09:40   PIV/PTV methods and applications III Chair: Theo Michelis Research on Combustion Mode Identification Method for Ramjet Based on Particle Image Velocimetry Guang Chang, Hongjie Zhong, Guannan Men Abstract: To address the testing requirements for the internal flow fields in scramjet engines, a combustion mode identification method based on particle image velocimetry (PIV) technology was developed, employing aluminum oxide powder as tracer particles with a high-frequency pulsed laser and high-speed camera serving as the excitation source and detector respectively, enabling measurements of both transient and time-averaged velocity field distributions in different combustion modes within the scramjet engine. Experimental results demonstrated that the airflow Mach number at the engine inlet could be derived from the inflow total temperature combined with PIV measurements, achieving reliable combustion mode identification whose accuracy was validated by conventional pressure-based recognition methodologies. 360° volumetric LPT measurements around a robotically actuated model of a wing Thomas Rockstroh, Alex Nila Abstract: The aerodynamics of flapping flight are very intricate and the evolving vortex systems are complex in a spatial as well as in a temporal sense [1]. In order to fully capture such flow scenarios in an experimental study, three-dimensional and time-resolved flow measurements are required alongside the measurement of the flapping insect/ wing/ model to further enhance insights into the fluid-structure-interaction. Up until recently, many volumetric measurements were limited to or solely focused on measuring regions in the wake of aerodynamic models due to negative impacts of optical obstructions during the reconstruction step. In order to overcome such limitations, a novel object-aware extension to 3D Lagrangian particle tracking methods – OA-LPT – was proposed by Wieneke and Rockstroh [2]. With OA-LPT it becomes possible to inform the particle reconstruction step about the presence of a view-obstructing object. As a result, only camera perspectives for the reconstruction of a particle are considered, which are physically able to see a particle and therefore minimizing reconstruction artifacts. In the initial paper by Wieneke and Rockstroh, only static objects were considered but in the meantime further developments have been made to support dynamic objects as well. With this contribution, we want to present an experimental set-up and first measurement results to demonstrate the potential of applying this novel method in the research context of flapping flight aerodynamics. On the flow behind the F1 Imperial Front Wing, comparison between volumetric PTV and numerical simulations. Isabella Fumarola, Alexandra I. Liosi, Parv Khurana, Isaac Balbolia, Adam Meziane, Spencer J. Sherwin, Jonathan Morrison Abstract: Abstract The recent development of Particle Tracking Velocimetry (PTV) for large-volume investigation in air has made this technique particularly well-suited to validate numerical simulations of complex aerodynamic configurations. At the same time, Large Eddy Simulations (LES) using high-order spectral h/p element methods have proven to be a powerful tool for investigating flows around complex geometries at high Reynolds (Re) numbers. In a recent study Liosi et al. [1] have successfully implemented this numerical methodology to analyse the flow behind a Formula 1 wing. However, the validation of their simulation was based on limited experimental data, mostly pointwise velocity measure, pressure surveys, or a limited number of PIV planes. In this work, the advantages of using volumetric PTV to validate numerical simulations is explored by presenting an experiment on the Imperial Front Wing (IFW) of the same geometry used by Liosi et al. [1]. The test was performed in the 10x5 wind tunnel over a moving ground (rolling road). Results show excellent agreement with the numerical prediction revealing new insights into the development of the vortical structures shed from the different wing elements. The work offers a unique dataset of significant value for the Formula 1 community. Extended Abstract attached.

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