Cross-scale prediction of aluminum dust concentration based on Image Fusion Physics-Informed Neural Networks

Research on predicting dust concentration can effectively help reduce the occurrence of dust explosion accidents. However, existing methods struggle to accurately and quickly reconstruct and predict concentration fields across scales in turbulent dust diffusion processes. This paper proposes a neural network framework that combines particle physics information with image inverse mapping to achieve rapid, multi-source, and cross-scale prediction of turbulent dust concentration fields. We conducted a 280 kg aluminum dust dispersion experiment, collecting ultrasound attenuation signals and image data for our dataset. Based on this, by incorporating the Maxwell-Stefan equation, our approach addresses the underfitting issues of existing neural networks in predicting microscale turbulence within concentration fields. Additionally, the inverse mapping of images provides macroscopic diffusion trends for the concentration field. Results demonstrate that our method reconstructs aluminum dust concentration and predicts future 0.06 s states in 0.011 s, with a mean squared error of only 0.0003. Compared to existing Convolutional Neural Networks, Physics-Informed Neural Networks, and Computational Fluid Dynamics methods, our approach shows significant improvement in cross-scale prediction, making accurate concentration prediction possible. This advancement offers quantitative prediction data crucial for preventing dust explosions.