ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912805113823232 |
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| author | Wilms, Christoph Xu, Wenchao Ozler, Gizem Jantač, Simon Schmelter, Sonja Grosshans, Holger |
| author_facet | Wilms, Christoph Xu, Wenchao Ozler, Gizem Jantač, Simon Schmelter, Sonja Grosshans, Holger |
| contents | The electrostatic charge acquired by powders during transport through ducts can cause devastating dust explosions. Our recently developed laser-optical measurement technique can resolve the powder charge along a one-dimensional (1D) path. However, the charge across the duct's complete two-dimensional (2D) cross-section, which is the critical parameter for process safety, is generally unavailable due to limited optical access. To estimate the complete powder charge distribution in a conveying duct, we propose a machine learning (ML) approach using a shallow neural network (SNN). The ML algorithm is trained with cross-sectional data extracted from four different three-dimensional direct numerical simulations of a turbulent duct flow with varying particle size. Through this training with simulation data, the ML algorithm can estimate the powder charge distribution in the duct's cross-section based on only 1D measurements. The results reveal an average $L^1$-error of the reconstructed 2D cross-section of 1.63 %. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02852 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct Wilms, Christoph Xu, Wenchao Ozler, Gizem Jantač, Simon Schmelter, Sonja Grosshans, Holger Fluid Dynamics The electrostatic charge acquired by powders during transport through ducts can cause devastating dust explosions. Our recently developed laser-optical measurement technique can resolve the powder charge along a one-dimensional (1D) path. However, the charge across the duct's complete two-dimensional (2D) cross-section, which is the critical parameter for process safety, is generally unavailable due to limited optical access. To estimate the complete powder charge distribution in a conveying duct, we propose a machine learning (ML) approach using a shallow neural network (SNN). The ML algorithm is trained with cross-sectional data extracted from four different three-dimensional direct numerical simulations of a turbulent duct flow with varying particle size. Through this training with simulation data, the ML algorithm can estimate the powder charge distribution in the duct's cross-section based on only 1D measurements. The results reveal an average $L^1$-error of the reconstructed 2D cross-section of 1.63 %. |
| title | ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2601.02852 |