Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters

Masullo, Alessandro; Theunissen, Raf

doi:10.1007/s00348-015-2110-8

Cited by 19 publications

(16 citation statements)

References 33 publications

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“…A second iteration of the tracking is then performed after some regularization, which consists in the identification of inconsistent positions. This is a direct implementation of the coherency estimation algorithm of Masullo & Theunissen (2016). The original algorithm was developed for particle image velocimetry (PIV) and is efficient for such heterogeneous displacement fields.…”

Section: Digital Volume Correlationmentioning

confidence: 99%

High-temperature deformation followed in situ by X-ray microtomography: a methodology to track features under large strain

Lhuissier¹,

Bormann²,

Pelloux³

et al. 2021

J Synchrotron Radiat

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Metallic materials processing such as rolling, extrusion or forging often involves high-temperature deformation. Usually under such conditions the samples are characterized post mortem, under pseudo in situ conditions with interrupted tests, or in situ with a limited strain rate. A full in situ 3D characterization, directly during high-temperature deformation with a prescribed strain-rate scheme, requires a dedicated sample environment and a dedicated image-analysis workflow. A specific sample environment has been developed to enable highly controlled (temperature and strain rate) high-temperature deformation mechanical testing to be conducted while performing in situ tomography on a synchrotron beamline. A dedicated digital volume correlation algorithm is used to estimate the strain field and track pores while the material endures large deformations. The algorithm is particularly suitable for materials with few internal features when the deformation steps between two images are large. An example of an application is provided: a high-temperature compression test on a porous aluminium alloy with individual pore tracking with a specific strain-rate scheme representative of rolling conditions.

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Section: Digital Volume Correlationmentioning

confidence: 99%

High-temperature deformation followed in situ by X-ray microtomography: a methodology to track features under large strain

Lhuissier¹,

Bormann²,

Pelloux³

et al. 2021

J Synchrotron Radiat

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“…These methodologies proved to be effective for isolated outliers, whereas they suffer from under-detection of outliers when a cluster of false vectors is present in the data. This phenomenon is common when low-seeding areas, if cross-correlation based algorithms are employed, or strong reflections on objects are present in the raw images (Masullo and Theunissen, 2016). In order to overcome this limitation, Masullo and Theunissen (2016) presented a new method based on the combination of a first spatial coherence test and a modified Gaussianweighted distance-based averaging median.…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon is common when low-seeding areas, if cross-correlation based algorithms are employed, or strong reflections on objects are present in the raw images (Masullo and Theunissen, 2016). In order to overcome this limitation, Masullo and Theunissen (2016) presented a new method based on the combination of a first spatial coherence test and a modified Gaussianweighted distance-based averaging median. The introduction of the former parametrises the extent of the region inside which the vectors are tested.…”

Section: Introductionmentioning

confidence: 99%

Physics-based universal outlier detector for flow statistics

Saredi¹,

Sciacchitano²,

Scarano³

2021

ISPIV21

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Outlier detection for PIV velocity fields is still nowadays an active field of research. In the last decades, several image pre-processing and processing algorithms have been developed aiming at increasing the dynamic velocity range of PIV measurements and reducing the measurement uncertainty. Nevertheless, PIV velocity fields are still often characterised by the presence of outliers, which potentially hamper the correct interpretation of the flow physics and negatively affect the evaluation of the flow statistics. The outlier detection strategies presented in literature are mainly based on the statistical analysis of the velocity vector with its immediate neighbour. Most of these algorithms have been demonstrated to be effective for instantaneous flow fields, where the errors associated with the outliers are order of magnitude larger than the expected measurement uncertainty. However, these approaches are not as effective for the flow statistics, where the outliers yield errors of the same order of the measurement uncertainty. To overcome this limitation, this paper proposed an outlier detection approach based on the agreement of the flow statistics to the constitutive equations, more specifically to the turbulent kinetic energy (TKE) transport equation. The focus is posed on the ratio between the local advection terms of TKE and a robust estimation of the TKE production along the local streamline. It is demonstrated that, in presence of outliers, the proposed principle yields a clear separation between the correct and the erroneous vectors. In order to assess the performance of the proposed principle, three different test cases are considered. For all of them, the results are compared with a reference outlier detection methodology, namely the universal outlier detection method proposed by Westerweel and Scarano (2005). The proposed turbulence transport-based approach exhibits higher performance in terms of percentage of outliers correctly identified in the flow statistics.

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“…Thus, some approaches are based around this idea. Velocity gradients and thresholds form the basis for some techniques [3,7], others consider adaptive local coherency [8].…”

Section: Introductionmentioning

confidence: 99%

Spurious PIV Vector Correction Using Linear Stochastic Estimation

Butcher

Spencer

2019

Fluids

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Techniques for the experimental determination of velocity fields such as particle image velocimetry (PIV) can often be hampered by spurious vectors or sparse regions of measurement which may occur due to a number of reasons. Commonly used methods for detecting and replacing erroneous values are often based on statistical measures of the surrounding vectors and may be influenced by further poor data quality in the region. A new method is presented in this paper using Linear Stochastic Estimation for vector replacement (LSEVR) which allows for increased flexibility in situations with regions of spurious vectors. LSEVR is applied to PIV dataset to demonstrate and assess its performance relative to commonly used bilinear and bicubic interpolation methods. For replacement of a single vector, all methods performed well, with LSEVR having an average error of 11% in comparison to 14% and 18% for bilinear and bicubic interpolation respectively. A more significant difference was found in replacement of clusters of vectors which showed average vector angle errors of 10°, 9° and 6° for bilinear, bicubic and LSEVR respectively. Error in magnitude was 3% for both interpolation techniques and 1% for LSEVR showing a clear benefit to using LSEVR for conditions that require multiple clustered vectors to be replaced.

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Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters

Abstract: validation routine is applied to the PIV analysis of experimental studies focused on the near wake behind a porous disc and on a supersonic jet, illustrating the potential gains in spatial resolution and accuracy.

Cited by 19 publications

References 33 publications

High-temperature deformation followed in situ by X-ray microtomography: a methodology to track features under large strain

High-temperature deformation followed in situ by X-ray microtomography: a methodology to track features under large strain

Physics-based universal outlier detector for flow statistics

Spurious PIV Vector Correction Using Linear Stochastic Estimation

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