Cross-correlation analysis for temperature measurement

Hohreiter, V.; Wereley, Steven T.; Olsen, Michael G.; Chung, J. N.

doi:10.1088/0957-0233/13/7/314

Cited by 66 publications

(41 citation statements)

References 11 publications

(13 reference statements)

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“…This can be treated as isotropic stresses to determine the temperature within the flow in a non-intrusive way. This was demonstrated by Hohreiter et al (2002) and Chamarthy et al (2009) for correlation peaks computed from interrogation windows with standard PIV evaluation. First, the influence of the particle image size on the accuracy of the determination of the correlation peak's size and position was investigated.…”

Section: Isotropic Stressesmentioning

confidence: 88%

Reynolds stress estimation up to single-pixel resolution using PIV-measurements

2011

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This article derives a theory for estimating Reynolds normal and shear stresses from PIV images with single-pixel resolution. The main idea is the analysis of the correlation function to identify the probability density function from which the Reynolds stresses can be derived in a 2-D regime. The work establishes a theoretical framework including the influence of the particle image diameter and the velocity gradients on the shape of the correlation function. Synthetic data sets are used for the validation of the proposed method. The application of the evaluation method on two experimental data sets shows that high resolution and accuracy are also obtained with experimental data. The approach is very general and can also be applied to correlation peaks that are obtained from sum-of-correlation PIV evaluations.

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Section: Isotropic Stressesmentioning

confidence: 88%

Reynolds stress estimation up to single-pixel resolution using PIV-measurements

2011

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“…Already in 1986, it was realized that turbulent fluctuations will lead to a broadening of the correlation peak in speckle velocimetry, so that the turbulence level can be determined from its width (Arnold et al 1986). As mentioned before, fluctuations due to Brownian motion will also broaden the peak, making it possible to deduce the local temperature from a cross-correlation analysis (Hohreiter et al 2002). Recently, it has been proposed to determine the Reynolds stresses from ensemble-averaged single-pixel PIV by considering the shape of the resultant correlation function shape (Scharnowski et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Generalized displacement estimation for averages of non-stationary flows

Poelma

Westerweel

2010

Exp Fluids

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When dealing with particle image velocimetry data sets with a relatively poor signal-to-noise ratio, averaged velocity fields are often the only achievable result. These average fields can be determined in a number of ways, of which correlation averaging has become the most prominent. We show that for instationary flows, the use of correlation averaging can lead to unreliable results: summation of individual correlation peaks from a transient flow creates a broadened peak. The location of the maximum of this peak generally does not coincide with the true temporal mean displacement. We propose to use the centroid of the correlation result as a better estimator. This method is demonstrated with simulated and experimental data, showing that it gives more reliable results, at the price of a small increase in noise level. For relatively small displacements, where the conventional method is not biased, the method is less suitable due to this increase in noise. Therefore, a straightforward hybrid method optimizes the displacement estimation for optimal results.

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“…Figure 3 shows a typical image of the 500 nm diameter micro-particle seeded flow. During the last 10 years, the μPIV technique has been used in many research fields, for example, to study electroosmotic, pressure driven and mixed flow in a twin-T microchannel intersection [34], thermal measurement dependent Brownian motion of microparticles [35], investigation of fluid slip mechanisms at wall [36] and in hydrophobic microchannels, internal circulation of aqueous plugs in two phases flow [37], in measurements of beads suspensions in water and human blood, using the red blood cells as a natural particle seeding [38]. A comprehensive review on the μPIV technique can be found in [39] and [40].…”

Section: Micro-particle Image Velocimetrymentioning

confidence: 99%

Recent advances in digital particle image velocimetry methods for flow motion analysis

Nasibov

Baytaroglu

2010

Int. J. Metrol. Qual. Eng.

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Abstract. The advent of charge-coupled devices (CCD) has significantly extended the horizons of full-field measurement methods in many fields of fundamental and applied sciences, including fluid flow dynamics. One of the major applications of imaging sensors in fluid mechanics is the Digital Particle Image Velocimetry (DPIV), which is a non-invasive, full field optical measuring technique. Due to recent advances in image processing methods, and in digital imaging, laser and optics DPIV has become a dominant tool for obtaining velocity information about fluid motion. On the other hand, over the last years, developments in micro-and nanofluidic systems have promised a shrinking of the desktop-sized chemical and biological devices to microscale size, so a detailed investigation of the behaviour of flow inside these microdevices is essential for the optimum design of microsystems, as well as for an understanding of flow dynamics in micron scales. In this work, the advances in visualization and quantitative measurements of flow velocity measurements are reviewed and explained.

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Cross-correlation analysis for temperature measurement

Cited by 66 publications

References 11 publications

Reynolds stress estimation up to single-pixel resolution using PIV-measurements

Reynolds stress estimation up to single-pixel resolution using PIV-measurements

Generalized displacement estimation for averages of non-stationary flows

Recent advances in digital particle image velocimetry methods for flow motion analysis

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