Si-Zhong (Simon) Lu
Simon Lu is an undergraduate engineering student at RMIT University. He has a keen interest in fluid simulation and thermodynamics and how these specializations can be utilized in the biomedical field. His goals are to help expand the limits of human knowledge and, through technology, provide solutions to the problems that humanity faces today. His other interests include programming/software development and is exploring how high speed carries affect the coasts and bays using software provided by academics.
Integrating multiple time-scales involved in arterial mass transport
The flow of blood in the cardiovascular system operates on a periodically-repeating time scale, characterised by the rhythmic contractions and expansions of the heart. Consequently flow-convected mass transport is directly influenced by these periodic fluctuations. Therefore, due to the influence of multiple time scales, of which the periodic fluctuations of the blood flow are important, it is difficult to accurately resolve the precise mass transport behaviour for long periods of time. To bypass this, many models of physiological processes do not consider pulsatile flow conditions, but rather use equivalent steady flow conditions. Unfortunately, many of these models use spatially-averaged flow profiles to obtain the equivalent steady flow field. However, because of this, temporal fluctuations are ignored by these models. To address this problem, this research project looks at the alternative approach of time-averaged flow to describe the convective flow profile. In doing so, periodic fluctuations are weighted by time, and so are manifest in the equivalent time-averaged flow profile. A mathematical model is therefore developed to capture transient physiological flow physics, and incrementally record the flow-field for time-averaging. The calculated time-averaged flow profile is then used as the convective vector field for passive scalar transport, which would represent mass transport in the cardiovascular system. This study is expected to contribute to the development of more sophisticated physiological flow models and will serve to validate existing models.