Key Takeaways & Executive Findings
- •• An autoencoder-based deep learning framework reconstructs 3D solid–liquid two-phase flow in flexible vibrating pipelines using sparse wall sensor data. • The X-model and F-model achieve high reconstruction accuracy with R2 values of 0.990 and 0.945, respectively. • Optimal sensor configuration: 20 sensors (0.06% of total grids) provide a balance between accuracy and cost, with full-length arrangement outperforming front-end dense placement. • The models demonstrate robustness across vibration parameters, physical fields, and vibration modes, with a signal-to-noise ratio tolerance of approximately 27 dB.
Abstract
Environmental factors induce vibrations in flexible pipelines, thereby affecting the internal flow characteristics. Therefore, real-time monitoring of solid–liquid two-phase flow in pipelines is crucial for system maintenance. This study develops an autoencoder-based deep learning framework to reconstruct three-dimensional solid–liquid two-phase flow within flexible vibrating pipelines utilizing sparse wall information from sensors. Within this framework, separate X-model and F-model with distinct hidden-layer structures are established to reconstruct the coordinates and flow field information on the computational domain grid of the pipeline under traveling wave vibration. Following hyperparameter optimization, the models achieved high reconstruction accuracy, demonstrating R2 values of 0.990 and 0.945, respectively. The models’ robustness is evaluated across three aspects: vibration parameters, physical fields, and vibration modes, demonstrating good reconstruction performance. Results concerning sensors show that 20 sensors (0.06% of total grids) achieve a balance between accuracy and cost, with superior accuracy obtained when arranged along the full length of the pipe compared to a dense arrangement at the front end. The models exhibited a signal-to-noise ratio tolerance of approximately 27 dB, with reconstruction accuracy being more affected by sensor failures at both ends of the pipeline. Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.
1. Introduction
Pipeline solid–liquid two-phase flow refers to a flow system within a pipeline that uses liquid as the continuous phase medium to transport solid particles. It plays an important role in modern industrial fields such as deep-sea mining, chemical processing, food engineering, and bioengineering. In the field of deep-sea mining, on the basis of the widespread application of rigid pipelines, as engineering requirements become increasingly complex, flexible pipelines have shown unique application value due to their ease of erection and adaptability to complex environments and system displacements. However, disturbances such as waves, currents, and mining equipment in the marine environment usually induce flexible pipes to vibrate, which not only affects the structural state of the pipeline itself, but also significantly changes the transport characteristics of the flow in the pipeline. Therefore, obtaining the real-time global flow state of two-phase flow in flexible vibrating pipelines has important reference significance for the maintenance and optimization of the transportation system.
It is well-established in ocean engineering practice and theory that for long flexible risers, the large-amplitude, low-frequency vibrations are overwhelmingly dominated by external environmental loads rather than the internal flow itself, particularly the vortex-induced vibration (VIV) caused by ocean currents. This is reflected in industry design standards, such as DNVGL-RP-F 105, where VIV analysis is a mandatory and critical step for ensuring the structural integrity and fatigue life of the riser. Corresponding studies further reinforce this point, identifying VIV as a key physical mechanism driving the dynamic response of such structures.
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Shengpeng Xiao, Chuyi Wan, Hongbo Zhu, Dai Zhou, Juxi Hu, Mengmeng Zhang, Yuankun Sun, Yan Bao, Ke Zhao (2025). Sparse pipeline wall information-based data-driven reconstruction for solid–liquid two-phase flow in flexible vibrating pipelines. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.07.011
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Frequently Asked Questions
What is the main objective of this research?
The main objective is to develop a data-driven framework using autoencoder-based deep learning to reconstruct three-dimensional solid–liquid two-phase flow in flexible vibrating pipelines from sparse wall sensor information.
How many sensors are needed for accurate reconstruction?
The study found that 20 sensors, which represent 0.06% of the total grid points, achieve a balance between accuracy and cost, with better accuracy when sensors are distributed along the full length of the pipe.
What are the key performance metrics of the proposed models?
The X-model and F-model achieved R2 values of 0.990 and 0.945, respectively, indicating high reconstruction accuracy.
How robust are the models to different conditions?
The models were evaluated across vibration parameters, physical fields, and vibration modes, demonstrating good reconstruction performance. They also tolerate a signal-to-noise ratio of approximately 27 dB.
What is the practical application of this research?
This research is particularly relevant for deep-sea mineral resource transportation, where real-time monitoring of two-phase flow in flexible pipelines is crucial for system maintenance and optimization.
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