Main results of the third international PIV Challenge

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

doi:10.1007/s00348-008-0462-z

Cited by 196 publications

(129 citation statements)

References 39 publications

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“…Furthermore, while current flashlamp-pumped Nd:YAG lasers usually deliver pulses up to 400 mJ at typical repetition rate of 10 Hz, the pulse energy of diodepumped Nd:YLF lasers operating in the kilohertz range typically does not exceed 20 mJ and drops well below 10 mJ at 10 kHz. The combination of lower image quality of CMOS cameras and weaker illumination of high repetition rate lasers are the principal causes of the reduced accuracy and precision of high-speed PIV systems, as also documented in the results of the most recent PIV challenge (Stanislas et al 2008).…”

Section: Introductionmentioning

confidence: 97%

Multi-frame pyramid correlation for time-resolved PIV

2012

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A novel technique is introduced to increase the precision and robustness of time-resolved particle image velocimetry (TR-PIV) measurements. The innovative element of the technique is the linear combination of the correlation signal computed at different separation time intervals. The domain of the correlation signal resulting from different temporal separations is matched via homothetic transformation prior to the averaging of the correlation maps. The resulting ensemble-averaged correlation function features a significantly higher signal-to-noise ratio and a more precise velocity estimation due to the evaluation of a larger particle image displacement. The method relies on a local optimization of the observation time between snapshots taking into account the local out-ofplane motion, continuum deformation due to in-plane velocity gradient and acceleration errors. The performance of the pyramid correlation algorithm is assessed on a synthetically generated image sequence reproducing a three-dimensional Batchelor vortex; experiments conducted in air and water flows are used to assess the performance on time-resolved PIV image sequences. The numerical assessment demonstrates the effectiveness of the pyramid correlation technique in reducing both random and bias errors by a factor 3 and one order of magnitude, respectively. The experimental assessment yields a significant increase of signal strength indicating enhanced measurement robustness. Moreover, the amplitude of noisy fluctuations is considerably attenuated and higher precision is obtained for the evaluation of time-resolved velocity and acceleration.

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Section: Introductionmentioning

confidence: 97%

Multi-frame pyramid correlation for time-resolved PIV

2012

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“…However, although it is possible to compute velocity vectors even in smaller scales than a micrometer grid, these vectors are not independent and bias errors can occur in particular situations. Even though many different approaches to increase the accuracy and resolution of DPIV were presented, see Adrian and Westerweel (2010), Keane et al (1995), Raffel et al (2007), Scarano (2001), Stanislas et al (2003Stanislas et al ( , 2005Stanislas et al ( , 2008, Stitou and Riethmuller (2001), Willert (1997), for instance, a detailed analysis of the resolution limit is still lacking and will be the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

On the resolution limit of digital particle image velocimetry

2012

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This work analyzes the spatial resolution that can be achieved by digital particle image velocimetry (DPIV) as a function of the tracer particles and the imaging and recording system. As the in-plane resolution for window-correlation evaluation is related by the interrogation window size, it was assumed in the past that single-pixel ensemble-correlation increases the spatial resolution up to the pixel limit. However, it is shown that the determining factor limiting the resolution of single-pixel ensemblecorrelation are the size of the particle images, which is dependent on the size of the particles, the magnification, the f-number of the imaging system, and the optical aberrations. Furthermore, since the minimum detectable particle image size is determined by the pixel size of the camera sensor in DPIV, this quantity is also considered in this analysis. It is shown that the optimal magnification that results in the best possible spatial resolution can be estimated from the particle size, the lens properties, and the pixel size of the camera. Thus, the information provided in this paper allows for the optimization of the camera and objective lens choices as well as the working distance for a given setup. Furthermore, the possibility of increasing the spatial resolution by means of particle tracking velocimetry (PTV) is discussed in detail. It is shown that this technique allows to increase the spatial resolution to the subpixel limit for averaged flow fields. In addition, PTV evaluation methods do not show bias errors that are typical for correlation-based approaches. Therefore, this technique is best suited for the estimation of velocity profiles.

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“…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

132

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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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Main results of the third international PIV Challenge

Cited by 196 publications

References 39 publications

Multi-frame pyramid correlation for time-resolved PIV

Multi-frame pyramid correlation for time-resolved PIV

On the resolution limit of digital particle image velocimetry

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

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