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.

Accurate modeling of air flow and aerosol transport in the alveolated airways is essential for quantitative predictions of pulmonary aerosol deposition. However, experimental validation of such modeling studies has been scarce. The objective of this study is to validate CFD predictions of flow field and particle trajectory with experiments within a scaled-up model of alveolated airways. Steady flow (Re = 0.13) of silicone oil was captured by particle image velocimetry (PIV), and the trajectories of 0.5 mm and 1.2 mm spherical iron beads (representing 0.7 to 14.6 μm aerosol in vivo) were obtained by particle tracking velocimetry (PTV). At twelve selected cross sections, the velocity profiles obtained by CFD matched well with those by PIV (within 1.7% on average). The CFD predicted trajectories also matched well with PTV experiments. These results showed that air flow and aerosol transport in models of human alveolated airways can be simulated by CFD techniques with reasonable accuracy.

Verifying numerical predictions with experimental data is an important aspect of any modeling studies. In the case of the lung, the absence of direct in-vivo flow measurements makes such verification almost impossible. We performed computational fluid dynamics (CFD) simulations in a 3D scaled-up model of an alveolated bend with rigid walls that incorporated essential geometrical characteristics of human alveolar structures and compared numerical predictions with experimental flow measurements made in the same model by Particle Image Velocimetry (PIV). Flow in both models was representative of acinar flow during normal breathing (0.82 ml/s). The experimental model was built in silicone and silicone oil was used as the carrier fluid. Flow measurements were obtained by an ensemble averaging procedure. CFD simulation was performed with STAR-CCM+ (CD-Adapco) using a polyhedral unstructured mesh. Velocity profiles in the central duct were parabolic and no bulk convection existed between the central duct and the alveoli. Velocities inside the alveoli were ∼2 orders of magnitude smaller than the mean velocity in the central duct. CFD data agreed well with those obtained by PIV. In the central duct, data agreed within 1%. The maximum simulated velocity along the centerline of the model was 0.5% larger than measured experimentally. In the alveolar cavities, data agreed within 15% on average. This suggests that CFD techniques can satisfactorily predict acinar-type flow. Such a validation ensure a great degree of confidence in the accuracy of predictions made in more complex models of the alveolar region of the lung using similar CFD techniques.

The injection of a liquid jet into a crossing Mach 6 air flow is investigated. Experiments were conducted on a sharp leading edge flat plate with flush mounted injectors. Water jets were introduced through different nozzle shapes at relevant jet-to-air momentumflux ratios. Sufficient temporal resolution to capture small scale effects was obtained by high-speed recording, while directional illumination allowed variation in field of view. Shock pattern and flow topology were visualized by Schlieren-technique. Correlations are proposed on relating water jet penetration height and lateral extension with the injection ratio and orifice diameter for circular injector jets. Penetration height and lateral extension are compared for different injector shapes at relevant jet-to-air momentumflux ratios showing that penetration height and lateral extension decrease and increase, respectively, with injector's aspect ratio. Probability density function analysis has shown that the mixing of the jet with the crossflow is completed at a distance of x/d j * 40, independent of the momentum-flux ratio. Mean velocity profiles related with the liquid jet have been extracted by means of an ensemble correlation PIV algorithm. Finally, frequency analyses of the jet breakup and fluctuating shock pattern are performed using a Fast Fourier algorithm and characteristic Strouhal numbers of St = 0.18 for the liquid jet breakup and of St = 0.011 for the separation shock fluctuation are obtained.