State-of-art preprocessing methods for Particle Image Velocimetry (PIV) are severely challenged by time-dependent light reflections and strongly non-uniform background. In this work, a novel image preprocessing method is proposed. The method is based on the Proper Orthogonal Decomposition (POD) of the image recording sequence and exploits the different spatial and temporal coherence of background and particles. After describing the theoretical framework, the method is tested on synthetic and experimental images, and compared with well-known pre-processing techniques in terms of image quality enhancement, improvements in the PIV interrogation and computational cost. The results show that, unlike existing techniques, the proposed method is robust in the presence of significant background noise intensity, gradients, and temporal oscillations. Moreover, the computational cost is one to two orders of magnitude lower than conventional image normalization methods. A downloadable version of the preprocessing toolbox has been made available at http://seis.bris.ac.uk/~aexrt/PIVPODPreprocessing/.
This paper introduces conceptual design principles for a novel class of adaptive structures that provide both flow regulation and control. While of general applicability, these design principles, which revolve around the idea of using the instabilities and elastically nonlinear behaviour of post-buckled panels, are exemplified through a case study: the design of a shape-adaptive air inlet. The inlet comprises a deformable post-buckled member that changes shape depending on the pressure field applied by the surrounding fluid, thereby regulating the inlet aperture. By tailoring the stress field in the post-buckled state and the geometry of the initial, stress-free configuration, the deformable section can snap through to close or open the inlet completely. Owing to its inherent ability to change shape in response to external stimuli—i.e. the aerodynamic loads imposed by different operating conditions—the inlet does not have to rely on linkages and mechanisms for actuation, unlike conventional flow-controlling devices.
In this paper the problem posed by interfaces when present in PIV measurements is addressed. Different image pre-processing, processing and post-processing methodologies with the intention to minimize the interface effects are discussed and assessed using Monte Carlo simulations. Image treatment prior to the correlation process is shown to be incapable of fully removing the effects of the intensity pedestal across the object edge. The inherent assumption of periodicity in the signal causes the FFT-based correlation technique to perform the worst when the correlation window contains a signal truncation. Instead, an extended version of the masking technique introduced by Ronneberger et al. (Proceedings of the 9th international symposium on applications of laser techniques to fluid mechanics, Lisbon, 1998) is able to minimize the interface-correlation, resolving only the particle displacement peak. Once the displacement vector is obtained, the geometric center of the interrogation area is not the correct placement. Instead, the centre of mass position allows an unbiased representation of the wall flow (Usera et al. in Proceedings of the 12th international symposium on applications of laser techniques to fluid mechanics, Lisbon, 2004). The aforementioned concepts have been implemented in an adaptive interrogation methodology (Theunissen et al. in Meas Sci Technol 18:275-287, 2007) where additionally non-isotropic resolution and re-orientation of the correlation windows is applied near the interface, maximizing the wall-normal spatial resolution. The increase in resolution and robustness are demonstrated by application to a set of experimental images of a flat-plate, subsonic, turbulent boundary layer and a hypersonic flow over a double compression ramp.
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This study proposes a cross-correlation based PIV image interrogation algorithm that adapts the number of interrogation windows and their size to the image properties and to the flow conditions. The proposed methodology releases the constraint of uniform sampling rate (Cartesian mesh) and spatial resolution (uniform window size) commonly adopted in PIV interrogation. Especially in non-optimal experimental conditions where the flow seeding is inhomogeneous, this leads either to loss of robustness (too few particles per window) or measurement precision (too large or coarsely spaced interrogation windows). Two criteria are investigated, namely adaptation to the local signal content in the image and adaptation to local flow conditions. The implementation of the adaptive criteria within a recursive interrogation method is described. The location and size of the interrogation windows are locally adapted to the image signal (i.e., seeding density). Also the local window spacing (commonly set by the overlap factor) is put in relation with the spatial variation of the velocity field. The viability of the method is illustrated over two experimental cases where the limitation of a uniform interrogation approach appears clearly: a shock-wave–boundary layer interaction and an aircraft vortex wake. The examples show that the spatial sampling rate can be adapted to the actual flow features and that the interrogation window size can be arranged so as to follow the spatial distribution of seeding particle images and flow velocity fluctuations. In comparison with the uniform interrogation technique, the spatial resolution is locally enhanced while in poorly seeded regions the level of robustness of the analysis (signal-to-noise ratio) is kept almost constant.
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