Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected by computational details such as image pre-conditioning, sub-pixel peak estimators, data validation procedures, interpolation algorithms and smoothing methods. The accuracy of several algorithms was determined and the best performing methods were implemented in a user-friendly, GUI based open-source tool for performing DPIV flow analysis in MATLAB.

PIVlab is a free toolbox and app for MATLAB ® . It is used to perform Particle Image Velocimetry (PIV) with image data: A light sheet illuminates particles that are suspended in a fluid. A digital camera records a series of images of the illuminated particles. The input images are divided into sub-images (interrogation areas), and for each of these, a cross-correlation is performed. The resulting correlation matrix is used to estimate the most probable displacement within each interrogation area. PIV is extensively used for flow analyses where a thin laser sheet illuminates suspended particles in the fluid, but also for other moving textures, like cell migration or ultrasonic images. This paper presents several improvements that were implemented in PIVlab, enhancing the robustness of displacement estimates. The benefit of these improvements is evaluated using experimental images and synthetic images of particle and non-particle textures. Linear correlation and repeated correlation increase the robustness and decrease bias and root-mean-square (RMS) error of the displacement estimates. Particle images have a significantly lower bias and RMS error than non-particle images.

In aircraft propellers that are used to propel aircraft forward at some speed, propeller blade twist is important to make the individual propeller 'wings' operate at a relatively constant effective angle of attack over the full span. Wing twist is sometimes also assumed to be essential in flapping flight, especially in bird flight. For small insects, it has however been shown that wing twist has little effect on the forces generated by a flapping wing. The unimportance of twist was attributed to the prominent role of unsteady aerodynamic mechanisms. These were recently also shown to be important in bird flight. It has therefore become necessary to verify the role of wing twist in the flapping flight of birds. The aim of the study is to compare the efficiency and the aerodynamic forces of twisted and non-twisted wings that mimic the slow-speed flapping flight of birds. The analyses were performed by using physical models with different amounts of spanwise twist (0°, 10°, 40°). The flow was mapped in three-dimensions using digital particle image velocimetry. The spanwise circulation, the induced drag, the lift-to-drag ratio and the span efficiency were determined. Twist and Strouhal number (St) both determine the local effective angles of attack of the flapping wing. Wings with low average effective angles of attack (resulting from high twist and/or low St) are more efficient, but generate significantly lower aerodynamic forces. High average effective angles of attack result in lower efficiency and high aerodynamic forces. Efficiency and the magnitude of aerodynamic forces are competing parameters. Wing twist is beneficial only in the cases where efficiency is most important-e.g. in cruising flight. Take-off, landing and maneuvering, however, require large and robust aerodynamic forces to be generated. The additional force comes at the cost of efficiency, but it enables birds to perform extreme manoeuvres, increasing their overall fitness.