ChemEng.Upatras Seminars 2022 - George Tatsios (University of Edinburgh (UK))
Abstract
In recent years an effort is underway for the miniaturization of devices and processes which are considered to be, compared to their normal sized counterparts, more reliable and efficient with faster response and less expensive. Due to this a clear need arises for micro pumping with varying design specifications, in terms of flow rate and pressure difference, depending on the specific application. Knudsen type thermally driven pumps, with no moving parts, which operate based on non-equilibrium phenomena that are prevalent in conditions far from local equilibrium are of particular interest. Thermally driven pumps are modelled based on kinetic theory on the basis of the Boltzmann equation or suitable kinetic models and the characteristic curves of various pump design are obtained. Following, the potential implementation of thermally driven pumps for the actuation of heat pumps with no moving parts free from electricity and for binary mixture separation are presented.
Speakers Short CV
Giorgos Tatsios graduated from the Department of Mechanical Engineering of the University of Thessaly in 2014 and pursued graduate studies in the same department, where he was awarded the degrees of M.Sc. and Ph.D. in Mechanical Engineering in 2015 and 2019 respectively. Upon obtaining his Ph.D. he continued as a postdoctoral researcher in the same department until late 2021. During his Ph.D. studies and postdoctoral appointment, he conducted research in the field of computational rarefied gas dynamics, studying various interesting and counterintuitive phenomena that arise in highly rarefied gases, as well as a number of applications ranging from flows encountered in MEMS to flows in large vacuum pumping systems. To study the above phenomena well established stochastic (DSMC) and deterministic (DVM) kinetic modelling, as well as moment methods were used. Currently Giorgos is a Post Doctoral Research Associate in the Institute for Multiscale Thermofluids, School of Engineering, University of Edinburgh conducting research in the field of multiscale modelling of rarefied gas flows using a physics informed machine learning approach.