Characterization of luminescent mini-tufts in quantitative flow visualization experiments: Surface flow analysis and modelization

Chen, Lin; Suzuki, Tomohiro; Nonomura, Taku; Asai, Keisuke

doi:10.1016/j.expthermflusci.2019.02.002

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…Edge detection and the Hough transform can be used to fit a line through the detected tufts and deduce their orientation, as in [34], though this method becomes inaccurate for highly curved tufts. Other methods detect the tufts' shapes by recognizing one end of the tuft and progressing pixel-by-pixel along the high-intensity ridge that defines the tuft's centerline [35].…”

Section: Flow Visualization With Tuftsmentioning

confidence: 99%

Deep learning based instance segmentation of particle streaks and tufts

Tsalicoglou

Roesgen

2022

Meas. Sci. Technol.

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3D Particle Streak Velocimetry (3D-PSV) and surface flow visualization using tufts both require the detection of curve segments, particle streaks or tufts, in images. We propose the use of deep learning based instance segmentation neural networks Mask R-CNN and Cascade Mask R-CNN, trained on fully synthetic data, to accurately identify, segment, and classify streaks and tufts. For 3D-PSV, we use the segmented masks and detected streak endpoints to volumetrically reconstruct flows even when the imaged streaks partly overlap or intersect. In addition, we use Mask R-CNN to segment images of tufts and classify the detected tufts according to their range of motion, thus automating the detection of regions of separated flow while at the same time providing accurate segmentation masks. Finally, we show a successful synthetic-to-real transfer by training only on synthetic data and successfully evaluating real data. The synthetic data generation is particularly suitable for the two presented applications, as the experimental images consist of simple geometric curves or a superposition of curves. Therefore, the proposed networks provide a general framework for instance detection, keypoint detection and classification that can be fine-tuned to the specific experimental application and imaging parameters using synthetic data.

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Section: Flow Visualization With Tuftsmentioning

confidence: 99%

Deep learning based instance segmentation of particle streaks and tufts

Tsalicoglou

Roesgen

2022

Meas. Sci. Technol.

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“…As shown in Figure 1, the experimental set-up in this study is similar to that in reference [30]. Excitation UV light source and high-speed camera are placed on top of the test section, while the target model is mounted inside the visualization section.…”

Section: Experimental Set-ups Experiments and Modelmentioning

confidence: 99%

“…Bonitz et al [29] applied Vey's approach on the flow interaction characteristics analysis between the wheel, wheelhouse, and the rotation of the wheel of a passenger car. Chen et al [30] analyzed the steady flow characterization and quantitative flow conducted on a flat plate model with the luminescent mini-tufts method.…”

Section: Introductionmentioning

confidence: 99%

Digitalization and Quantitative Flow Visualization of Surrounding Flow over a Specially-Shaped Column-Frame by Luminescent Mini-Tufts Method

Chen

2022

Aerospace

Self Cite

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The luminescent mini-tufts method is widely used for flow visualization for quantitative field analysis. A set of numerical methods for digitalization of 3D surfaces surrounding flows with luminescent mini-tufts has been developed in this study. The procedure includes digital image pre-processing, mini-tufts recognition, mean field mini-tufts calculation, inclination angle calculation, oscillation area calculation, etc. The model is subjected to a newly proposed digitalization method and realized by in-house code. The time mean angle’s changing mode, along the mini-tuft, are analyzed, which shows that the mini-tuft follows the inflow well. The transient oscillation of mini-tufts is observed as well, which shows that on the middle part of the irregularity cylinder, the flow oscillates more intensively.

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“…Visualization model experiments require only short test times and provide easy observations, explanations, and understanding, and convenient methods for improving oil-displacing agents, but their further development is limited by their ability to calculate experimental results (Chen et al, 2019). Visualization evaluation methods are mostly used to observe subtle phenomena of fluids in porous media.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effect of Oil-Displacing Agents Using Computer Graphics and Visualization Glass Plate Model Experiments

Sun

Ren

et al. 2021

Front. Energy Res.

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The results of visualization experiments primarily provide descriptions of local features or a general conclusion because, to obtain accurate numerical results, it is necessary to count each small point in the visual image and calculate the remaining oil based on the color. There is currently no method that can automatically and accurately calculate the recovery factor based on a visualized image. Computer graphics can be used to solve this problem, and Matlab software with its powerful image calculation functions was used to analyze and calculate images of visualization experiment processes. This article first summarizes the development and respective characteristics of the visual experiment evaluation of oil displacement agents. Then, the corresponding relationship between the actual oil displacement recovery parameters and the image parameters is introduced in detail, and a calculation formula for the visual image recovery factor is summarized. Finally, using the quantitative visual experimental results from different oil displacement agent injection methods as an example, we compared the different oil displacement processes and their recovery. The different characteristics of the two injection methods were evaluated, and the feasibility of the calculation method was verified. This method provides data support for interpreting visual experimental processes and a description of the experimental results. This provides a clear and unified calculation method for the recovery factors of oil displacement processes, which often involve many difficult-to-compare processes.

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Characterization of luminescent mini-tufts in quantitative flow visualization experiments: Surface flow analysis and modelization

Cited by 15 publications

References 11 publications

Deep learning based instance segmentation of particle streaks and tufts

Deep learning based instance segmentation of particle streaks and tufts

Digitalization and Quantitative Flow Visualization of Surrounding Flow over a Specially-Shaped Column-Frame by Luminescent Mini-Tufts Method

Evaluating the Effect of Oil-Displacing Agents Using Computer Graphics and Visualization Glass Plate Model Experiments

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