A novel approach is devised for the quantification of the systematic uncertainty due to peak locking in particle image velocimetry (PIV), which also leads to correction of the peak-locking errors. The approach relies on a linear regression of the measured displacements from multiple Δt acquisitions (Δt being the time separation between two frames of an image pair). In presence of peak locking, the measured particle image displacement is not a linear function of Δt as the measurement error varies non-linearly with the sub-pixel particle image displacement. In the proposed approach, image acquisition is conducted with multiple Δt's, and then a linear regression is carried out among the measured time-averaged displacements at different Δt's, yielding a regression displacement. When the Δt's are selected properly, the latter represents a correction to the measured displacement where systematic errors due to peak locking are significantly diminished. The expression of the standard uncertainty in the regression displacement is provided. The regression displacement (and velocity) can also be used to quantify the systematic uncertainty due to peak locking in the measured time-averaged displacements (and velocities). The methodology is first assessed with synthetic data and then applied to planar PIV experiments on a uniform flow and an airfoil wake flow. Reference measurements with much larger Δt than the Δt's of the actual measurements (such that relative peak-locking errors are negligible for the former) are used to validate the proposed approach in both the experiments. Uncertainty coverages for the estimated uncertainties in the velocities measured in the experiments of the uniform flow and the wake flow are of 67% and 59%, respectively, which are comparable to 68% confidence level at which the uncertainties are computed. This proves the validity of the proposed multi-Δt approach for peak-locking uncertainty quantification.

A novel approach is introduced that allows the elimination of undesired laser light reflections from particle image velocimetry (PIV) images. The approach relies upon anisotropic diffusion of the light intensity, which is used to generate a background image to be subtracted from the original image. The intensity is diffused only along the edges and not across the edges, thus allowing one to preserve, in the background image, the shape of boundaries as laser light reflections on solid surfaces. Due to its ability to produce a background image from a single snapshot, as opposed to most methods that make use of intensity information in time, the technique is particularly suitable for elimination of reflections in PIV images of unsteady models, such as transiting objects, propellers, flapping and pitching wings. The technique is assessed on an experimental test case which considers the flow in front of a propeller, where the laser light reflections on the model’s surface preclude accurate determination of the flow velocity. Comparison of the anisotropic diffusion approach with conventional techniques for suppression of light reflections shows the advantages of the former method, especially when reflections need to be removed from individual images.

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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.

A survey on PIV error sources and uncertainty quantification (UQ) is performed. The aim of the survey is to understand how users and researchers in academia and industry perceive the PIV technique, especially for what concerns the measurement errors and uncertainties. A questionnaire is designed to determine the respondents’ areas of work/research, type of PIV setup they typically employ, flow properties they measure, challenges they encounter, most significant error sources and their UQ strategies. Over 100 respondents have provided valuable answers to the questions and supporting explanations. The responses are analyzed both quantitatively and qualitatively. The quantitative results are presented in form of figures, such as pie charts, bar graphs, bubble plots, and are supported by the analysis of the descriptive answers from the respondents. Overall, this work not only provides a picture of the current status of PIV perceived by the users and researchers but also highlights areas where further development is needed. To participate in the survey, follow the link- https://forms.gle/gxhD9CcKGFSBpNqW8