Assimilation of tomographic particle velocimetry data of turbulent mixed convection in a cuboidal cell

Abstract

Data assimilation, i.e. applying numerical algorithms on experimental data to extract additional information, has become an increasingly important topic in the field of fluid dynamics and turbulence research in the past decades (di Leoni et al. 2020). Although tomographic particle image velocimetry (tomo PIV) allows to capture largely temporally and spatially resolved, three-dimensional velocity fields of turbulent flow, usually not all essential parts of the flow problem can be measured. Due to the reflection of the laser light sheet and low tracer particle densities near solid walls, boundary layers are often difficult to capture using PIV. In addition, turbulent small-scale structures are often filtered out, since velocity vectors represent surface- or volume-averaged quantities obtained from a correlation step. Moreover, for fluid problems involving thermal convection, the temperature field––next to the velocity field––is essential for the flow problem. However, to capture the temperature field simultaneously with the velocity field requires additional elaborate measurement techniques which are often not feasible.