Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV

Adatrao, Sagar; Bertone, Michele; Sciacchitano, Andrea

doi:10.1088/1361-6501/abdcde

Cited by 10 publications

(17 citation statements)

References 32 publications

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“…However, the analysis for Y and Z velocity components show that the factor ∆t is significant also in the regions of low flow fluctuations (the results are not shown for conciseness). In that case, the factor ∆t corresponds to peak-locking error and, as observed by Adatrao et al (2021), the regions of low flow fluctuations are those where the mean velocity is affected by peak-locking errors the most. Additionally, the factor f# is associated with errors due to out-offocus particles caused by imperfect camera focussing.…”

Section: Resultsmentioning

confidence: 66%

Design of experiments: a statistical tool for PIV uncertainty quantification

Adatrao¹,

Sciacchitano²,

Velden³

et al. 2021

ISPIV21

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A statistical tool called Design of Experiments (DOE) is introduced for uncertainty quantification in particle image velocimetry (PIV). DOE allows to quantify the total uncertainty as well as the systematic uncertainties arising from various experimental factors. The approach is based on measuring a quantity (e.g. time-averaged velocity from PIV) several times by varying the levels of the experimental factors which are known to affect the value of the measured quantity. In this way, using Analysis of Variances (ANOVA), the total variance in the measured quantity can be computed and hence the total uncertainty. Moreover, the analysis provides the individual variances for each of the experimental factors leading to the estimation of the systematic uncertainties from each factor and their contribution to the total uncertainty. The methodology is assessed for an experimental test case of the flow at the outlet of a ducted Boundary Layer Ingesting (BLI) propulsor to quantify the total uncertainty in time-averaged velocity from stereoscopic PIV measurements as well as the constituent systematic uncertainties due to the experimental factors, namely, camera aperture, inter-frame time separation, interrogation window size and stereoscopic camera angle.

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Section: Resultsmentioning

confidence: 66%

Design of experiments: a statistical tool for PIV uncertainty quantification

Adatrao¹,

Sciacchitano²,

Velden³

et al. 2021

ISPIV21

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the systematic errors in PIV arise not only in the selection of the processing algorithm and the related parameters, but also during the data acquisition phase (Sciacchitano 2019). For instance, peak-locking errors were found to be dependent on the interframe time separation by Nogueira et al (2011), Legrand et al (2012), Adatrao et al (2021), among others. Because PIV-UQ algorithms do not account for the systematic error sources or account for them only partly, they give an incomplete or underestimated prediction of the total uncertainty.…”

Section: Introductionmentioning

confidence: 94%

Design of experiments: a statistical tool for PIV uncertainty quantification

Adatrao

Velden

Meulen³

et al. 2022

Meas. Sci. Technol.

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A statistical tool called Design of Experiments (DOE) is introduced for uncertainty quantification in particle image velocimetry (PIV). DOE allows to quantify the total uncertainty as well as the systematic uncertainties arising from various experimental factors. The approach is based on measuring a quantity (e.g. time-averaged velocity or Reynolds stresses) several times by varying the levels of the experimental factors which are known to affect the value of the measured quantity. Then, using Analysis of Variances (ANOVA), the total variance in the measured quantity is computed and hence the total uncertainty. Moreover, the analysis provides the individual variances for each of the experimental factors, leading to the estimation of the systematic uncertainties from each factor and their contributions to the total uncertainty. The methodology is assessed for planar PIV measurements of the flow over a NACA0012 airfoil at 15 degrees angle of attack considering five experimental factors, namely camera aperture, inter-frame time separation, interrogation window size, laser sheet thickness and seeding density. Additionally, the methodology is applied to the investigation by stereoscopic PIV of the flow at the outlet of a ducted Boundary Layer Ingesting (BLI) propulsor. The total uncertainty in the time-averaged velocity as well as the constituent systematic uncertainties due to the experimental factors, namely camera aperture, inter-frame time separation, interrogation window size and stereoscopic camera angle, are quantified.

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“…Figure 2 shows three instantaneous raw images of the three considered datasets. The three datasets have been acquired with different systems: in the 2D-wing case, a single-camera two-component PIV system has been used (Adatrao et al, 2021); the images for the near-wake of the cylinder have been acquired with four cameras placed at a large tomographic aperture (Schneiders et al, 2016); finally, the nearwake of the Ahmed body has been investigated using the Robotic Volumetric PIV system , which is composed by four cameras placed at a low tomographic angle (Saredi et al, 2020). In order to assess the performance of the turbulence transport-based principle, its results are compared to the ones obtained by the universal outlier detection, hereafter referred as UOD, proposed by Westerweel and Scarano (2005).…”

Section: Experimental Assessmentmentioning

confidence: 99%

Physics-based universal outlier detector for flow statistics

Saredi¹,

Sciacchitano²,

Scarano³

2021

ISPIV21

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Outlier detection for PIV velocity fields is still nowadays an active field of research. In the last decades, several image pre-processing and processing algorithms have been developed aiming at increasing the dynamic velocity range of PIV measurements and reducing the measurement uncertainty. Nevertheless, PIV velocity fields are still often characterised by the presence of outliers, which potentially hamper the correct interpretation of the flow physics and negatively affect the evaluation of the flow statistics. The outlier detection strategies presented in literature are mainly based on the statistical analysis of the velocity vector with its immediate neighbour. Most of these algorithms have been demonstrated to be effective for instantaneous flow fields, where the errors associated with the outliers are order of magnitude larger than the expected measurement uncertainty. However, these approaches are not as effective for the flow statistics, where the outliers yield errors of the same order of the measurement uncertainty. To overcome this limitation, this paper proposed an outlier detection approach based on the agreement of the flow statistics to the constitutive equations, more specifically to the turbulent kinetic energy (TKE) transport equation. The focus is posed on the ratio between the local advection terms of TKE and a robust estimation of the TKE production along the local streamline. It is demonstrated that, in presence of outliers, the proposed principle yields a clear separation between the correct and the erroneous vectors. In order to assess the performance of the proposed principle, three different test cases are considered. For all of them, the results are compared with a reference outlier detection methodology, namely the universal outlier detection method proposed by Westerweel and Scarano (2005). The proposed turbulence transport-based approach exhibits higher performance in terms of percentage of outliers correctly identified in the flow statistics.

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Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV

Cited by 10 publications

References 32 publications

Design of experiments: a statistical tool for PIV uncertainty quantification

Design of experiments: a statistical tool for PIV uncertainty quantification

Design of experiments: a statistical tool for PIV uncertainty quantification

Physics-based universal outlier detector for flow statistics

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