Deep learning-based prediction of the remaining time and future distribution of pebble flow from real-scene images

Pebble flow dynamics is a crucial issue for designing and operating pebble bed reactors. The existing experimental or simulation methods are often associated with high time, resource, and effort costs. Therefore, image-based deep learning methods are explored to make real-time predictions of pebble flow dynamics directly from images. Real-scene images, captured by the high-speed camera during pebble flow experiments are used as the dataset. This paper proposes an RT-Net model based on Convolutional Neural Network (CNN) to predict the remaining time of pebble flow from experimental images. The core of the RT-Net model is the mergeable multi-branch convolutional component called ConvBlock, which effectively improves accuracy and reduces computational costs compared to traditional convolutional operators. Results show that this model is superior in both accuracy and efficiency metrics compared with typical convolutional neural networks (like AlexNet and VGGs). The proportion of test sets with prediction error within 0.05 s reaches 96.7 %, and the parameters count and inference time are 5.02 M and 1.99 ms respectively. Furthermore, to anticipate the pebble distribution at a given future time, a dual-input PreNet that combines CNN and Generative Adversarial Network (GAN) is designed, where the input includes the current pebble flow image and an arbitrarily chosen temporal displacement Δt. Targeted evaluation metrics, such as Target Similarity (TS) and Equivalent Target Similarity (TSeq) are proposed to quantitatively evaluate the model's performance. Results indicate that the Pre-Net model can make quite satisfactory predictions of the future distribution of most data, while more efforts such as a task-specific loss function are encouraged to achieve better performance.