099 mm. Therefore, the variation of accuracy of our results is about 27% from the previous studies. However, results generated from our studies are generic, that is, results are gender independent, and the effect of body-to-lung ratio in stroke execution is not taken into consideration. These factors potentially provide a platform for further improvement in our simulation models.