Flows are everywhere around us. Both in nature and industry flows often involve multiple phases (gas, liquid or solid). This can be a wave on a water surface, but often there is a continuous phase that contains small droplets, bubbles or solid particles. Examples of these so-called dispersed multiphase flows are sand in rivers, oil droplets in contaminated water, catalyst particles in chemical reactors, or red blood cells in blood. Being able to predict these flows is essential for more efficient and sustainable production processes, such as the processing of raw materials, food production or the production of energy.
While we know the exact equations that describe these flows, we cannot solve them analytically. Computer simulations are possible, but demand enormous computational efforts, even for relatively simple problems. The only solution for applied flow studies is therefore a prediction based on simplified models. However, these models lack a solid underpinning, predominantly due to a lack in good experimental data: these flows are difficult to characterize, because they turn opaque even at modest volume loads. Current optical techniques (based on lasers and cameras) will thus no longer work. By making use of non-optical imaging techniques, generally borrowed from a medical setting, we can see in these opaque flows. For instance, imaging based on ultrasound, magnetic resonance, and X-ray will allow us to determine velocities and concentrations – we can clarify these opaque flows.
In this project, we developed and applied non-optical measurement techniques to investigate opaque flows. Three benchmark flows were selected that contain interesting scientific questions, but could also be used to further refine the techniques: particle-laden pipe flow, Taylor-Couette flow, and a cavitating venturi. For each case, we combined the (complementing) data that we obtained from various techniques. This provided an unprecedented insight in each of these flows, so that we now have a better understanding. As some techniques had partial overlap in the data that they provided, we could also refine the measurement methodology. Furthermore, the data will be instrumental in validating numerical simulations, so that industrial applications can reliably be predicted in te future.
