Reconstruction of three-dimensional fluid stress field via photoelasticity using physics-informed convolutional encoder-decoder
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909593389498368 |
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| author | Igarashi, Daichi Kumagai, Shunsuke Yokoyama, Yuto Jingzu, Yee Horie, Masanobu Tagawa, Yoshiyuki |
| author_facet | Igarashi, Daichi Kumagai, Shunsuke Yokoyama, Yuto Jingzu, Yee Horie, Masanobu Tagawa, Yoshiyuki |
| contents | Measuring stress fields in fluids and soft materials is crucial in various fields such as mechanical engineering, medicine, and bioengineering. However, conventional methods that calculate stress fields from velocity fields struggle to measure complex fluids where the stress constitutive equation is unknown. To address this, we propose a novel approach that combines photoelastic measurements -- which can non-invasively visualize internal stresses -- with machine learning to measure stress fields. The machine learning model, which we named physics-informed convolutional encoder-decoder (PICED), integrates a convolutional neural network (CNN)-based encoder-decoder model with a physics-informed neural network (PINN). Using this approach, three-dimensional stress fields can be predicted with high accuracy for multiple interpolated data points in a rectangular channel flow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconstruction of three-dimensional fluid stress field via photoelasticity using physics-informed convolutional encoder-decoder Igarashi, Daichi Kumagai, Shunsuke Yokoyama, Yuto Jingzu, Yee Horie, Masanobu Tagawa, Yoshiyuki Fluid Dynamics Data Analysis, Statistics and Probability Measuring stress fields in fluids and soft materials is crucial in various fields such as mechanical engineering, medicine, and bioengineering. However, conventional methods that calculate stress fields from velocity fields struggle to measure complex fluids where the stress constitutive equation is unknown. To address this, we propose a novel approach that combines photoelastic measurements -- which can non-invasively visualize internal stresses -- with machine learning to measure stress fields. The machine learning model, which we named physics-informed convolutional encoder-decoder (PICED), integrates a convolutional neural network (CNN)-based encoder-decoder model with a physics-informed neural network (PINN). Using this approach, three-dimensional stress fields can be predicted with high accuracy for multiple interpolated data points in a rectangular channel flow. |
| title | Reconstruction of three-dimensional fluid stress field via photoelasticity using physics-informed convolutional encoder-decoder |
| topic | Fluid Dynamics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2504.15952 |