Publications

We investigate the interface dynamics in an unsteady quasi-capillary channel flow. The configuration consists of a liquid column that moves along a vertical 2D channel, open to the atmosphere and driven by a controlled pressure head. Both advancing and receding contact lines were analyzed to test the validity of classic models for dynamic wetting and to study the flow field near the interface. The operating conditions are characterized by a large acceleration, thus dominated by inertia. The shape of the moving meniscus was retrieved using Laser-Induced Fluorescence-based image processing, while the flow field near was analyzed via Time-Resolved Particle Image Velocimetry (TR-PIV). The TR-PIV measurements were enhanced in the post-processing, using a combination of Proper Orthogonal Decomposition and Radial Basis Functions to achieve super-resolution of the velocity field. Large counter-rotating vortices were observed, and their evolution was monitored in terms of the maximum intensity of the Q-field. The results show that classic contact angle models based on interface velocity cannot describe the evolution of the contact angle at a macroscopic scale. Moreover, the impact of the interface dynamics on the flow field is considerable and extends to several capillary lengths below the interface.

A liquid metal flow in the form of a submerged round jet entering a square duct in the presence of a transverse magnetic field is studied experimentally. A range of high Reynolds and Hartmann numbers is considered. Flow velocity is measured using electric potential difference probes. A detailed study of the flow in the duct's cross-section about seven jet's diameters downstream of the inlet reveals the dynamics, which is unsteady and dominated by high-amplitude fluctuations resulting from the instability of the jet. The flow structure and fluctuation properties are largely determined by the value of the Stuart number N. At moderate N, the mean velocity profile retains a central jet with three-dimensional perturbations increasingly suppressed by the magnetic field as N grows. At higher values of N, the flow becomes quasi-two-dimensional and acquires the form of an asymmetric macrovortex, with high-amplitude velocity fluctuations reemerging.

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big data is a concept currently in its infancy which requires further development. It is of special importance if the aim is a prediction that is good in a statistical sense or if the vector fields should be predicted as good as possible. For this purpose, the statistical and deterministic prediction of the unsteady but periodic flow of the von Kármán Vortex Street (KVS) was examined using an Echo State Network (ESN) which is well suited for learning from time series due to its recurrent connections. The experimental data of the velocity field of the KVS were collected by Particle Image Velocimetry (PIV). Then, the data were reduced by Proper Orthogonal Decomposition (POD) and the flow was reconstructed by the first hundred most energetic modes. An ESN with 3000 neurons was optimized with respect to its three main hyperparameters to predict the time coefficients of the POD modes. For the deterministic prediction, the aim was to maximize the correct direction of the vertical velocities. The results indicate that the ESN can mimic the periodicity and the unsteadiness of the flow. It is also able to predict the sequence of the upward and downward directed velocities for longer time spans. For the statistical prediction, the similarity of the probability density functions of the vertical velocity fields between the predicted and actual flow was achieved. The leaking rate of the ESN played a key role in the transition from deterministic to statistical predictions.

Microfluidic devices are governed by three-dimensional velocity and temperature fields, and their boundary conditions are often unknown. Therefore, a measurement technique is often desired to measure both fields in a volume. With astigmatism particle tracking velocimetry (APTV) combined with luminescence lifetime imaging, the temperature and all velocity components in a volume can be measured with one optical access. While the three-dimensional particle position is determined by evaluating the shape of the corresponding particle image, the temperature measurement relies on estimating the temperature-dependent luminescence lifetime derived from particle images on two subsequent image captures shortly after the photoexcitation. For this, typically a high-energetic pulsed laser is required to ensure a high signal-to-noise ratio. However, it can also cause additional heating of the fluid. We show that this problem is solved by replacing the pulsed laser with an LED. To compensate for the lower power provided by the LED, we adapted the timing schedule and vastly extended the illumination time and the exposure time for both image captures. In addition, we were able to replace the typically used high-speed camera with an ordinary double-frame camera. In this way, very low measurement uncertainties on all measured quantities can be achieved while keeping the temperature of the fluid unaffected. Random errors dominate within the two focal planes of APTV, yielding a standard deviation of the temperature of individual particles of about 1 only. The measurement error caused by the movement of tracer particles during the much longer illumination and exposure time were found to be acceptable when the measured velocity is low. With the circumvention of light-source induced heating and the lower cost of hardware devices, the adapted approach is a suitable measurement technique for microfluidic related research.