Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(2)

Published: Feb. 1, 2025

Pipeline hydraulic transportation is the primary method for transporting deep-sea mineral resources and fossil fuels. blockage often causes excessive pressure in pipeline, leading to pipeline breakage or even cargo leakage, which severely impacts safety can easily trigger secondary disasters. Therefore, clarifying global flow field within pipelines, such as particle distribution, crucial monitoring controlling systems. This study uses a limited number of measurable wall sensor values inputs deep learning models reconstruction, with solid–liquid two-phase three-dimensional output. Three model frameworks from existing studies are summarized, their reconstruction effects compared. Based on this, new framework proposed. It expands low-dimensional same size using pseudo-decoder then processes them through an autoencoder. The results indicate that achieves further accuracy improvements compared previous three frameworks, R2 mean squared error reaching 0.933 5.13 ×10−4, respectively. Additionally, skip connection configuration model, dataset size, rate, well arrangement sensors accuracy, investigated. Finally, transferability demonstrated by reconstructing fluid velocity fields flow.

Language: Английский