A novel approach is introduced to enlarge the range of measurable velocities by PTV systems. The approach relies upon the acquisition of two or more sets of double-frame images with increasing pulse separation time Δt. The underlying principle is that measurements with a short Δt yield a velocity field with high percentage of valid vectors, but low measurement precision. Conversely, the measurements with longer Δt potentially offer a higher measurement precision but suffer from an increased probability of spurious particle pairing. Their combination is shown possible making use of Reynolds decomposition to form a predictor for the mean displacement and its statistical dispersion. The time-averaged velocity field produced with a short Δt is used as predictor to set the expected average displacement. Moreover, the extent of the search region is based on the estimate of the velocity fluctuations from the evaluation at short Δt. The algorithm can be applied progressively, increasing the pulse separation till truncation errors are found to limit the accuracy of the measurement. An experiment on the near wake of the Ahmed body performed with the Robotic Volumetric PIV system is used to assess the performance of the proposed method, which is compared with reference data from multi-frame measurements based on the Shake-the-Box (STB) algorithm. Results are firstly evaluated in terms of velocity pdf along the in-plane and coaxial directions. Furthermore, the vorticity field obtained by the different methods is compared.
The occurrence of data outliers in PIV measurements remains nowadays a problematic issue; their effective detection is relevant to the reliability of PIV experiments. This study proposes a novel approach to outliers detection from time-averaged three-dimensional PIV data. The principle is based on the agreement of the measured data to the turbulent kinetic energy (TKE) transport equation. The ratio between the local advection and production terms of the TKE along the streamline determines the admissibility of the inquired datapoint. Planar and 3D PIV experimental datasets are used to demonstrate that in the presence of outliers, the turbulent transport (TT) criterion yields a large separation between correct and erroneous vectors. The comparison between the TT criterion and the state-of-the-art universal outlier detection from Westerweel and Scarano (Exp Fluids 39:1096–1100, 2005) shows that the proposed criterion yields a larger percentage of detected outliers along with a lower fraction of false positives for a wider range of possible values chosen for the threshold. Graphical abstract
This work proposes a new multi-step measurement algorithm for PTV to cope with high flow velocities that do not allow time-resolved measurements. The proposed methodology relies on an iterative process, whereby a double-frame single-pulse image acquisition is followed by a multi-exposure acquisition. The novelty of the approach lies in the adaptive selection of the time separation used in the second acquisition to maximise the measurement dynamic velocity range and minimise the occurrence of overlapping particle images. The adaptive selection is performed based on the velocity field obtained by the first double-frame single-pulse acquisition and aims to obtain a rather uniform particle displacement across the measurement volume. The proposed methodology is demonstrated based on flow measurements in the near-wake of a truncated cylinder at Re=33,000. The results obtained by the presented method are then compared to those retrieved via the state-of-the-art PTV algorithm Shake-The-Box, a double-frame single-exposure strategy and a standard double-frame double-exposure strategy with a fixed pulse separation time. The increment of the particle displacement in the regions characterized by low velocity leads to a reduction of the relative uncertainty of the evaluated velocity and a consequent increase of the achievable dynamic velocity range. Among the double-frame strategies, the requirement to allow PTV measurement at a relatively high speed, the proposed methodology achieves the lowest error with respect to the reference, given by STB measurement in this case.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.