Physics-informed shadowgraph network: an end-to-end self-supervised density field reconstruction method

This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks. The proposed method utilizes the shadowgraph technique visualizing the flow field, enabling reliable quantitative measurement of flow density fields. Compared to traditional methods, which obtain the distribution of physical quality in spatial coordinates case by case, our approach establishes an end-to-end neural network that directly maps shadowgraph images to physical fields. Besides, the model employs a self-supervised learning approach without any labeled data. Experimental validations across hot air jets, thermal plumes, and alcohol burner flames prove the model’s accuracy and universality. This approach offers a non-invasive, real-time surrogate model for flow diagnostics. It is believed that this technique could cover and become a reliable tool in various scientific and engineering disciplines.