Advancements in Large-Scale Volumetric PIV and PTV

Advancements in Large-Scale Volumetric PIV and PTV

Saredi, E. (TU Delft Aerodynamics)

Sciacchitano, A. (promotor)
Sciacchitano, A. (copromotor)

Delft University of Technology

2023-06-14

Particle Image Velocimetry (PIV) is considered nowadays the state-of-the-art for non-intrusive and quantitative 3D velocity measurements. Its ability to measure the velocity field around complex geometries is a valuable tool that engineers can exploit for aerodynamic design optimization in various domains, such as aerospace, wind turbines and automotive, among others. Despite recent advancements, performing a PIV measurement in the industrial environment remains challenging due to several reasons: achieving large-scale measurements, complex geometries and high Reynolds numbers. The introduction of helium-filled soap bubbles, new Lagrangian Particle Tracking (LPT) algorithms and Robotic Volumetric PIV has allowed for the measurement of large-scale volumes around complex geometries. However, despite the described advancements, large-scale PIV and LPT measurements for industrial aerodynamics require further development to accelerate their applications. The first bottleneck considered is the maximum measurable velocity. For aerodynamic flows in the transport sector, the velocity is often larger than 50 m/s when considering aircraft and race cars. To apply the mentioned techniques, acquisition frequencies higher than the one commonly available are needed. The double-frame timing strategy, characterized by image pairs with a small time separation, is detrimental to the measurement accuracy, especially when low aperture systems, such as Robotic Volumetric PIV, are considered. This research has led to the development of novel acquisition strategies (chapters 3 and 4) that improve the accuracy of double-frame velocity measurements suited for high speed applications (U∞ > 50 m/s). Another current topic of research concerns the detection of data outliers in PIV measurements, which affect their reliability and trustfulness. In this thesis (chapter 5) a novel approach to outliers detection from time-averaged three dimensional PIV data is introduced. The principle invokes the physical mechanism of turbulence transport and is based on the agreement of the measured data to the turbulent kinetic energy (TKE) transport equation. The application of this new criterium to several experimental databases shows that spurious data can be detected more easily and unambiguously as an outlier along with a low fraction of false positives. This research also attempts to  decrease the gap between Computational Fluid Dynamics’ (CFD) and experiments’ aerodynamic data. In chapter 6, the application of PIV data for data assimilation is discussed. Data assimilation is a discipline in which observation and numerical or theoretical models are combined. This can be performed with two possible aims: improving the observation with physics-based models or increasing the capability of the model to represent reality. In this thesis, the latter is considered. A novel state observer technique is investigated for the assimilation of three-dimensional velocity measurements into computational fluid dynamics simulations based on Reynolds-averaged Navier–Stokes (RANS) equations. The state observer approach locally forces the solution to comply with the reference value, with increasing benefits when the density of forced points, or forcing density, is increased.

Quantitative flow visualization
Particle Image Velocimetry
lowspeed aerodynamics
outlier detection
data assimilation

https://doi.org/10.4233/uuid:5fc6009a-d2dd-47d9-91d9-ca522d69f91d

978-94-6384-456-7

2024-04-30

Institutional Repository

doctoral thesis

© 2023 E. Saredi