No abstract

No abstract

No abstract

Zusammenfassung Stereoscopic Particle Image Velocimetry (SPIV) eignet sich als schnelles und präzises Messverfahren zur zeitlich und räumlich hochaufgelösten Ermittlung von 3D-Geschwindigkeitsverteilungen in Rohrleitungen. Die Methode wird mit dem bekannten Laser Doppler Velocimetry (LDV)-Messverfahren und Computational Fluid Dynamics (CFD)-Simulationen am Beispiel einer stark gestörten Strö-mung hinter einem Drallgenerator bewertet. Erstmalig können so SPIV-Messabweichungen bei der Volumenstrombestimmung von deutlicher kleiner als 2% an einem komplexen Strömungszustand nachgewiesen werden.Summary Stereoscopic Particle Image Velocimetry (SPIV) is a fast and precise measurement method for the investigation of 3D velocity distributions in pipes with high temporal and spatial resolutions. The method is tested in a strongly disturbed flow by comparison with Laser Doppler Velocimetry (LDV) measurements and Computational Fluid Dynamics (CFD) simulations. For the first time measurement deviations less than 2% for the flow rate determination could be proven under such complex conditions.

This study looks to find a suitable turbulence model for calculating pressure losses of ventilation components. In building ventilation, the most relevant Reynolds number range is between 3×104 and 6×105, depending on the duct dimensions and airflow rates. Pressure loss coefficients can increase considerably for some components at Reynolds numbers below 2×105. An initial survey of popular turbulence models was conducted for a selected test case of a bend with such a strong Reynolds number dependence. Most of the turbulence models failed in reproducing this dependence and predicted curve progressions that were too flat and only applicable for higher Reynolds numbers. Viscous effects near walls played an important role in the present simulations. In turbulence modelling, near-wall damping functions are used to account for this influence. A model that implements near-wall modelling is the lag elliptic blending k-ε model. This model gave reasonable predictions for pressure loss coefficients at lower Reynolds numbers. Another example is the low Reynolds number k-ε turbulence model of Wilcox (LRN). The modification uses damping functions and was initially developed for simulating profiles such as aircraft wings. It has not been widely used for internal flows such as air duct flows. Based on selected reference cases, the three closure coefficients of the LRN model were adapted in this work to simulate ventilation components. Improved predictions were obtained with new coefficients (LRNM model). This underlined that low Reynolds number effects are relevant in ventilation ductworks and give first insights for suitable turbulence models for this application. Both the lag elliptic blending model and the modified LRNM model predicted the pressure losses relatively well for the test case where the other tested models failed.