Automatic extraction of wall streamlines from oil-flow visualizations using a convolutional neural network
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909571022323712 |
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| author | Schulte-Sasse, Jonas Steinfurth, Ben Weiss, Julien |
| author_facet | Schulte-Sasse, Jonas Steinfurth, Ben Weiss, Julien |
| contents | Oil-flow visualizations represent a simple means to reveal time-averaged wall streamline patterns. Yet, the evaluation of such images can be a time-consuming process and is subjective to human perception. In this study, we present a fast and robust method to obtain quantitative insight based on qualitative oil-flow visualizations. Using a convolutional neural network, the local flow direction is predicted based on the oil-flow texture. This was achieved with supervised training based on an extensive dataset involving approximately one million image patches that cover variations of the flow direction, the wall shear-stress magnitude and the oil-flow mixture. For a test dataset that is distinct from the training data, the mean prediction error of the flow direction is as low as three degrees. A reliable performance is also noted when the model is applied to oil-flow visualizations from the literature, demonstrating the generalizability required for an application in diverse flow configurations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_07456 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic extraction of wall streamlines from oil-flow visualizations using a convolutional neural network Schulte-Sasse, Jonas Steinfurth, Ben Weiss, Julien Fluid Dynamics Oil-flow visualizations represent a simple means to reveal time-averaged wall streamline patterns. Yet, the evaluation of such images can be a time-consuming process and is subjective to human perception. In this study, we present a fast and robust method to obtain quantitative insight based on qualitative oil-flow visualizations. Using a convolutional neural network, the local flow direction is predicted based on the oil-flow texture. This was achieved with supervised training based on an extensive dataset involving approximately one million image patches that cover variations of the flow direction, the wall shear-stress magnitude and the oil-flow mixture. For a test dataset that is distinct from the training data, the mean prediction error of the flow direction is as low as three degrees. A reliable performance is also noted when the model is applied to oil-flow visualizations from the literature, demonstrating the generalizability required for an application in diverse flow configurations. |
| title | Automatic extraction of wall streamlines from oil-flow visualizations using a convolutional neural network |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2412.07456 |