Diploma and Master Thesis
Development of the data base system for »quasi« smart prediction of two-phase flow patterns in any selected system
The ability to predict two-phase flow patterns and transitions between them is one of the key research directions in the world scale. The approach to date was predominantly based on empirical flow pattern maps derived from the experimental research. Consequently, such maps are limited by the application and require the extrapolation of experimental results, which in most cases is not reliable. The solution is to develop new tools based on the flow pattern prediction utilizing the first principles of fluid dynamics and the description of two-phase flow, taking into account the nature of several scales. The long-term goal is to deepen the knowledge about the flow pattern development and to identify the key emergent parameters that would be helpful to build a general tool for the flow pattern estimation.
The task of the thesis is to establish a database system that would allow the systematic collection of experimental and numerical data from various research groups that are working within the ViR2AL Institute. The system should be scalable and adaptive, so it could combine data for different configurations of flow systems at a wide array of process parameters. Finally, the system should allow the storage of key two-phase flow parameters at the appropriated scales.
Contact person: Assist. Prof. PhD. Matjaž PerparWebpage of the laboratory: https://www.fs.uni-lj.si/en/faculty_of_mechanical_engineering/about_faculty/departments_and_laboratories/laboratories/2005012810071882/
