KIVI Hoogendoorn Fluid Mechanics Award 2022
It is great pleasure to announce that the recipient of the KIVI Hoogendoorn Fluid Mechanics Award for the best PhD thesis defended in the academic year 2021-2022 is:
Dr. ir. Willian Hogendoorn
He defended his PhD thesis (with Cum Laude distinction) on 15th December 2021 at Delft University of Technology, with promotors prof. dr. ir. C. Poelma and dr. Ir. W.P. Breugem. The thesis is entitled: “Suspension dynamics in transitional pipe flow”. The Award Ceremony will take place at the upcoming Burgers Symposium on 31 May – 1 June 2023 in Lunteren, where the recipient will also give a presentation on his thesis work.
Thesis summary: Suspension flows are abundantly present in nature and industry. Typical examples include volcanic ash clouds, sediment transport in rivers, blood flow through human capillaries and dredging. Accurate models of suspension flows are of key importance for prediction, optimisation and control of particle-laden flows, especially in industrial applications. However, accurate experimental reference data are hardly available for the development and validation of these models. The opaque nature of suspension flows precludes the acquisition of quantitative flow information by means of established optical measurement techniques. Therefore, in this dissertation measurements are performed using state-of-the-art measurement techniques, which provide insight in particle-laden flows. These measurement techniques include ultrasound, magnetic resonance and optical imaging. The high-quality data, obtained using these measurement modalities, are subsequently be used for the modelling of suspension flows. The aim of this dissertation was to study the effect of the particle size and concentration on the behaviour of pipe flow, in particular in the laminar-turbulent transition region. A completely new transition scenario from laminar to turbulent flow was discovered for certain combinations of volume fraction and particle size. A simple parameter is introduced that predicts the transition scenario (classical, intermediate, or particle-induced); this parameter is validated using own data and literature data. This will be a convenient parameter to predict the behaviour of a wide range of industrial processes.