A method is proposed to obtain full-domain spatial modes based on proper orthogonal decomposition (POD) of particle image velocimetry (PIV) measurements taken at different (overlapping) spatial locations. This situation occurs when large domains are covered by multiple non-simultaneous measurements and yet the large-scale flow field organization is to be captured. The proposed methodology leverages the definition of POD spatial modes as eigenvectors of the spatial correlation matrix, where local measurements, even when not obtained simultaneously, provide each a portion of the latter, which is then analyzed to synthesize the full-domain spatial modes. The measurement domain coverage is found to require regions overlapping by 50–75% to yield a smooth distribution of the modes. The procedure identifies structures twice as large as each measurement patch. The technique, referred to as Patch POD, is applied to planar PIV data of a submerged jet flow where the effect of patching is simulated by splitting the original PIV data. Patch POD is then extended to 3D robotic measurement around a wall-mounted cube. The results show that the patching technique enables global modal analysis over a domain covered with a multitude of non-simultaneous measurements.

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. When performing robotic volumetric Particle Image Velocimetry (PIV) (Jux et al., 2018), the large-scale mean velocity fields are estimated merging several measurements performed at different (adjacent) locations covered by the robot sequence. The proposed methodology leverages the definition of POD modes as eigenvectors of the spatial correlation matrix to obtain also large-scale modes. Performing measurements over overlapping (50-75%) regions allows to approximate the correlation matrix in the two adjacent domains. When applied over a sequence of views, this method has the potential to deliver full-domain POD modes spanning the volume covered by the robot sequence, even if different regions are covered asynchronously. This methodology is particularly well-suited for applications that seek to investigate large-scale flow structures, whenever the dynamic spatial range (DSR) of the measurement system does not allow to capture the whole domain at once. The methodology is validated using a 2D experimental dataset of a turbulent boundary layer, where patches are artificially created from splitting the PIV measurements, later used as ground truth to assess the results. Furthermore, we apply the technique to a 3D robotic volumetric PIV experiment of the flow around a wall-mounted cube.