Experimental analysis of particle sizes for PIV measurements

Abstract

The right choice of seeding particles strongly influences the outcome of a particle-image velocimetry (PIV) measurement. Particles have to scatter enough light to be seen by cameras and follow the flow faithfully. As the flow following behavior depends on the inertia and therefore the size of the particle, smaller particles are desirable. Unfortunately, larger particles possess better light scattering behavior, which is especially important for volumetric PIV measurements. In this paper, the particle response of two exemplary solid particles to an oscillatory air flow created by a piston movement is analyzed and compared to analytic results by Hjelmfelt and Mockros (1966 Appl. Sci. Res. 16 149-61) concerning phase lag and amplitude ratio between particle movement and flow field. To achieve realistic experimental boundary conditions, polydispersed particles are used for the analysis. The analytic results show a strong dependence on the diameter. That is, using the volumetric mean diameter an overestimation of the phase lag of the particles is determined, whereas an underestimation of phase lag is computed for the number mean diameter. Hence, for polydispersed particles a more general analysis than that based on the particle mean diameter is required to determine in detail the particle following behavior.

11th International Symposium on Particle Image Velocimetry (PIV 2015)

“…Thus, PIV 2015 served as a venue for dissemination of the latest measurements and analysis advances meant to improve the quality and resolution of the data as well as more robust quantification of the inherent uncertainties associated with PIV data sets. Many of the conference contrib utions focused on improving the quantity and/or quality of data acquired (increased dimen sionality, improved spatial and temporal resolution, new measurement methodologies) [3, 4, 8-11, 17, 19, 21], development of more accurate and/or efficient data reduction methodolo gies [1,6,7,[13][14][15][16]22], and the development of robust uncertainty quantification methods meant to more objectively assess and document measurement uncertainties for verification and validation purposes [2,5,12,18,20]. The contributions included in this special feature span this broad spectrum of measurement and analysis advances, and we wish to thank all contributors for their willingness to publish their work in Measurement Science and Technology.…”