Practical trainings, student assistants and theses

Numerical simulation of particles in rising gas bubbles (Id 356)

Student practical training

The separation of aerosol particles by a moving gas-liquid fluidic interface is central to a wide variety of industrial and natural applications, among which stand out air purification systems and precipitation scavenging. The particle size significantly affects the separation rate. The diffusion of particles in the nanometer range is largely dominated by molecular diffusion. In this regime, predictive models accurately estimate the separation rates. Model inaccuracy increases, however, significantly when the particle size ranges from 0.1 μm to 2.5 μm. In this impaction-dominated regime, the complex interplay between the flow dynamics on both sides of the fluidic interface and the particle inertia makes it difficult to develop suitable models.
In this work, the student will numerically investigate whether enforcing bubble deformation into a non-spherical shape leads to a higher deposition rate, hereby making the particle separation process more efficient. The results will lead to the development of an improved and reliable separation model accounting for the deformation of the fluidic interface and the associated flow changes.

Department: Experimental Thermal Fluid Dynamics

Contact: Maestri, Rhandrey, Dr. Lecrivain, Gregory

Requirements

  • General interest in fluid mechanics
  • Preliminary experience in code development (C++) is desirable
  • Good written and oral communication skills in either English or German

Conditions

  • Either an immediate start or a start in 2022 is possible
  • Duration of the internship is anticipated to be 6 months but can be modified according to study regulations
  • Remuneration according to HZDR internal regulations

Online application

Please apply online: english / german

Druckversion


Motion tracking of autonomous sensor particles in industrial vessels (Id 335)

Master theses / Diploma theses / Compulsory internship

Foto: AutoSens_StirredReactor ©Copyright: fwdf (Mailgruppe)Data acquisition in large industrial vessels such as biogas fermenters or wastewater treatment plants is limited to local measurement points due to limited access to the vessel and the non-transparency of the fluid. To optimize these kinds of plants, the three-dimensional flow field and the spatial distribution of fluid properties such as temperature and electrical conductivity inside the vessel must be known. This can be achieved by the autonomous flow-following sensor particles developed by the HZDR. Equipped with a pressure sensor, an accelerometer, two gyroscopes and a magnetometer, the sensor particle can track the movement inside the vessels and derive the flow field from that. Additionally, the sensor particle gets position information by an ultra-wide-band based localization module (like GPS) as soon as it is on the fluid surface. The motion of the sensor particle is currently tracked with an error-state Kalman filter and yields a reliable tracking of the velocity and position, respectively. However, the tracking time is limited by the propagation of uncertainties of the inertial sensors through the filter. The objective of this master thesis is to extend this tracking time by the use of more advanced tracking algorithms like particle filter or other types of Kalman filters. This includes the following tasks:

  • Literature review of advanced filters for motion tracking
  • Theoretical comparison and implementing the most promising algorithm in Python
  • Verification and performance analysis based on experimental data

Department: Experimental Thermal Fluid Dynamics

Contact: Buntkiel, Lukas, Dr. Reinecke, Sebastian

Requirements

  • Studies in the area of electrical, mechatronic, mechanical engineering or similar
  • Basics of measurement uncertainty, digital signal processing
  • Data analysis in Python
  • Independent and structured way of working

Conditions

  • Start possible at any time
  • Duration according to the respective study regulations

Links:

Online application

Please apply online: english / german

Druckversion