We introduce two new singularity detection criteria based on the work of Duchon-Robert (DR) [J. Duchon and R. Robert, Nonlinearity, 13, 249 (2000)], and Eyink [G.L. Eyink, Phys. Rev. E, 74 (2006)] which allow for the local detection of singularities with scaling exponent h 1/2 in experimental flows, using PIV measurements. We show that in order to detect such singularities, one does not need to have access to the whole velocity field inside a volume but can instead look for them from stereoscopic particle image velocimetry (SPIV) data on a plane. We discuss the link with the Beale-Kato-Majda (BKM) [J.T. Beale, T. Kato, A. Majda, Commun. Math. Phys., 94, 61 (1984)] criterion, based on the blowup of vorticity, which applies to singularities of Navier-Stokes equations. We illustrate our discussion using tomographic PIV data obtained inside a high Reynolds number flow generated inside the boundary layer of a wind tunnel. In such a case, BKM and DR criteria are well correlated with each other.

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Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.

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In the present work, a standard large eddy simulation is combined with tracer particle seeding simulations to investigate the different PIV bias errors introduced by intermittent particle seeding and particle lag. The intermittency effect is caused by evaluating the velocity from tracer particles with inertia in a region where streams mix with different seeding densities. This effect, which is different from the vastly-discussed particle lag, is frequently observed in the literature but scarcely addressed. Here, bias errors in the velocity are analysed in the framework of a turbulent annular gaseous jet weakly confined by low-momentum co-flowing streams. The errors are computed between the gaseous flow velocity, obtained directly from the simulation, and the velocities estimated from synthetic PIV evaluations. Tracer particles with diameters of 0.037, 0.37 and 3.7 µm are introduced into the simulated flow through the jet only, intermediate co-flowing stream only and through both regions. Results quantify the influence of intermittency in the time-averaged velocities and Reynolds stresses when only one of the streams is seeded, even when tracers fulfil the Stokes-number criterion. Additionally, the present work proposes assessing unbiased velocity statistics from large eddy simulations, after validation of biased seeded simulations with biased PIV measurements. The approach can potentially be applied to a variety of flows and geometries, mitigating the bias errors.

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