An image processing algorithm for the measurement of multiphase bubbly flow using predictor-corrector method

Zhou, Haojie; Niu, Xiaojing

doi:10.1016/j.ijmultiphaseflow.2020.103277

Cited by 23 publications

(23 citation statements)

References 26 publications

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“…30 Another example is to use a more accurate segmenting and reconstruction algorithm for overlapping particles in a cluster. 16 Second, through the prejudgment of measured particle shape, determine the most suitable particle shape-based reconstruction models. In the last respect, for very irregular particles, the 3D image techniques based on binocular 31 or multiple visions 32 need to be developed.…”

Section: Discussionmentioning

confidence: 99%

“…No matter how the particles in the cluster are segmented and then reconstructed, errors or missed identifications are always unavoidable. 15,16 From a scientific perspective, there is still room for improving accuracy. However, it is accurate enough in its current state from an engineering perspective.…”

Section: Irregular Particlesmentioning

confidence: 99%

“…However, as a side effect, the object size will be decreased or increased . At the same time, even if the most reasonable object recognition approach is adopted to identify overlapping particles, erroneous or omitted identifications are unavoidable. , Another uncertainty in image analysis comes from geometric structure reconstruction of irregular particles, which are closely related to subsequent evaluation of particle size and volume . A test experiment by using two different photo-optical probes and with either manual or automatic image analysis offered different particle size distributions though they were similar in the overall trend …”

Section: Introductionmentioning

confidence: 99%

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Evaluation on the Accuracy of an Inline Image-Based Method for Regular and Irregular Particles

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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Although the image-based method is considered to be the most direct and accurate measurement method compared to some other methods, there are some factors that add uncertainties to the measurements. In this study, a newly developed inline image-based method consisting of a telecentric vision probe and automatic particle identification software is evaluated by comparing with other recognized measuring technologies for regular and irregular particles. The subject technique provides pretty good repeatability for both regular (σ D 50 = 0.65%) and irregular particles (σ D 50 = 1.26%). From an engineering perspective, the measurement results of average diameters reveal the reliability of this image-based method. The maximum relative deviations are respectively 6.56% and 3.90% compared to the laser diffraction. Future work should be devoted to further improvement of the accuracy of this image-based method including contour identification, the particle shape-based reconstruction models, and the 3D image techniques based on binocular or multiple vision probes.

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Section: Discussionmentioning

confidence: 99%

Section: Irregular Particlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation on the Accuracy of an Inline Image-Based Method for Regular and Irregular Particles

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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“…The various imaging techniques that can be used to characterize 2D images of bubbly flows (Paolinelli et al (2018); Zhou et al (2020); Torisaki et al (2020); Haas et al (2020); Cerqueira et al (2018)) can be classified in two main categories. Deterministic methods mainly rely on segmentation, pattern recognition and geometrical methods to characterize the images (Cerqueira et al (2021); Vinnett et al (2020);).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Parameters of a Stochastic Geometrical Model for Multiphase Flow Images Using Local Measures

et al. 2021

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This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability densityof local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

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“…Many CNN-based deep learning frameworks are effective because CNNs emulate the human brain's natural visual perception mechanism by systematically learning features through multiple operational layers 45 . Image-based deep learning models can play a vital role in fully understanding boiling physics because boiling images are richly embedded with bubble statistics, which are quantitative measurements of the dynamic boiling phenomena [46][47][48] . Despite the potential for understanding image-based boiling physics via deep learning frameworks, very few attempts have been made to build them.…”

mentioning

confidence: 99%

Deep learning predicts boiling heat transfer

Suh

Bostanabad

Won

2021

Sci Rep

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Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.

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An image processing algorithm for the measurement of multiphase bubbly flow using predictor-corrector method

Cited by 23 publications

References 26 publications

Evaluation on the Accuracy of an Inline Image-Based Method for Regular and Irregular Particles

Evaluation on the Accuracy of an Inline Image-Based Method for Regular and Irregular Particles

Estimating the Parameters of a Stochastic Geometrical Model for Multiphase Flow Images Using Local Measures

Deep learning predicts boiling heat transfer

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