Main results of the Second International PIV Challenge

Stanislas, Michel; Okamoto, Koji; Kähler, Christian J.; Westerweel, J.

doi:10.1007/s00348-005-0951-2

Cited by 220 publications

(144 citation statements)

References 19 publications

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“…At higher Reynolds numbers, real model aircraft wing tip vortex velocities were measured by Scarano et al (2002). Typical problems encountered in PIV measurements on tip vortices were shown at the First International PIV Challenge (Stanislas et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Flow field measurement around vortex cavitation

2015

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stage. These involve the smooth transport of vapor into the tip vortex, leading harmful implosions away from the propeller surface. The tip vortex cavity dynamics are not directly related to the propeller rotation rate. The excitation of this cavity often leads to high amplitude broadband pressure fluctuations between the fourth and seventh blade rate frequency (Wijngaarden et al. 2005).To better understand the sound production from vortex cavitation, a model for the waves on a tip vortex cavity was developed and was shown to be able to describe the interface dynamics in detail (Pennings et al. 2015). As a result, a condition could be predicted where a cavity resonance frequency could be amplified to produce high amplitude sound, as reported by Maines and Arndt (1997).The part that remains to be validated is a vortex model for the cavity size and cavity angular velocity. It has been shown that a Lamb-Oseen vortex model poorly estimates the cavity size as a function of cavitation number (Pennings et al. 2015). It is possible that overestimation of the vortex peak azimuthal velocity could have led to an overestimation of the cavity size. The main goal of the present study was to configure a simple low-order vortex model, to serve as an input into a vortex cavity wave dynamics model, to describe the tip vortex resonance frequency. It is based on the following steps.1. Model the tip vortex velocity field without cavitation (further referred to as wetted vortex) 2. Model the cavity size as a function of cavitation number based on wetted vortex properties 3. Show the difference in velocity field around a wetted and cavitating vortex 4. Obtain the cavity angular velocity value that gives the best correlation to the tip vortex cavity resonance frequency Abstract Models for the center frequency of cavitatingvortex induced pressure-fluctuations, in a flow around propellers, require knowledge of the vortex strength and vapor cavity size. For this purpose, stereoscopic particle image velocimetry (PIV) measurements were taken downstream of a fixed half-wing model. A high spatial resolution is required and was obtained via correlation averaging. This reduces the interrogation area size by a factor of 2-8, with respect to conventional PIV measurements. Vortex wandering was accounted for by selecting PIV images for a given vortex position, yielding sufficient data to obtain statistically converged and accurate results, both with and without a vapor-filled vortex core. Based on these results, the loworder Proctor model was applied to describe the tip vortex velocity outside the viscous core, and the cavity size as a function of cavitation number. The flow field around the vortex cavity shows, in comparison with a flow field without cavitation, a region of retarded flow. This layer around the cavity interface is similar to the viscous core of a vortex without cavitation.

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Section: Introductionmentioning

confidence: 99%

Flow field measurement around vortex cavitation

2015

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“…Nevertheless, these methods have great potentials for the fine measurement of complex indoor airflow, such as the inlet air jet and wall turbulence, where larger velocity gradient tensor exists and greater accuracy is needed. Unfortunately, though variety kinds of algorithms available, there is not a single algorithm that has the best performance everywhere [26]. Detailed analyses of the performances of the state-of-the-art evaluation methods are available in the main results of PIV challenges [25][26][27].…”

Section: Image Evaluation Methodsmentioning

confidence: 99%

“…Presently, PIV has become a powerful tool for studying flow field in indoor environment. The measurement principle and major developments of PIV have been reviewed in many excellent papers [24][25][26][27][28] and in a comprehensive book by Raffel et al [17]. Therefore, in this section, we just introduce the PIV technologies focusing on the applications in indoor environment.…”

Section: Piv Technologies For Indoor Applicationmentioning

confidence: 99%

Particle image velocimetry measurement of indoor airflow field: A review of the technologies and applications

Cao

Liu

Jiang

et al. 2014

Energy and Buildings

135

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Quantifying the airflow field in building room or vehicle cabin indoor space is crucial for creating a thermal comfortable and healthy indoor environment. Airflow field measurement can provide quantitative information of indoor air distribution and local air velocity around occupants or passengers, which has strong relationship with the ventilation effectiveness, the pollutant transportation and the energy conservation in a building or a vehicle. However, the airflow field in indoor environment is normally characterized by high turbulent level and unsteady due to relatively low air velocity from the diffuser, thermal plume of heat source and unsteady perturbation of occupants' behavior. Generally, it is very difficult to conduct accurate and detailed measurement of such a complex turbulent flow field with point-wise anemometry.

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“…Unfortunately, this ideal scenario is not always possible as the flow region of interest can be occupied by objects producing artefacts (light reflections, shadow areas, local variations in seeding density, etc.) hindering the measurement quality (Westerweel et al 2005). If the required particle image displacement measurement is in the near vicinity of a surface, special care must be taken in adequate surface preparation during the experimental phase (Paterna et al 2013), images must be properly pre-processed by means of, e.g., background subtraction (Mendez et al 2017), or advanced image interrogation processes are to be adopted (Ronneberger et al 1998;Gui et al 2003;Usera et al 2004).…”

Section: Introductionmentioning

confidence: 99%